Atomic Spectrometry Update – a review of advances in environmental analysis
Jeffrey R.
Bacon
*a,
Owen T.
Butler
b,
Warren R. L.
Cairns
c,
Jennifer M.
Cook
d,
Regina
Mertz-Kraus
e and
Julian. F.
Tyson
f
a59 Arnhall Drive, Westhill, Aberdeenshire, AB32 6TZ, UK. E-mail: bacon-j2@sky.com
bHealth and Safety Executive, Harpur Hill, Buxton, SK17 9JN, UK
cCNR-IDPA, Universita Ca’ Foscari, 30170 Venezia, Italy
dBritish Geological Survey, Keyworth, Nottingham, NG12 5GG, UK
eInstitut für Geowissenschaften, Johannes Gutenberg-Universität, Becher-Weg 21, 55099 Mainz, Germany
fDepartment of Chemistry, University of Massachusetts, Amherst, MA 01003, USA
First published on 2nd November 2018
Abstract
This is the 34th annual review of the application of atomic spectrometry to the chemical analysis of environmental samples. This Update refers to papers published approximately between August 2017 and June 2018 and continues the series of Atomic Spectrometry Updates (ASUs) in Environmental Analysis1 that should be read in conjunction with other related ASUs in the series, namely: clinical and biological materials, foods and beverages;2 advances in atomic spectrometry and related techniques;3 elemental speciation;4 X-ray spectrometry;5 and metals, chemicals and functional materials.6 The review is not intended to be a comprehensive overview but selective with the aim of providing a critical insight into developments in instrumentation, methodologies and data handling that represent a significant advance in the use of atomic spectrometry in the environmental sciences.
1. Introduction
This review is intended to provide a critical insight into the most significant advances of atomic spectrometry as applied to the environmental sciences. Close to the beginning of each paragraph is an italicised word or phrase that highlights the subject of that particular paragraph. Regular readers will note that Owen Butler has stepped down as co-ordinator of this review after many years of diligently getting the review to press on schedule. He continues to write the section on the analysis of air and so we shall still benefit from his knowledge and experience.In the field of air analysis, highlights within this review period included the fabrication of a new portable cascade impactor sampler that is also wearable and a portable PM2.5 sampler that could be deployed in large numbers around wildfires. Coupling air sampling units to ICP-MS is of interest to those wishing to determine elemental species in near-real time. In a laboratory setting, a new air sampler that incorporated an XRF detector for time-resolved measurements performed well when challenged with test aerosols. Data generated during subsequent field trials agreed with those generated by conventional filter sampling and ICP-MS analysis. Improvements in the capabilities of aerosol mass spectrometers were noted as were developments in other complementary techniques such as combustion analysis and spectrophotometry for the measurement of diesel fume, silica and oxides of nitrogen.
In the field of water analysis, progress was made in multielemental and species-specific concentration methods, along with methods for the preconcentration of NPs and the elimination of the background contribution from dissolved elements before single particle counting. Researchers are starting to take advantage of the flexibility of ICP-MS/MS instrumentation to determine elements such as F previously undetectable or to replace radiochemical methods for radioisotope determinations. In addition, LA-ICP-MS is now being used to investigate concentrations of elements in ice cores at greater depth resolution than previously possible, a trend that will continue into the future once ice cores with older ice become available.
For the analysis of soils, there was no noticeable diminution in the research into sample preparation procedures in which analytes are extracted from solid matrices into mineral acids for subsequent determination by an atomic spectrometry technique. Nor was there any reduction in efforts to increase the concentration of analytes in such digests so that methods involving spectrometries such as FAAS could achieve meaningful LODs. The possibilities of SPE in which micro- or nano-particulate material was separated magnetically seemed particularly attractive, as this combined the advantages of high-efficiency phase separation with the high extraction efficiency of a dispersed extraction procedure. In addition, there was some interest in alternate procedures such as alkaline extraction and fusion. Reports on the applications of LIBS for direct solids analysis continued to grow in number though it is clear that calibration, and hence accuracy, remained a major challenge. Although X-ray spectrometry continued to be used widely, the relative maturity of some techniques was reflected in the fact that many reports concerned pure application and did not present advances in the measurement science. Interest in the applications of pXRFS was sustained, in particular for rapid screening purposes. Advances in MS were mainly focused on the need to remove or to correct for interferences as well as on isotope analyses.
Significant progress has been made in the development of appropriate matrix-matched RMs suitable for calibration and quality control in the elemental and isotopic analysis of geological materials. Another area of great activity is the application of in situ techniques, such as LA-ICP-MS, SIMS and μEDXRFS, to obtain elemental and isotopic information for geological materials at high spatial resolution. A notable number of contributions described analytical developments related to the dating of different minerals, especially the use of in situ techniques.
2. Air analysis
2.1 Review papers
A review (72 references) summarised7 sampling and measuring techniques available for assessing occupational exposure to ultrafine particles. Krause et al.8 (39 references) evaluated the utility of various analytical techniques such as sp-ICP-MS, TEM, SAXS, XRD, ion-beam techniques and Raman microscopy for the characterisation of Al, Al2O3 and TiO2 NPs. Trends in the use of various analytical techniques for the characterisation of airborne particles were studied9 (252 references) and a useful guide to the suitability of any given technique presented. Metcalf et al.10 reviewed (191 references) the potential use in aerosol measurements of microfluidics, the science of handling small liquid volumes. Techniques designed to capture small sample masses from air samples, such as spot sampling and particle-into-solution sampling, were most amenable to subsequent measurements using microfluidics.2.2 Sampling techniques
Developments in particle sampling included a low-flow rate cascade impactor;11 a low-cost PM2.5 sampler12 and a mobile high-flow rate PM4 sampler.13 The portable cascade impactor ran at a nominal 2 L min−1 flow rate and employed 10 impaction stages to segregate particles in the size range from 60 nm to 9.6 μm. By removing the two lowest impaction plates, the sampler became wearable thereby making personal sampling of particles down to 170 nm size possible. A prototype compact battery-powered PM2.5 filter sampler, designed for rapid deployment around wildfire incidents, enabled the generation of time-averaged smoke concentration data comparable in quality to those derived from larger US EPA reference air samplers. The low build cost, estimated to be ∼1/20th of that of a reference sampler, made this device potentially suitable for large-scale deployment around bush-fires and in other campaigns where saturation monitoring may be required. Future work will aim at refining the ability to measure particle concentrations in real-time using an embedded low-cost optical sensor. Little is known about the levels of respirable (PM4) crystalline silica in ambient air. A new mobile PM4 sampler, operating at 50 L min−1, used13 porous foams to achieve the desired particle size cut. The battery power was sufficient to collect 30 m3 air samples so allowing RCS to be quantified at levels below 1 μg m−3.Passive air samplers can be deployed in large numbers and in locations where the logistics and cost of operating pumped (powered) samplers become prohibitative. Canadian researchers developed14 a new Hg0 passive sampler for ambient air studies. Laboratory chamber studies were used to determine the nominal sampling uptake rate of 0.121 m3 per day and to investigate the effect of temperature and wind speed on this uptake rate. In order to neutralise any corrosive effects on TD-AAS components caused by SO2 liberated from the S-impregnated activated-carbon sorbent used, 0.15 g of Na2CO3 was added to each sample prior to combustion. A week-long field deployment was sufficient for ambient concentrations of 1–2 ng m−3 to be quantified. However, the development of passive samplers for the collection of particles is more challenging. When the University of North Carolina passive air sampler was deployed15 in a working mine, the airborne mass concentrations measured were only 24–35% (PM2.5) and 39–58% (PM10) of those measured at the same time by pumped sampling onto filters.
Innovations in air sampling included16 a radio-controlled quadcopter, equipped with a Au-coated quartz tube for sampling Hg emissions. The authors concluded that this new sampling platform could play a useful role in assessing emissions above remote landscapes or above point sources which are currently inadequately characterised. From the realms of science fiction, an EDB was coupled17 to a mass spectrometer so that individual particles could be levitated in an electric field for predetermined time intervals prior to their transfer into the MS system. This novel EDB-MS system offered potential for future studies of atmospheric chemical reactions in laboratory chambers.
2.3 Reference materials and calibrants
A candidate reference material of a PM2.5-like dust was prepared18 by spiking a particle suspension with an aqueous solution containing Ca2+, K+, Mg2+, Na+ and NH4+ ions. Excess water was siphoned off, the solid residue subjected to shock freezing in liquid N2 and any residual moisture removed by freeze drying. The resultant material resembled PM2.5 dust physically and had similar leaching characteristics when analysed for ions. The authors concluded that this approach may be amenable for preparing other dusts containing, for example, metals or PAHs. A new aerosol generation system for the simultaneous production of up to 96 replicate air filter samples used19 a flame atomiser to convert metal-containing liquid standards (Co, Cu, Ni, Pb and Zn) into metal oxide NPs. Upon dilution in air, the NPs were introduced into a sampling chamber containing 96 individual air samplers. Filters, prepared with elemental loadings of 5–100 μg per filter with a batch precision of <4%, were used in a laboratory proficiency testing programme. The production of certified reference filters is now planned. A sensitive scanning XRF system with a beam resolution of ∼0.5 mm enabled20 the spatial distribution of various elements across the surface of air filters to be examined. Application to commercially available Micromatter filter standards, widely used by XRFS laboratories that undertake air filter analysis, demonstrated that the distributions of elements were uniform to within 2%.2.4 Sample preparation
A review (141 references) on new approaches in microwave-assisted digestions included21 the following topics: digestions involving dilute acids; reactants such as O2; UV-mediated reactions; high-pressure FIA digestions and microwave-initiated combustion procedures. The development of a new microwave method, using HNO3 heated to 240 °C, for the dissolution of workplace air filter samples was reported22,23 in German with English abstracts. The first paper discussed the initial method development whilst the second paper described a collaborative trial involving a number of German laboratories to generate method precision data. For elements such as Cr, Fe, Mn and Ni, data between laboratories agreed well when the supplied reference dust samples were analysed. More variability in results was encountered in samples containing elements such as Sb and W where dissolution in only HNO3 is not appropriate. A third paper detailing the findings of a second collaborative trial involving international participants is planned.Although the measurement of the bioaccessible metal fraction of workplace aerosols is of interest from a health perspective, the lack of standardised methods and associated performance data has hampered progress until now. An interlaboratory study assessed24 the performance of a procedure described in ISO 15202-2 which advocated leaching air filter samples into water at 37 ± 2 °C for 60 minutes. Test welding fumes containing Cr, Fe, Mn, Mo and Ni were sent to 10 laboratories for analysis. The repeatability (within-laboratory precision on replicate filters) was satisfactory at 11–21% but reproducibility (between laboratory precision) was noticeably poorer at 19–53%. Further studies to understand the reason for such variations would be welcomed. The first use of DGT for assessing the biocharacteristics of airborne particulates involved25 preparing polyacrylamide hydrogels embedded with Chelex 100 ion-exchange resin and immersing them in simulant dust suspensions for various time intervals. Upon removal, the probes were digested in a HNO3–HCl (4 + 1) acid mixture using MAD and analysed by ICP-MS to determine the labile metal content released from these dusts. Simulants were prepared by suspending in water dust samples recovered from inside an air conditioning unit. The total metal contents were determined using a MAD that required HF to digest the siliceous matrix. Further development of this procedure will involve applying DGT to real air filter samples. In a continuation of work reviewed1 last year, researchers in Scotland proposed26 the use of a novel two-step sequential-extraction procedure to assess the bioaccessibility of As, Cd, Cr, Cu, Fe, Mn, Pb and Zn in urban air particulate matter. A new artificial mucus fluid extractant was applied to samples followed by either the SBET or the stomach phase of the UBMG so enabling metal bioaccessibility fractions in both the upper airways and the stomach to be determined.
2.5 Instrumental analysis
Atmospheric Hg measurements are commonly carried out using the Tekran™ AFS instrument. An alternative software protocol for processing the raw atomic fluorescence signal output gave28 an improvement in LOD of 31–88%.
Researchers at NIOSH are focused on developing new and interesting portable analytical techniques for the determination of elemental species in workplace aerosols. Recent work was summarised in the following three papers. In a procedure involving a prototype rf-GD-AES instrument, aerosols were preconcentrated29 on a microelectrode tip. The achievable LOD was in the range 0.05 to 1 ng with a reproducibility of 5–28% when tested with 100 nm-sized particles containing C, Cd, Mn and Na. Zheng et al.30 recorded emission intensities from the C I and C II atomic lines in their investigation of the spatial and temporal dynamics of a pulsed, electrical spark microplasma using black carbon particles. The results provided insights into the dynamics of the plasma and will be useful in future optimisation of this technique for elemental analysis of aerosols. A new multivariate PLS calibration approach for use with SES has been described.31 In the analysis of a reference welding fume aerosol, the RMSEP obtained from the PLS model was 13% for Cr, 23% for Fe, 22.5% for Mn and 12% for Ni. In the LIBS analysis of filter samples for assessment of worker exposure to airborne CNTs, the Al and Fe impurities in the CNTs were measured32 as surrogate markers because of the ubiquitous presence of C. Using a personal air sampler running at 1 L min−1 for 8 h, it was possible to achieve an elemental LOD of ∼1 ng cm−2 on the filter. This was equivalent to an airborne LOD of ∼8 ng m−3 and provided a CNT LOD in the workplace (∼0.4 μg m−3) below the NIOSH-recommended CNT exposure limit of 1 μg m−3.
2.5.2.1 Inductively coupled plasma mass spectrometry. The SMPS provides size distributions and concentrations of NPs in air with a temporal resolution of a few minutes or less. There is ongoing interest in the benefits of coupling the SMPS to ICP-MS systems for the generation of real-time elemental data so Swiss researchers provided33 a practical guide, including video clips, to setting up such a system and its operation. Whereas radiochemical monitoring methods are slow and time consuming for the determination of radioactive 90Sr released during nuclear power plants accidents, a ICP-MS/MS procedure for the determination of 90Sr in airborne particles had34 a run-time of only 10 minutes. Air was sampled through a PM2.5 impactor to generate an aerosol containing only small particles. The aerosol was introduced into a gas-exchange device in which the air matrix was replaced by Ar. Isobaric interferences arising from 89YH+, 90Y and 90Zr were eliminated through optimisation of the cell chemistry. The LOD of 3.6 × 10−4 ng m−3 was equivalent to 1.8 × 10−6 Bq cm−3. It was acknowledged that the incomplete vaporisation or ionisation of larger particles within the plasma could be responsible for poor Sr recoveries, which were only 30–76% of those obtained using a more conventional filter sampling method. However, even with this bias, the authors concluded that this new approach merited consideration as a useful early warning tool. The online measurement of ambient aerosols by ICP-MS involved35 sampling air through a cyclone at 30 L min−1 and depositing the resultant <250 nm sized particles into an impaction well which was continuously flushed with water. Nitric acid was then added downstream and the resultant suspension passed through a quartz photo-reactor for digestion. Further work was required to minimise system blanks and so take advantage of the inherent ICP-MS sensitivity.
Other notable ICP-MS applications have been described. In a procedure involving the tandem use of LA and LIBS for the characterisation of U particles in nuclear materials, simulants were prepared36 by depositing reference U-containing particles on forensic double-sided tape. Rapid elemental profiling by LIBS enabled a 5 × 5 mm target area to be rastered in <50 minutes. When U-rich particles had been located, LA-ICP-MS was used for IRA. The determined 235U/238U ratios were within 10% of theoretical values for particles containing U at 0.01% (m/m). Suggested refinements included the use of MC-ICP-MS for more accurate and precise IRA. Determination of the 87Sr/86Sr isotopic ratio in particles trapped in snow can provide useful information on the source of atmospheric pollution in the Polar Regions. Accurate and precise IRA is however compromised because of the low analyte (particle) concentrations and the small quantity of snow available for analysis. A HEN MC-ICP-MS method made it possible to determine37 the isotopic composition of sub-ng amounts of Sr in Antarctic snow. The method sensitivity was boosted through use of a HEN with membrane desolvation, a high ion transmission cone interface and 1012 Ω amplifiers instead of the standard 1011 Ω amplifiers. The 87Sr/86Sr ratio repeatability was <0.5% RSD for a test sample containing only 160 pg Sr. Procedural blanks were <3 pg. Pollution sources could be differentiated using only 30 g quantities of snow containing as little as 0.5–2 ng Sr. A HPLC-ICP-MS procedure for the simultaneous speciation of arsenate, chromate, molybdate and vanadate species in ash, cement and flue dust samples was considered38 to be reliable, accurate (spike recoveries in the range 98–102%), precise (repeatability < 3%) and fast (chromatographic separations in 10 minutes).
2.5.2.2 Other mass spectrometry techniques. Secondary ion mass spectrometry is a sensitive surface technique which has the capability for both spatial imaging and depth profiling. A review (143 references) highlighted39 the advantages and limitations of its use in particle analysis, surveyed recent applications and suggested possible future developments.
Single particle aerosol mass spectrometry is an emerging technique for identifying the chemical composition, sizing and mixing state of individual aerosol particles so papers detailing instrumental performance are most welcome. A new commercially available SPAMS instrument had40 an overall particle detection efficiency of 0.01–4.23% for PSL particles in the 200–2000 nm size range; 0.44–6.57% for NH4NO3 particles in the 300–1000 nm size range and 0.14–1.46% for NaCl particles also in the 300–1000 nm size range. Useful reference mass spectra were provided for 32 different particle types relevant for atmospheric aerosol studies such as NH4NO3 or diesel soot. A mass spectra dataset, generated during a single day of air sampling at a rural location near a tramline, was used as training data to test the capability of either fuzzy clustering or reference spectra deconvolution protocols for the classification of particles. Applications of SPAMS included the chemical interrogation of coal combustion particles41 and urban aerosols.42 In the former paper, comparative analytical data were generated using ICP-MS and TEM techniques and in the latter paper using aerosol MS. In a derivation of this technique, a new tool for the analysis of solid materials employed43 LA to release particles from various powdered coal samples for subsequent analysis by SPAMS. Useful sample fingerprint data were thus generated for use in future source apportionment studies.
Other MS developments in the field of nuclear safeguarding included a PTR-MS method44 for the determination of gas-phase CH3I and the isotopic analysis of U-containing particles by either GD-TOF-MS45 or LA-TOF-MS.46
Although a feasibility study demonstrated58 that direct-on-filter, field-portable FTIR analysis could quantify respirable-size crystalline silica in coal dust samples, a PLS regression protocol was required to deconvolute a spectral overlap from the kaolinite commonly present in such samples. A method for the determination of trace airborne concentrations of RCS employed59 spot sampling to preconcentrate aerosols onto a small impaction target. The instrumental LOD was 8–55 ng and the resultant method LOQ 2–19 μg m−3 for a 12 L air sample. Analysis of certified RCS filter samples by a new QCL-based IR technique demonstrated60 that an instrumental LOD of ∼3 μg per filter was feasible. The potential for the development of a new compact hand-held aerosol analyser was therefore good.
New instrumental developments for nitrogen oxide species included61 a new field-deployable method for the simultaneous, real-time measurement of the four most abundant N2O isotopocules. Coupling of a trace-gas extractor unit with a QCLAS enabled autonomous operations at remote monitoring sites to be conducted at a sampling frequency of ∼1 per hour with repeatabilities better than <0.17‰. A fully autonomous QCLAS was successfully deployed62 on a tram in Zurich to provide city-scale NO2 concentration maps at high spatio-temporal resolution. Initial laboratory tests at typical urban NO2 air concentrations gave precisions of 0.23 ppb at one-second integrations and 0.03 ppb after 200 s averaging. Comparison of results with those obtained by reference CLD revealed a systematic NO2 loss of ∼10% to the internal walls so a calibration correction was required prior to deployment. In operational use, temperature-dependent drift within the optics was easily corrected by regular baseline measurements of NO2-free air provided via a separate air sampling line equipped with a NO2 scrubber unit. A new photometer design enabled63 measurements of both NO2 and NO to be made just a few seconds apart. The NO2 species was measured directly by absorption at 405 nm. The NO species was converted to NO2 by in situ reaction with O3 and then measured as NO2. The folded tubular design enabled an effective optical path length of 2 m to be packaged within a compact size. The LOD was <1 ppb. Interestingly, the authors proposed a similar photometric approach for the measurement of other key atmospheric pollutants such as black carbon or SO2 – so readers should watch this space!
3. Water analysis
3.1 Sample preconcentration and extraction
Fewer review papers were published this year in comparison to previous years, perhaps reflecting the maturity of the methods currently used, and tended to be more specific – for example on the use64 of functionalised nanomaterials for SPE prior to analysis by ICP-MS (190 references) or the continued use65 of micro-extraction for speciation analysis of Se in various matrices including water (52 references). The endless quest to extract Cd from environmental matrices was extensively covered66 (91 references).The most significant advances in analyte preconcentration for water analysis are summarised in Tables 1–3.
| Analytes | Matrix | Substrate | Coating or modifier | Detector | LOD (in μg L−1) unless stated otherwise | Validation | Ref. |
|---|---|---|---|---|---|---|---|
| Ag NPs | Environmental waters | Chitosan | Fe3O4 NPs | ICP-MS | 0.016 for 31 nm particles | Spike recovery | 328 |
| Ag, Au, Pd | Water, ore, automobile catalyst | Graphene oxide and MnFe2O4 NPs | 2-Mercaptobenzothiazole | ICP-AES | 0.045 (Au) to 0.076 (Ag) | Spike recovery (for water samples) | 329 |
| Ag, Pt | Seawater | Dowex® 1 × 8 | None | ICP-SF-MS | 0.02 (Pt) to 0.20 (Ag) pM kg−1 | Spike recovery from internal Geotraces QA samples; ID | 330 |
| AsIII | Water | Cellulose fibers coated with amorphous silica | (3-Mercaptopropyl)-trimethoxysilane | XRFS | 0.045 ng mL−1 | Spike recovery and IRMM CRM BCR-610 (ground water) | 331 |
| As, Cu, Pb, Se | Water | Graphene oxide NPs | CeO2 | EDXRFS | 0.07 (AsV, PbII) to 0.17 (CuII) ng mL−1 | Spike recovery and IRMM CRM BCR-610 (ground water) | 332 |
| AsV, CrIII | Water | Graphene oxide | Alumina | EDXRFS | 0.02 (As) and 0.11 (Cr) ng mL−1 | Spike recovery and NIST SRM 1640a (natural water) | 333 |
| As, Cr, Hg, Sb, Sn, V | Water | Ethyl methacrylate and 2-diethylaminoethyl methacrylate | 1,3-Propanesultone | ICP-MS | 1.0 (As) to 3.7 (Sn) ng L−1 | Chinese CRMs GSBZ3175-94, GSB07-1187-2000, GSB07-3171-2014, GSB07-1376-2001, GSB07-3173-2014, BWZ6705-2016, (all environmental water) | 334 |
| Bi, Cu, Ni, Pb, Se, Tl, Zn | Water | Fe3O4 NPs | In situ MnO2 coating | TXRFS | 0.19 (Pb) to 0.73 (Se) | IRMM CRM BCR-610 (ground water) | 335 |
| Cd, Cu, Fe, Mn, Ni, Pb, Zn | Seawater | Nobias PA-1 resin | EDTA and iminodiacetic acid coordination groups | ICP-MS/MS | 62.2 (Pb) pg L−1 to 1.76 (Ni) ng L−1 | NRCC CRM NASS-6 (seawater) | 336 |
| Cd, Co, Cu, Fe, Ni, Pb, Zn | Seawater | Presep® PolyChelate resin | Carboxymethylated pentaethylenehexamine | ICP-SF-MS | 0.6 (Pb) to 58.6 (Ni) pM | Internal Geotraces QA samples; spike recovery; IDA | 337 |
| CrIII | Industrial and river waters | MWCNTs | L-Arginine | FAAS | 0.7 ng mL−1 | NIST SRM 1640a (natural water) | 338 |
| Cu, Fe, Pb, Zn | Drinking, tap and seawaters | Silica gel | Diethyldithiophosphate | FAAS | 0.45 (Zn) to 1.17 (Pb) | NIST SRM 1643e (trace elements in water); spike recovery | 339 |
| CrVI | Water | Graphene oxide | 3-Aminopropyltriethoxysilane | EDXRF | 0.17 ng mL−1 | Spike recovery | 340 |
| CH3Hg+ | Water | CNTs | Fe3O4 NPs | GC-AFS | 5.4 pg mL−1 | Spike recovery | 341 |
| Hg2+, CH3Hg+ | Water | Graphene oxide | Thiourea | HPLC-ICP-MS | 3.8 (Hg2+) to 1.3 (CH3Hg+) ng L−1 | Spike recovery | 342 |
| Hg2+, CH3Hg+ and PhHg+ | Tap, environmental and waste waters | SiO2 and Fe3O4 NPs | Gamma-mercaptopropyltrimethoxysilane | HPLC-ICP-MS | 0.67 (CH3Hg+) to 0.74 (Hg2+) ng L−1 | Spike recovery | 343 |
| REEs | Water, seawater, sediments | TiO2 NPs coated with Fe3O4 | Di(2-ethylhexyl)phosphoric acid | ICP-MS | 0.01 (Tm) to 0.12 (Nd) ng L−1 | Chinese CRM GBW07301a (stream sediment); spike recovery (water) | 344 |
| REEs and Al, As, Co, Cr, Cu, Fe, Ga, Mn, Ni, Pb Se, Sb, Sn, V, Zn | River and seawaters | Mg(OH)2 precipitate | Triethylamine | ICP-MS | 0.01 (Ho) to 72 (Al) ng L−1 | NRCC CRMs CASS-4 (near shore seawater) and SLEW-3 (estuarine water) | 345 |
| 90Sr | River water and reactor coolant | Sr-resin 20–50 μm and Dowex® 50Wx8 100–200 mesh | ICP-MS | 14.5 Bq L−1 (2.9 pg L−1) | IAEA water standard for proficiency testing (II) and spike recovery | 346 | |
| 99Tc | Water | TRU® resin (50–100 mm) and AG® MP-1M resin | ICP-MS | 0.4 pg L−1 | Samples spiked with Re as a recovery tracer | 347 | |
| Analytes | Matrix | Method | Reagents | Detector | LOD in μg L−1 (unless stated otherwise) | Method validation | Ref. |
|---|---|---|---|---|---|---|---|
| AsIII | Water and soil | SSME | Decanoic acid, tetrahydrofuran and APDC | ETAAS | 0.2 ng mL−1 | Spike recovery | 348 |
| Cd | Seawater | SBME | Trioctylmethylammonium chloride (Aliquat® 336), kerosene, dodecan-1-ol | ETAAS | 0.04 nM | IRMM CRM BCR-403 (seawater) | 349 |
| Cd, Cu | Tap, river and seawaters | CPE | Triton X-114, 4-phenyl-3-thiosemicarbazide | FAAS | 0.2 (Cd) and 0.5 (Cu) | NRCC CRMs SLRS-5 (river water) and SPS RM SW2 batch 127 (surface water) | 350 |
| Cd, Pb | Water, soil, rice and tea | DLLME | 2-Amino-3-sulfhydrylpropanoic acid, tetrafluoroborate and 1-butyl-3-methylimidazolium tetrafluoroborate | ETAAS | 0.2 (Cd) and 0.7 (Pb) ng L−1 | NIST SRM 1643e | 351 |
| Fe3+ and total Fe | Tap, spring and river waters | DLLME | 4,5-Dihydroxy-1,3-benzenedisulfonic acid and cetyl pyridinium chloride | FAAS | 5.6 | HPS CRM-TMDW (trace metals in drinking water) | 352 |
| Hg2+, CH3Hg+ and EtHg | Water | DLLME | 2-Mercaptoethanol and 1-hexyl-3-methylimidazolium hexafluorophosphate | HPLC-CV-AFS | 1.5 (Hg2+, CH3Hg+) to 3.0 (EtHg) ng L−1 | Spike recovery | 353 |
| REEs | Simulated water | LLE | N,N,N′,N′-Tetraoctyl diglycolamide, EDTA, petroleum ether | ICP-AES | 0.04 (Nd) to 0.5 (Ho) ng mL−1 | Spike recovery | 354 |
| REEs, Zn | Water, urine and serum | CPE | Triton X-114 and 1,10-phenanthroline | MP-AES | 0.3 (Zn) to 1.0 (Tb) | Spike recovery | 355 |
| Analytes | Matrix | Method | Substrate | Reagents | Detector | LOD | Method validation | Ref. |
|---|---|---|---|---|---|---|---|---|
| CrIII | Potable and seawaters | CPE | Graphene oxide | Triton X-45 | ETAAS | 5 ng L−1 | NIST SRM 1640a (water), SPS RM SW2 batch 125, NRCC CRM CASS 6 (seawater), IRMM CRM ERM-CAO11b (hard drinking water) | 356 |
| SeIV | Rain, tap, river and seawaters | Micro dispersive SPE | Nanosilica | 1-Dodecyl-3-methylimidazolium bromide, APDC, ethyl acetate/Triton X-114 | ETAAS | 1.1 ng L−1 | NIST SRM 1643e (trace elements in water) | 357 |
| V | Water and beer | CPE | Graphene oxide | Triton X-114 | ETAAS | 0.02 μg L−1 | NIST SRM 1640a (water), NRCC CRM NASS 6 (near coastal seawater), IRMM CRM ERM-CAO11b (hard drinking water) | 358 |
3.2 Speciation and nanomaterial analysis
A series of tetrabromobisphenol-A-based brominated fire retardants were successfully determined70 in river, tap and seawaters by HPLC-ICP-MS/MS. Using a binary gradient elution system of 0.1% (v/v) acetic acid added to methanol (eluent A) or water (eluent B), seven fire retardant derivatives of the tetrabromobisphenol molecule were separated on a 15 cm long standard bore C18 column. An instrumental LOD of 0.1 μg L−1 for 79Br was achieved when ICP-MS/MS detection (H2 reactant gas) was used. For real samples, a 200 mL filtered aliquot was adjusted to pH 4–5 with HCl and extracted three times with 100 mL dichloromethane. The combined extracts were dried and the residue dissolved in 1 mL of methanol![[thin space (1/6-em)]](https://www.rsc.org/images/entities/char_2009.gif)
:water (4 + 1) of which 50 μL was injected onto the column. This extraction procedure gave recoveries of 84% (tap water) to 105% (seawater) depending on the spike level, and a MDL of 0.7–1.0 ng L−1 dependent on the target analyte. This performance was sufficient to detect tetrabromobisphenol A in a seawater sample and tetrabromobisphenol A-bis(2-hydroxyethyl ether) in a tap water sample.
Manganese in the +2 and +7 oxidation states was successfully separated71 on a column of C8-bonded silica using a pH 7.5 aqueous mobile phase containing 1 mM tetrabutylammonium hydroxide as an ion pairing agent and 0.36 mM EDTA as a complexing agent for MnII. Baseline resolution was achieved in <8.5 minutes when a flow rate of 1 mL min−1 was used. The ICP-MS LODs were 0.22 and 1.55 μg L−1 for MnII and MnVII, respectively. Although maintaining the stability of the sample could be difficult because MnVII converts to MnII slowly over time if present as the dominant form, this situation was actually unlikely for tap water samples. Results for the SCP Science ES-H-2 (ground water) and EU-H-3 (waste water) RMs were well within the expected ranges. The MnII concentrations in bottled, tap and artesian waters were successfully determined. In the opinion of this reviewer, this method is ripe for the use of SSID to correct for the redox interconversions that occur in the samples.
In a sequential extraction scheme for the selective extraction of PtIIand PtIVfrom river or tap water, platinum ion associates were formed72 in solution after reacting the chloro-anions of Pt with 5 mL of 5 × 10−3 M NaSCN and 5 mL of 1 × 10−3 M 1,3,3-trimethyl-2-[3-(1,3,3-trimethyl-1,3-dihydroindol-2-yliden)-propyl-1,3-dienyl]-3H-indolium chloride. These complexes were dispersed in the sample using DMF and then back extracted into 1 mL toluene before determination by ETAAS. Separation of the species occurred because the ion associates of PtII formed easily and were extracted first whereas PtIV ion associates only formed after sonication (400 W) of the sample for 5 minutes. For a 25 mL water sample, the LOD was 0.3 μg L−1 Pt.
The LODs for the determination of organolead, mercury and tin species by GC-ICP-MS were improved73 (for example to 0.1 pg L−1 for TML and to 12.6 pg L−1 for Hg2+) by adopting large volume injections. This was made possible by removing high-boiling-point matrix components through use of a programmed-temperature vaporisation inlet packed with the Bondesil-ENV, 125 μm, styrene–divinylbenzene polymeric resin on which analytes were preconcentrated at the head of the column. The analytes in a 50 mL sample were propylated with sodium tetrapropylborate and extracted into 500 μL of hexane. Three 25 μL sequential injections were made. Good results were achieved for a GEOTRACES comparison sample when using external calibration and isotopically labelled internal standards. Results for river water samples agreed with those obtained using the standard GC-ICP-MS method already in use by the authors.
Although the instrumental settings for single particle counting of nanoparticles by ICP-MS seem to have been standardised, sample treatment has not. Dilution of samples to the concentration range of 1–25 × 104 particles per mL provided77 linear and reproducible results. Centrifugal filtration and asymmetric FFF as pretreatment steps to eliminate the ionic fraction and to reduce the size LOD were considered unsuitable due to loss of finer material. Analysis of lake and tap water samples gave results below the instrumental LOD for total silver (value not reported) and below the size distribution LOD of 12–15 nm. When samples were spiked with 50 and 80 nm silver NPs at a concentration of 50 × 103 particles per mL, the measured particle sizes were 49 and 76 nm, respectively, and the particle number recoveries 84 and 135%, respectively. This suggested overestimation of the number concentration for the larger particles. The agreement of results with those obtained from TEM and asymmetric FFF analyses demonstrated that sp-ICP-MS was suitable as a first screening method for environmental samples. To enable the size characterisation of smaller (<60 nm) silver NPs, 10 mg of magnetic graphene oxide were added78 to a 10 mL solution containing 2 μg L−1 Ag+ and 0.05 μg L−1 of silver NPs for selective DME of the interfering Ag+. After removal of the graphene oxide, the silver background decreased from around 500 cps to about 10 cps, thereby reducing significantly signal overlaps and improving discrimination of particle events above the background signal. Prior to extraction only particles above 60 nm could be distinguished but after removal of 98% of the Ag+, particles down to 30 nm in diameter could easily be characterised. The method was tested on various river waters spiked with 0.25 μg L−1 Ag+ and 50 ng L−1 silver NPs.
3.3 Instrumental analysis
In-furnace preconcentration by repeat injection has been rediscovered81 for the determination of Tl in drinking waters by ETAAS. Using a 2 g L−1 ascorbic acid solution as a modifier and 10 drying (90–120 °C ramp) and pyrolysis (650 °C) steps before atomisation at 1400 °C, a LOD of 0.01 μg L−1 was obtained. Results for drinking waters compared well with those obtained using ICP-MS. Analysis of the Chinese water RMs GSB07-1978-2005 (206702, 206706, 206704) gave results within the certified values.
The use of photochemical vapour generation with photocatalysts is becoming more widespread to improve the effectiveness and speed of reaction. A nanoscale amine-functionalised titanium-metal–organic framework (MIL-125) was used83 to improve the generation efficiency for SeIV and SeVI in solution. After careful optimisation of the UV irradiation time, carrier gas, flow rate, pH and concentration of organic acid, a catalytic conversion efficiency of >80% was achieved resulting in an LOD of 0.3 ng mL−1 with ICP-AES detection. Analysis of the Chinese CRM GBW(E) 080395 (water) gave a result that fell just inside the range of certified values and the analysis of a water sample gave a value that agreed well with that obtained by ICP-MS analysis. When the internal walls of a microfluidic device were coated84 with Au-coated TiO2 NPs as photocatalyst, the PVG of inorganic As species occurred within 15 s at an irradiation wavelength of 365 nm and power density of 11 mW cm−1. This meant that the device could be used as an interface between an HPLC column and an ICP-MS instrument for the determination of AsIII and AsV in the eluent from the chromatographic column. Optimisation of the pH, formic acid concentration and pre-reduction step with 0.1% (m/v) Na2S2O4 gave spike recoveries of >92% for both species even in the presence of high concentrations of coexisting ions. The LODs for the optimised method were 0.23 and 0.34 μg L−1 for AsIII and AsV, respectively, and spike recoveries from industrial ground water were 93%. Analysis of the NIST SRM 1643e (trace elements in water), for which the total As concentration has a certified value of 60.45 ± 0.72 μg L−1, gave a concentration of 55.83 ± 0.36 (n = 3) μg L−1 for AsV.
The high sensitivity and selectivity of ICP-MS/MS means it is often used for single element determinations in water. Gallium was determined87 in natural water by ICP-MS/MS in both collision and reaction modes so that IDA could be used to quantify Ga accurately. Although the 71Ga signal acquired in H2 collision mode had no appreciable interference from MnO+, the 69Ga peak was significantly interfered by Ba2+ so NH3 had to be used as a reaction gas to mass shift the analyte to m/z 86. Using a high efficiency nebuliser, the best LOD obtained was 0.02 ng L−1 for m/z 71 with H2 as the collision gas. At m/z 86 the LOD was 0.28 ng L−1. Analysis of the NWRI RMs TM 9.2 and TMDA 64.3 (waters) gave results in good agreement with the reference values. Analysis of a series of mineral and ground waters showed that measurements at both masses gave comparable results so the use of IDA could be an appropriate approach in future research. The addition of Ba to the carrier flow via a T-piece allowed88 the determination of F by ICP-MS/MS as the 138Ba19F ion. The ions at m/z 157 were selected in the first quadrupole and interfering ions removed by collision with O2 in the collision cell. The signal response in the second quadrupole at m/z 157 was proportional to the F concentration. The LOD was 0.043 mg L−1 for total F. The recovery for water samples spiked with 1 mg L−1 of F was 97 ± 4% (n = 10). The reaction was rapid enough to allow the coupling of a chromatographic method for fluoride and fluoroacetate analysis to the ICP-MS instrument. The F response was independent of the compound being analysed so ICP-MS analysis would be suitable for the determination of other F-containing organic compounds. To determine 238U directly in filtered acidified seawater, the two quadrupoles were set89 at m/z 233 and 238, respectively. By using no reaction or collision gas in order to avoid signal attenuation, the formation of 232Th1H was reduced to negligible levels. Use of the 238U/233U ratio in the IDA gave best results for the IAEA-381 (seawater) interlaboratory comparison standard when samples were diluted 20 times. The LOD was 0.1 ng L−1.
The high sensitivity of ICP-MS/MS means it is now being investigated for the determination of radioisotopes in water. By setting both quadrupoles to m/z 210 without any reaction or collision gas to avoid any loss of signal, it was possible90 to determine 210Pb in drinking water with an instrumental LOD of 314 fg L−1, the equivalent of 0.9 Bq L−1. However, this level was still well above the WHO control level of 100–200 mBq so a previously published CPE preconcentration method followed by back extraction of 210Pb from a 390 mL sample was employed to lower the LOD to 15 fg L−1 (45 mBq L−1). This had the added advantage of removing the polyatomic interferences from Bi, Er and Pt. The sample turnaround time of 4.5 h was a considerable improvement over the 3–6 days required for liquid scintillation counting. Using ICP-MS/MS in single quadrupole mode with He as a collision gas, 226Ra was determined91 directly in ground water at levels down to 1 Bq L−1. The method LOD could be reduced to 5 mBq L−1 by using standard radiometric sample preparation procedures (SPE and evaporation) to preconcentrate from 1 L samples. In comparison to analysis using α-spectrometry which has an acquisition time of 5 days per sample, sample throughput was much improved due to the ICP-MS/MS acquisition time of just 5 minutes per sample.
The use of LA-ICP-MS for the determination of Hg in water has been proposed.92 Mercury was preconcentrated onto a printed gold electrode in the field using portable voltammetry. Use of a deposition voltage of 0.2 V ensured the electrodeposition of all inorganic and organic mercury species. In the laboratory the electrode was placed into a custom-made 15 cm3 LA cell and was ablated with a 266 nm laser operating at a spot size of 160 μm, a pulse repetition rate of 20 Hz and sampling rate 35 μm s−1. The method LOQ was 0.2 μg L−1. The method was validated against the Chinese CRM GBW08603 (water). The concentrations in river waters were below the LOQ but spike recoveries at 0.5 and 10 μg L−1 were 98–101% even for effluent samples. The direct analysis of ice cores by LA-ICP-MS means that high temporal resolution data can be obtained. In the estimation of subannual variability in dust levels, a 2.85 m long section of the North GRIP ice core was cut93 into 50 × 11 × 11 mm slices that could be placed into a custom cryocell in which the samples were ablated with a UV laser (200 μm spot size). The major elements Al, Ca, Fe, Mg and Na were detected by ICP-MS. An ice sample spiked with the NIST SRM 1648a (urban dust) was used to calibrate the procedure. The LODs were between 0.6 (Ca) and 48 (Na) μg L−1. Analysis of a section of the core from 2691.20 m below the surface showed that a large drop in elemental concentrations occurred over only 10 mm (corresponding to approximately 1 year). This was at a point where other research had identified a switch between a climatic stadial (cold) to an interstadial (warm) period occurred demonstrating that high resolution elemental analysis can complement stable isotope and other high resolution analyses for this kind of sample.
Although MC-ICP-MS would usually be used for isotope ratio analysis of natural waters, a quadrupole ICP-MS method has been published94 for the determination of Sr isotope ratios in mineral waters and wine. To separate Sr from the interfering Rb isotopes, Sr was coprecipitated out of solution by the slow addition of a 1 M Na2CO3 solution. Following washing with the coprecipitation solution, the precipitate was redissolved in 0.5 M HNO3. The instrument was used in time-resolved analysis mode so that only one point per peak was used with an integration time of 0.05 s per isotope thereby increasing the scanning speed over a 120 s acquisition. A series of sample dilutions were analysed for their Sr isotopic composition to generate a plot in which the slope gave the isotope ratio in the sample. This allowed signal variations due to the plasma or sample dilution to be corrected. Mass bias was corrected for using the NIST SRM 987 (strontium carbonate isotopic standard). Analysis of an in-house standard gave a δ87Sr value of −12.37 ± 0.66‰. It was possible to discriminate between 3 mineral waters using the measured δ87Sr values. An improved metal-free low-volume distillation method was developed95 to separate Br from Cl in water and seawater before determination of the Br isotope ratio by MC-ICP-MS. In the traditional method, Br− is oxidised to Br2 which is distilled into a KOH collector solution as BrO− before reduction to Br− by boiling with metallic zinc. Analysis of this final solution would have contaminated the instrument with high levels of Zn, so to avoid this, the authors oxidised the Br− with HNO3 added in the ratio of 1 + 4 HNO3:sample up to a maximum sample volume of 8 mL. The sample was heated with a candle and the distillate collected in 1 mL 0.5 M NH3 receiver solution for 10 minutes or until the receiver solution volume reached 1.5–3 mL. The Br collected was stable in this solution without the need for reduction to Br−. Analysis of 15 aliquots of an in-house seawater standard gave a mean δ81Br value of +0.06 ± 0.11‰ (1SD). This compared well with results from a chromatographic separation method. The determination of S isotopes by MC-ICP-MS is hampered by the 16O16O interference on 32S. Although this can be prevented by using a desolvation system, the high affinity of S (as SO42−) for the membranes used96 results in a large signal loss (>90%) and memory effects. Addition of NaOH to the sample at a Na:S mole ratio of 2:1 resulted in the formation of Na2SO4, a molecule that is thermally stable and has little affinity for the desolvation membrane, thereby reducing memory effects and signal loss (<2%). At concentrations of 3 nM S in pore waters, this improved method gave accurate determinations of δ34S and Δ33S (2SD precisions of ±0.18 and ±0.10‰, respectively) when the results were compared to those obtained for the NIST SRM NASS-5 (seawater) by IRMS and for a mono-elemental RM by ICP-MS.
4. Analysis of soils, plants and related materials
4.1 Review papers
Two broad-based reviews have appeared in the year covered by this Update. In one (107 references), on the analytical techniques used in paleotempestology studies of past tropical cyclones in the western North Atlantic Basin, the analysis of tree rings was discussed.100 Most of the review was concerned with either the application of X-ray spectrometry to the analysis of mineral cores or with the use of proxies, such as grain-size analysis and loss-on-ignition. The report for a long-term study of the transformation of PTEs in soil/water/sediment systems included101 a review of sequential extraction procedures. The researchers also presented work aimed at improving the BCR methodology, in particular by the use of CF systems and sonication (to accelerate leaching).Reviews on the determination of individual elements have also appeared. In one (121 references) of thiolated As in natural systems, the authors provided102 an overview of formation mechanisms, reaction chemistry, speciation and analytical methodologies. Unsurprisingly, the reviewers concluded that future research needs to be devoted to determining the molecular structure of unknown thioarsenic compounds in various environmental samples. In reviewing (85 references) Sb measurements in environmental matrices, Maher et al.103 reported that it was not possible to quantify organic Sb species in plants because of the oxidation of SbIII to SbV and the formation of artefact Sb species. It was therefore thought that XAS should be considered for this analysis as it can discriminate between SbIII, SbV, Sb minerals, oxides, and adsorbed Sb species. A review (46 references) covered104 the application of NAA and ID-MS to the determination of metals and metalloids in dietary supplements. In addition, speciation studies in which separation techniques combined with element-specific detectors were considered.
Reviews on the applications of several laser-based techniques included a discussion by Bauer and Buckley105 (52 references) of recent and novel applications of LIBS. Also discussed were calibration-free and indirect measurements, such as that of pH and humification in soil and heating value in coal. The analyses of agricultural materials, coal, minerals, and metals were included in the review as was the potential of spatial heterodyne spectroscopy to overcome, through the use of interferometers, the limitation of the small optical throughput of most spectrometers. Both LIBS and LIFS methods were considered106 (36 references) suitable for measuring the degree of humification of soil organic matter in whole soils. In particular, LIFS could be used to probe the whole solid soil sample, thereby avoiding the need for chemical or physical treatments to separate organic matter from the mineral matrix. It was also concluded that the emerging application of LIBS to whole soils appeared promising due to its good sensitivity, selectivity, accuracy and precision. In a review by Busser et al.107 (83 references) on elemental imaging by LIBS, particular attention was paid to the in situ elemental profiling and mapping of metals in biological materials, including plants. Advantages of this technique included minimal sample preparation, compatibility with optical microscopy and the simultaneous detection of low- and high-Z elements. Advances in the applications of LA-ICP-MS are such that publications in just the past three years merited a review108 (142 references). The authors highlighted the value of fast wash-out ablation cells and calibration strategies such as ink printing and dried-droplets for applications such as the mapping of elemental distributions in biological samples (including plant tissues), detection of NPs and protein and single cell analyses.
4.2 Reference materials
Various comparison studies have featured soil, vegetation and sediment CRMs. The NIST SRMs 2709a (San Joaquin soil) and 1515 (apple leaves) were included109 in the suite of eight RMs used in an interlaboratory comparison of MC-ICP-MS methods for the determination of 25Mg and 26Mg isotopic abundances. It was concluded that all the materials were suitable for validation of 25Mg and 26Mg determinations in geochemical studies. The 129I content of three environmental CRMs were determined110 by AMS and TIMS. One of the materials, IAEA-375 (soil collected approximately 160 miles northeast of Chernobyl), is currently the only soil/sediment material with a certified 129I activity but has not been available for purchase since 2010. The other two materials, NIST SRM 4354 (freshwater lake sediment) and NIST SRM 4357 (ocean sediment) have certified activities for a variety of radionuclides but not for 129I. The discrepancy between the values measured by AMS and TIMS in this study and those published previously indicated that a more thorough 129I assessment was needed, including measurements by other laboratories, before these two NIST SRMs will be useful in the determination of 129I. The researchers concluded that there is an immediate need for CRMs with known 129I concentrations. As the lack of CRMs was seen as the main problem in the QC of the determination of 237Np in environmental samples, Benedik and Trdin111 used a combination of three techniques (γ-ray spectrometry; α-particle spectrometry following separation of Np radioisotopes by ion-exchange or extraction chromatography; and pre-separation radiochemical NAA) to determine 237Np in a variety of environmental materials, including CRMs, sediments and soils. The Ge concentrations for three Chinese soil CRMs (GBW 07401, GBW 07404 and GBW 07407) were revised112 and a value proposed for the plant CRM ERM-CD281 (ryegrass).4.3 Sample preparation
The topic attracting most attention in the sample preparation of soils has been acid extraction. The best MAE procedure for the determination of multiple elements in organic soil amendments used117 a mixture of 3 mL HNO3, 1 mL HBF4 and 2 mL H2O for 150 mg of sample and was validated by the analysis of a CRM (Sigma-Aldrich sewage sludge CRM 029). In the comparison118 of open-vessel digestion on a hot plate and closed-vessel MAE for the multi-elemental analysis of the organic-rich soil CRM BCR 700, the MAE procedure was faster and easier to use. In a method for the determination of Cd in soil micro-arthropods, a sample mass of 50–150 μg was digested119 with 30 μL of a HNO3–H2O2 (3 + 1) mixture at 105 °C for 3 h. Two analytical procedures based on the generation of volatile TBT derivatives, their separation by headspace SPME and subsequent determination using ICP-AES were developed120 for the selective determination of trace TBT in the presence of other butyltins and inorganic tin in sediments without the use of chromatography. A microwave-assisted leaching of Sn compounds from the sediment using 25% (v/v) acetic acid was applied as sample pretreatment. The first method took advantage of TBT chloride releasing from the lecheate after adding 3 M HCl. The analyte was subsequently obtained by headspace SPME using carboxen-poly(dimethylsiloxane). The second method involved the use of masking agents, namely EDTA and diphenylcarbazone, which formed stable chelates with MBT and DBT, respectively, followed by the ethylation of TBT at pH 5 using sodium tetraethylborate. The methods were validated by the analysis of several CRMs, including NRCC marine sediments PACS-2 and SOPH-1, and BCR 646 (freshwater sediment).
The acid digestion of plants has attracted considerable attention. For the comparison of the digestion and spectroscopic determination methods used to measure B concentrations in maize shoots, increasing doses (0, 2.5, 5 and 10 mg kg−1 B) were applied121 to soil and maize plants. Although the measured concentrations were dependent on the sample preparation and measurement procedures used, there were no statistically significant differences between the results obtained using dry ashing and MAD when the final determination was by ICP-AES. Therefore the ICP-AES measurement was considered compatible with both dry ashing and MAD and so was suggested as a suitable method to determine B contents of maize shoots. The effects of boiling time and acidity were studied122 in the determination of As, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn in aqueous herbal teas extracts by various acids including lemon juice. Multivariate techniques such as redundancy and PCA were used. Metal release was not considerably affected by the boiling time whereas the acidity of the medium was generally positively correlated. The extraction efficiencies of the metals were classified as highly, moderately, and poorly extractable, with Cr showing largest variations. The optimised procedure for the extraction of Ca, Cu, Fe, Mg, Mn and Zn from cassava peel involved123 extraction with dilute HNO3, HCl and acetic acids assisted by ultrasound and microwave radiations. The accuracy was checked by the analysis of NIST SRM 1515 (apple leaves) and by comparison of the results with those obtained with a microwave-assisted total digestion method. A fast analytical method for the determination of Ca, Cu, Fe, K, Mg, Mn, Na, P and Zn in foodstuff samples by ICP-AES involved124 the use of ultrapure 50% (v/v) formic acid on a heating block. The recoveries for three NIST SRMs were 80.7–114% and the RSDs <20%. The LODs were 0.06 to 600 mg kg−1. It was concluded that the formic acid procedure is a simple alternative to MAE and, as formic acid is not an oxidant, that it has potential as an extraction procedure for speciation analysis.
The advantages of acid extraction include speed, simplicity, low cost and use of diluted acid solutions in mild conditions (temperature and pressure). However, most extraction procedures are performed in batch mode and so are more laborious, time-consuming and susceptible to errors than procedures employing flow and flow-batch modes. A flow-batch system was developed125 for the online determination of macro and micronutrients in dried plant leaves by ICP-AES with the aim of producing a fast screening procedure. The procedure was simple to operate, inexpensive, allowed online determination and could easily be applied in any routine analysis laboratory. The best efficiency extraction was achieved using 50 mg of dried spinach leaves, 10 mL of 8% (v/v) HCl and a 4 minute extraction time even though some elements (Cu, K, Mg, Mn and Na) could be extracted in as little as 30 s.
Standard protocols for the digestion of plant matrices do not generally involve the use of HF and so silica will remain in the solid phase after digestion. A study on the determination of Cu, Mn and Zn in herbs by FAAS showed126 that incomplete oxidation of carbon was beneficial as the residual carbon blocked silica surfaces, and so prevented reabsorption of the metal ions. It was advised that particular attention should be paid to the presence of silicon in plant samples, including CRMs for which silicon contents are rarely given.
Selenium isotope variations have been proved to be a powerful tracer for redox processes and are therefore promising for the exploration of the species-dependent Se metabolism in plants. The analysis of plant tissues and growth medium (phytoagar) is particularly challenging and requires specific preparation and purification steps to ensure precise and valid Se isotope data. Validation tests on Se-free plant material and phytoagar doped with a RM of known Se isotope composition revealed127 the high impact of organic residues on the accuracy of MC-ICP-MS measurements. Extraction using either a MAD procedure for plant tissue or a vacuum filtration method for phytoagar gave full Se recoveries and reduced organic residues to a minimum. Only the purification method with no detectable organic residues, HG and trapping, resulted in valid mass bias correction for plant samples with an average deviation to true δ82Se/76Se values of 0.2‰ and a reproducibility (2SD) of ± 0.2‰. For phytoagar, this test yielded a higher deviation of 1.1‰ from the true value and a reproducibility (2SD) of ± 0.1‰. The developed methods provided sufficient accuracy and precision to be considered a promising approach to resolve plant internal Se isotope fractionations, for which δ82Se/76Se values of +2.3 to +3.5‰ for selenate and +1.2 to +1.9‰ for selenite were obtained.
Understanding the translocation of nanoparticles into plants is challenging because qualitative and quantitative methods are still being developed and the comparability of results produced by different methods remains uncertain. A study on the uptake of TiO2 NPs and larger BPs in rice plant (Oryza sativa L.) used128 electron microscopy and sp-ICP-MS to analyse plant extracts. Two dissolution procedures were evaluated for their ability to dissolve particles without causing aggregation. An acid digestion procedure resulted in higher particle numbers and the detection of a broader range of particle sizes than an enzymatic digestion approach and so highlighted the need for development of robust plant digestion procedures for NP analysis. The ICP-AES total elemental analysis of plants exposed to TiO2 NPs and BPs at 5 and 50 mg L−1 concentrations revealed that TiO2 NPs penetrated into the plant root and resulted in higher Ti accumulation in above ground tissues than that produced by BPs.
Several procedures were developed with extraction and digestion reagents other than acids. A microwave-assisted wet partial-digestion method using H2O2 in a high-pressure system was developed129 for the determination of Br, Cl and I in soils by ICP-MS. Sample masses of 50 and 25 mg containing 50 and 98% organic matter, respectively, were digested efficiently with just 5 mL of 50% H2O2 solution at a maximum temperature of 250 °C. The inaccurate results for I obtained at lower digestion temperatures were attributed to the formation of I2 during nebulisation. The method was validated130 by the comparison of the results with those obtained by pyrohydrolysis and by the analysis of NIST SRM 1632c (bituminous coal). The ICP-MS determination of I is prone to both memory effects and isobaric interferences. To support the study of 131I deposition following the Fukushima Daiichi Nuclear Power Plant accident, a method of extraction with TMAH at 90 °C was developed.131 Oxidation with K2S2O8 released I from co-extracted organic matter. The addition of ammonium sulfite (0.01% (m/v)) overcame memory effects, and the use of LLE avoided both a matrix effect and isobaric interferences in the subsequent ICP-MS-MS analysis. The method was validated by the analysis of several CRMs and was applied to the determination of 129I and 129I/127I ratios.
Residual carbon can be an issue in the determination of As because it can enhance ionisation in a plasma. By showing that the results of the determination of total As in the sub-critical water-3% (v/v) H2O2 extracts132 of rice by ETAAS were, on average, not significantly different from those by ICP-MS, it was deduced that there was no carbon effect. Samples (200 mg) were extracted with 10 mL H2O–H2O2 (9 + 1) at 200 °C and 25 bar. All AsIII was converted to AsV so making speciation by AEC-ICP-MS easier as the inorganic As peak was moved away from the peaks of any As cations present in the void volume and also from the poorly resolved DMA peak. Three CRMs and two well-characterised rice flours (IMEP 107 and NIES 10a) were analysed and, although there was agreement regarding the total As concentration, there were problems with the results for the As species. The authors concluded that “total As in extracts can be quantified by ICP-MS without the use of a collision/reaction cell as the residual carbon doesn’t cause any enhancement of the ICP-MS signals”. However, the ionisation enhancement effect in question occurs in the plasma, and is not affected by gas-phase reactions inside the spectrometer.
A number of methods involving sample fusion were reported. Germanium exists at trace levels in the Earth’s crust and is a powerful geochemical tracer of the Si cycle. A simple and reliable method for determining Ge contents in environmental samples used112 the chemical preparation procedure developed for Si isotopes, as Ge and Si have very similar chemical properties. Powdered samples were fused with NaOH at 720 °C after initially drying at 105 °C and then calcinating at 250 °C rising to 450 °C. Advantages were: (a) efficient removal of the matrix and main interferences affecting Ge determinations by ICP-MS; (b) a low LOD of 6 ng L−1; (c) relative repeatability of approximately 3% obtained on 74Ge; (d) robustness and accuracy based on agreement within errors with the published Ge values for rock reference materials (BHVO-2, AGV-2 and BCR-2). The Ge values for the three soil CRMs GBW 07401, GBW 07404 and GBW 07407, were revised to 1.67 ± 0.09, 2.41 ± 0.18 and 1.89 ± 0.10 μg g−1, respectively. In addition, a value of 70 ± 3 μg g−1 was proposed for the plant CRM ERM-CD281. This procedure should make it possible to use both Ge and Si as tracers of the Si biogeochemical cycle within a single measurement procedure. An alkali flux (K2CO3–MgO) was used133 to fuse rocks, soils and sediments for the determination of total S and sulfate S by ICP-AES. The fused material was leached with water, acidified with perchloric acid and the KClO4 precipitate removed by filtration. The emission line at 181.978 nm was used with nitrogen purging. Interference effects were investigated and the method validated by the analysis of seven CRMs. A method for the determination of actinides (Am, Np, Pu, Th and U) and 90Sr in soils by ICP-MS was based134 on fusion with LiBO2–LiBr (98.5 + 1.5) followed by coprecipitation with Ca3(PO4)2 by adding NH4OH. The analytes were preconcentrated by SPE in an automated flow system connected directly to the spectrometer. The procedure was applied to the analysis of soils obtained in intercomparison exercises (IAEA-TEL-2012-03 and IAEA-TEL-2015-04) organised by the IAEA and to the analysis of the CRM IAEA 385 (Irish Sea sediment). A pretreatment method commencing with lithium metaborate fusion was developed135 for the determination of Am, Cm and Pu isotopes in large environmental samples by α-spectrometry and ICP-MS. After fusion, the button was dissolved in a mixture of HNO3 and HCl and polyethylene glycol (PEG-6000) added to flocculate and remove the gelatinous silica precipitate. For the subsequent co-precipitation step, TiOCl2 solution was added and the pH of the sample raised to 12–13 with 10 M NaOH. The iron hydroxide precipitate that formed was removed by centrifugation and dissolved in concentrated HCl. A further co-precipitation with CeF3 was carried out with dissolution in a mixture of HNO3 and boric acid followed by preconcentration by SPE. The method was validated by analysis of 20 g of soil samples, spiked with known amounts of 241Am and 239Pu, as well as by the analysis of the IAEA CRMs 384 (Fangataufa Lagoon sediment) and 385 (Irish Sea sediment).
Developments in sample treatment prior to extraction or dissolution included136 a study of sample storage and preparation procedures for characterisation of element fractionation in lichens by ICP-MS. Cryogenic freezing gave the best reproducibility for total extractable elemental concentrations over a period of several months but was inappropriate for sequential extraction procedures. For such procedures, either no sample preparation or drying in a desiccator gave the best extraction recoveries so the assumption that species stability can be ensured through cryopreservation and freeze-drying techniques needs to be revisited. Two IEC separation schemes for the selective and quantitative isolation of the REE group from accompanying elements were developed137 because direct analyses by ICP-MS or INAA could be inaccurate for some REEs. After checking their performance with the aid of radioactive tracers, the schemes were further used for the determination of the REEs in CRMs. Comparison of these results with those obtained by ICP-MS and INAA analyses without preseparation revealed that, although the direct analyses provided reliable results for the majority of REEs, results for Sc, Yb and Tm were inaccurate. High performance IR quartz tubes were used138 in dry-ashing devices as heating elements for the first time and were proposed for preparation of botanical samples with high levels of organic matter. A layer of gold was plated on the surface of the heating tube in order to reflect and focus IR light and enhance the effect of heating. The flow-through quartz ashing tube built in-house ensured oxygen penetrated the entire sample layer, thereby increasing the efficiency of ashing. In addition, the heating chamber had a very small volume so the size of the proposed device was only one-fifth of a traditional muffle furnace. The sample could be heated from room temperature to 900 °C in just 1 minute and for most kinds of samples, ashing was complete within 30 minutes. The procedure was validated by analysis of five Chinese CRMs. The procedure was further developed139 (a) to allow the addition of O3 to accelerate the carbonisation process, (b) to trap volatile elements in a cold trap and (c) to use ultrasound to accelerate dissolution of the ash. The so-called “volatile” elements As, Cd and Pb were determined accurately in three Chinese CRMs.
Three pretreatment methods involving ferric hydroxide precipitation were compared140 for the determination of Pu in water-soluble and exchangeable (1 M ammonium acetate) fractions of soils. The best method involved wet-ashing with HNO3–H2O2 followed by Fe(OH)3 coprecipitation. In the final stage of the procedure, the Pu was preconcentrated by multistep SPE. Using an SF instrument for analysis, the LOD for 239Pu in a 100 g sample was 9 μBq kg−1 for the overall process. Recoveries of 242Pu spikes from both water-soluble and exchangeable fractions were 70%.
| Analyte | Sample matrix | Method | Reagent(s) | Detector | LOD (μg L−1) | CRMs or other validation | Ref. |
|---|---|---|---|---|---|---|---|
| AsIII, AsV | Soil, water | UAE-SSME | Decanoic acid, tetrahydrofuran, APDC | ETAAS | 0.2 for both species | Dust CRM (no details given); spike recovery | 348 |
| Cd, Cu, Pb | Coastal seawater, surrogate digestive fluids, soil leachates | APDC, toluene, nitric acid | ICP-AES | 0.002 for Cd, 0.001 for Cu, 0.006 for Pb | Spike recovery | 359 | |
| Cd, Pb | Soil, water, rice, tea | UA-DLLME | 2-Amino-3-sulfhydrylpropanoic acid (L-cysteine), tetrafluoroborate ion, 1-butyl-3-methylimidazolium tetrafluoroborate | ETAAS | 0.0002 for Cu, 0.0007 for Pb | NIST SRM 1643e (trace elements in water); spike recovery | 351 |
| Co | Water, spinach, black and green tea, tomato sauce | DDC, methanol, carbon tetrachloride | FAAS | 7 | GSJ CRM JSL1 (Toyama clay slate); spike recovery | 360 | |
| Cu | Edible oils | DLLME | Chitosan, hydrochloric acid, water | ICP-AES | 2 | Spike recovery | 361 |
| Mn | Basil herb, spinach, dill, and cucumber barks | DE-LLE | Choline chloride (vitamin B4) and tartaric, oxalic, or citric acids | ICP-AES | 0.5–1.0 | NIST SRM 1573a (tomato leaves); spike recovery | 362 |
| Pd | Soil, water | UA-MLLE | Na DDC, octanol | FAAS | 0.9 | Spike recovery | 363 |
| SeIV, SeVI | Rice | MIL-DLLE | 2,3-Diaminonaphthalene, 1-butyl-3-methylimidazolium tetrachloroferrate | ETAAS | 0.02 for both species | NRCCRM CRMs GBW 10010 (rice), 10043 (Liaoning rice), and 10045 (Hunan rice); spike recovery | 364 |
| Analyte | Matrix | Substrate | Substrate coating/modifying agent or analyte complexing agent | Detector | LOD (μg L−1 unless stated otherwise) | CRMs or other validation | Ref. |
|---|---|---|---|---|---|---|---|
| Ag, Au, Pd, Pt | Waste water, seawater, road dust | Graphene oxide, MIO NP, silica | Polypyrrole–polythiophene | FAAS | 0.1–1 | Zidarovo-PMZrZ (206 BG 326) (polymetallic gold ore); spike recovery | 365 |
| AsIII | Rice plants and ash tap, well and mineral water | MWCNTs | DDC | ETAAS | 0.008 | Spike recovery | 366 |
| Cd | Tobacco leaves, cigarettes, pipe tobacco | Dowex 50W-X8 | ICP-MS | 0.001 | NRCC CRM DORM-2 (dogfish muscle) | 367 | |
| Cd, Co, Cu | Molasses, rice, water | Silica gel | 2-[N,N-Bis(salicylaldimine)]aminoethyl amine | FAAS | 0.5 for Cd, 1 for Co, 0.6 for Cu | INCT CRM CTA-VTL-2 (Virginia tobacco leaves), LGC CRM NWTM-15.2 (water), comparison with an ICP-MS method; spike recovery | 368 |
| Cd, Fe, Mn, Pb, Zn | White and wild rice | Oxidized MWCNTs | Bathophenanthroline | FAAS | 0.1–0.4 | NCS CRMs DC73349 (bush branches and leaves) and ZC73029 (rice); spike recovery | 369 |
| Ce, Th | Bastnaesite ore | Amberlite XAD-16 | Anoxybacillus flavithermus | ICP-AES | 0.4 for Ce, 0.1 for Th | IAEA CRM Soil-7 | 370 |
| Cu | Tomato, shallot, green papaya, carrot, eggplant, garlic, yard long bean, chili pepper, lime green papaya salad | MIO chitosan-graphene NPs | ICP-AES | 0.01 | Comparison with another method; spike recovery | 371 | |
| Cu species | Corn flour, cassava root flour, oregano leaves, cumin, wine | Bamboo fiber | Bathocuproine | FAAS | 0.5 | NIST SRM 1573a (tomato leaves); spike recovery | 372 |
| Cu | Plants, water | MIO NPs | SDS, 1-(2-pyridylazo)-2-naphthol | FAAS | 0.6 | HPS CRM TMDW-500 (drinking water), NIST SRMs 1570a (spinach leaves) and 1573a (tomato leaves); spike recovery | 373 |
| Cu, Pb | Poppy seeds, chamomile, water | MIO–MoS2 nanocomposite | FAAS | 2 for Cu, 3 for Pb | NCS CRMs DC73349 (bush branches and leaves), SPS-WW2 (waste water level 2), TMDA-53.3 (environmental water); spike recovery | 374 | |
| Cu, Pb | Cigarettes, hair | MIO MWCNTs | Chromotrope FB | FAAS | 4 for Cu, 12 for Pb | NCS CRM DC73349 (bush branches and leaves); spike recovery | 375 |
| Pb | Water, synthetic seawater, serum physiological solution, chocolate powder, Ginkgo biloba | Pb-imprinted poly(methacrylic acid) | 1-(2-Pyridylazo)-2-naphthol | FAAS | 0.5 | NRCC CRM MESS-3 (marine sediment); spike recovery | 376 |
| Pb | Water, chocolate powder, Ginkgo biloba | SiO2/Al2O3/SnO2 ternary oxide | FAAS | 2 | NRCC CRM MESS-3 (marine sediment); spike recovery | 377 | |
| Pb | Industrial effluent, sludge, black tea, spinach leaves, green beans | Silica | Penicillium chrysogenum | FAAS | 7 | BCR CRM701 (lake sediment); comparison with another method (ICP-AES); spike recovery | 378 |
| Pd | Soil | MIO NP SDS 2-(5-bromo-2-pyridylazo)-5-diethyl aminophenol | 5-Amino-1,10-phenanthroline | FAAS | 0.1 | spike recovery | 379 |
| REEs | River water, lake water, seawater and sediment | MIO–TiO2 NP di(2-ethylhexyl)phosphoric acid | ICP-MS | 0.0001 for Nd, 0.00001 for Tm | NRCCRM GBW07301a (stream sediment), spike recovery | 344 | |
| REEs | Tea leaves, water | TiO2 nanofibres | ICP-MS | 0.0006 for La, 0.0001 for Lu | NRCCRM CRM GBW 07605 (tea leaves); spike recovery | 380 | |
| SbIII, SbV | Tea leaves, tea infusion | TiO2 nanofibres | DLLME | ETV-ICP-MS | 0.00002 for both species | NRCCRM CRM GBW 07605 (tea leaves) | 381 |
| SbIII, SbV | Soil, tuna, rice, spinach, black tea, mixed fruit juice, iced tea | Polystyrene oleic acid imidazole | DDC for SbIII | ETAAS | 0.006 for SbIII, no LOD given for SbV | NRCC CRM SLRS-5 (river water); spike recovery | 382 |
| TlI, TlIII | Soil, green cabbage | Al2O3 | Oxine, diethylenetriaminepentaacetic acid | ICP-MS | 0.04 for TlI, 0.2 for TlIII | IAG CRMs OU-6 (Penrhyn Slate), AMH-1(Mount Hood andesite), GBPG-1 (garnet-biotite plagiogneiss); NIST SRM 2711 (Montana soil); NRCC CRM GBW07405 (soil) | 383 |
| Th, U | Soil, sediment, phosphogypsum | UTEVA resin | ICP-MS | 0.7 ng kg−1 for Th, 0.1 for U | IAEA CRM 375 (soil); BCR CRM 320 R (sediment); MatControl RM CSN-CIEMAT 2008 (phosphogypsum) | 384 | |
4.4 Instrumental analysis
In the following sections, advances in methodology in which the nature of the instrument or some feature of the instrument operation has been a key feature have been selected for inclusion under the appropriate headings. Sample handing and introduction procedures, such as CVG and LA, are included in the section appropriate to the spectrometric technique concerned.Other applications of AAS included143 the determination of P by ETAAS in soil fractions. The procedure was validated by the analysis of CRMs BCR-141R (trace elements in calcareous loam soil) and NIST SRM 2710a (Montana soil) and by comparison of results with those obtained by spectrophotometry. A PVG system with sonication-assisted batch-type gas-liquid-separator was optimised144 to improve the sensitivity, precision and accuracy of Ni determination by AAS. The inefficient nebuliser of the flame unit was replaced by a UV-assisted vapour generator and atomisation of the volatile species generated was achieved with a quartz tube atomiser. Parameters optimised included organic acid (formic acid) and concentration (6 M), UV irradiation period (4 minutes), sonication period, carrier gas flow rate (2140 mL min−1) and atomiser temperature (1000 °C). The LOD was 10 μg L−1. The method was validated by spike recoveries (95–104%) and the analysis of a waste water CRM, EU-L-2.
Addition of Freon R12 (CCl2F2) as modifier gas in ETAAS had145 a strong influence on pyrolysis and vaporisation temperatures and sensitivity. Although the Freon lowered the volatilisation temperature through creation of volatile fluorides and so allowed direct analysis of medicinal herbs without digestion, standard additions were still necessary because the Freon introduced variable matrix effects in the determination of As, Cd, Cr, Cu, Mn, Ni, Pb and Zn in medicinal herbs. Results for the analysis NIST SRMs 1515 (apple leaves) and 1574 (peach leaves) were in good agreement with the certified values.
The novel use of matrix-matched RMs made with gelatine made it possible147 to determine Ca at concentrations of up to 1000 μg g−1 in the LA-ICP-AES determination of Ca, K and Na in tobacco plant stems and leaf petioles. Advantages of the system over LA-ICP-MS analysis included simultaneous detection, lower running costs, lower sensitivity to contamination and user-friendliness. Plant samples were mixed148 with paraffin as a binder to provide better cohesion of the particles in the pellets as well as supply C to serve as IS (atomic line C I 193.027 nm) as a way to compensate for matrix effects and variations in the ablation process. The LODs were 0.4–1and 20–600 mg kg−1 for metallic and metalloid elements (B, Ca, K, Mg, Mn and Zn) and non-metallic elements (P and S), respectively. The method was validated by the analysis of the four NIST plant SRMs. Sample preparation involved grinding the samples to ≤75 μm to have the same particle size as the SRMs, binder addition and mixing and pellet pressing. This direct solid analysis was considered less time-consuming and “more efficient” than wet sample digestion methods.
Reports on modifications of the plasma conditions included149 a paper in Japanese in which the determination of trace-levels of Rb with cool plasma conditions (750 W and 0.95 mL min−1 carrier gas flow rate) was described. The low LOD of 0.005 mg kg−1 could be attributed to the effective suppression of spectral interference from argon emission lines when measuring the Rb emission line, thereby improving the S/N 20-fold. Matrix effects of Ca and K were still observed for the measurement of Rb by cool plasma ICP-AES, so approaches such as gravimetric standard addition incorporating IS correction were required to correct for these matrix effects in real samples. Results for the analysis of NMIJ CRMs 7502-a (white rice flour), 7505-a (tea leaf powder) and 7512-a (milk powder) were in good agreement with the certified values (2 to 9 mg kg−1).
In comparison with FAAS, an AES instrument with a MIP sustained in inexpensive N2 had150 similar linear dynamic ranges but significantly improved sensitivities. It also had short start and warmup times (∼20 minutes). When used with a simple dry-ashing apparatus, it was a cost-effective alternative for trace element analysis. The Helios Rapid Ashing prototype reached 750 °C using a ceramic radiative heating element, and so gave sample decomposition in <5 minutes. It required inexpensive sample holder materials such as aluminium foil and quartz fibre pads. Samples were decomposed at 500 °C, and the analytes extracted into a 10% (v/v) HCl solution before analysis. The LODs were 2, 20, 30, 0.6, 2 and 5 μg L−1 for Ca, Fe, K, Mg, Na and Zn, respectively. The procedure was validated by the analysis of NIST SRM 1573a (tomato leaves) and by comparing the results to those obtained with an acid MAE procedure. The method was applied to the analysis of “challenging samples” such as cheese, butter, peanut butter, infant formula and biodiesel.
The possibility of direct analysis of soils by using a high-powered (5–15 kW) two-jet plasma AES was investigated151 using certified RMs of black earth, grey desert and red soils. Calibration standards were prepared by mixing 100 mg of a suitable powdered CRM and 100 mg of a spectroscopic buffer (graphite powder containing 15% (w/w) NaCl) and an aerosol generated by a spark discharge between zirconium electrodes, which created blast waves that agitated the powder. The elements As, B, Cd, Cu, Hg, P and V could be determined after a 2-fold dilution with the spectroscopic buffer, whereas a 10-fold dilution was used for Be, Co, Cr, Ga, Nb, Pb and Zn. The strong matrix effects observed for red soil with its high Al and Fe concentrations led to overestimation of the Ni, Pb and Sn concentrations. Clear advantages of the technique over existing methods were the simple sample preparation process requiring no reagents except for the spectroscopic buffer, and the use of the same calibration samples in the analysis of different soils.
Developments in sample introduction by CVG included156 a novel and sensitive LSDBD-CVG procedure developed for the measurement of Pb by ICP-MS. Dissolved Pb2+ was readily converted to volatile species by LSDBD-plasma-induced chemical processes in the presence of 5% (v/v) formic acid in a supporting electrolyte (0.01 M HCl). The sample solution was converted to aerosol and simultaneously mixed with the discharge generated at the nozzle of a pneumatic nebuliser. This facilitated the CVG of Pb because of the enhanced interaction of sprayed analytes and the plasma. The LOD was 0.003 μg L−1 and the RSDs (n = 5) were 2.1 and 1.7% for 0.03 and 1 μg L−1 Pb standards, respectively. In comparison with other CVG methods, the new procedure required no unstable reagents, had a rapid response, was easy to interface with FI and had a high tolerance for coexisting ions. The accuracy of the proposed method was demonstrated by analysis of several CRMs including plants and soils. The new procedure extended the number of elements accessible by plasma-CVG and provided an alternative efficient approach for the CVG of Pb.
Although PVG is emerging as a promising analytical tool for Te determination thanks to its efficient matrix separation and simple procedure, the PVG generation efficiency has remained low. The sensitivity was157 15-fold better in the presence of ferric ions when using nano-TiO2 as a photocatalyst in the reaction chamber compared to the conventional PVG response. The high photocatalytic performance of TiO2 under short-wavelength UV irradiation (254 and 185 nm) provided equal and high PVG generation efficiencies for TeIV and TeVI in the presence of ferric ions. The LOD was 1.0 ng L−1. The precision (n = 7) for TeIV was 2.3% (RSD) at 0.5 μg L−1. The procedure was validated by the analysis of the Chinese stream sediment CRMs GBW07303a and GBW07305a. The method was considered to have potential for the highly sensitive determination of vapour-forming elements. In a novel sensitive approach for the determination of Cl by SF-ICP-MS using PVG for sample introduction, methyl chloride was generated158 from different chlorine species in a flow-through photochemical reactor using a 1% solution of acetic acid containing 7.5 μg g−1 Cu2+. The volatile product was directed by an argon carrier gas to a gas–liquid separator and introduced into the instrument. The sensitivity obtained for a flow rate of 1.7 mL min−1 and a 45 s irradiation time was 74-fold better than that of conventional nebulisation. Although limited by blanks, the LOD of 0.5 ng g−1 was suitable for quantitation at trace levels. The procedure was validated by the analysis of the NIST SRMs 1568b (rice flour) and 1571 (orchard leaves) and by spike recovery from three brands of bottled water.
New separation techniques coupled with ICP-MS detection included159 an analytical procedure for 37Cl/35Cl analysis in perchlorate designed to be simpler than the method currently in use. Solutions containing less than 200 mg L−1 were preconcentrated by SPE and the perchlorate then separated from chloride by IC prior to analysis by MC-ICP-MS. The accuracy of the method was validated by analysis of international perchlorate CRMs USGS-37 and USGS-38; analytical precision was better than ±0.4‰. In the analysis of enriched microbial cultures from a contaminated soil, the isotope enrichment factor ε37Cl of −13.3 ± 1‰ was in the range reported previously for perchlorate biodegradation by pure microbial cultures. A method for the simultaneous detection of trace Zn dimethyldithiocarbamate and Zn ethylenebisdithiocarbamate by CE and ICP-MS involved160 chelation of the compounds with trans-1,2-diaminocyclohexane-N,N,N′,N′-tetraacetic acid to form a macromolecular complex. The two chelates were separated by α-cyclodextrin-modified CE at a separation voltage of 15 kV for 12 minutes. The LODs were 2 and 3 ng mL−1 (as Zn) for Zn dimethyldithiocarbamate and Zn ethylenebisdithiocarbamate, respectively. The two compounds spiked into cabbage leaves were successfully extracted by UAE and determined with a RSD (n = 5) of ≤6% and a recoveries of 95–107%. The careful separation, identification and characterisation of polymer-coated quantum dots in complex media such as soil solution is key for understanding their behaviour and to predicting accurately their fate in the environment. A synthesised CdSe/ZnS core/shell suspension, stable for at least six months, was characterised161 for dimension, structure and elemental composition. The element-specific data obtained by ICP-MS after asymmetric FFF separation enabled the signal to be deconvoluted reliably. It was possible to determine quantum dot populations which interacted with the soil solution and so would be susceptible to either aggregation or dissolution. Analytical performance was evaluated by the analysis of CRM TM-25.4, batch 0914 (Lake Ontario water). The LODs were 0.01, 0.05, and 0.03 μg L−1 for Cd, Se and Zn, respectively, when using ICP-MS detection.
Reports on the determination of isotopes with MC instruments included162 a new method developed for precise and accurate Mo isotope ratio measurements in plant materials. The Mo species in a 0.2 M HF–0.5 M HCl mixture were retained on AG® 1-X8 anion exchange resin while the matrix components were removed. The species were then eluted with dilute HNO3. Use of an Apex-Q desolvating nebuliser and the addition of 0.04% EDTA solution enhanced sensitivity 10–12-fold. The drift in instrumental mass bias was corrected by normalisation to the response for an Pd IS added online. This led to an improvement in measurement precision for the δX/95Mo values. The method was validated by the analysis of NIST SRM 1547 (peach leaves). The Mo isotope ratios could be determined in sample solutions with Mo concentrations as low as 10 ng g−1. Application of the method to the determination of natural variations in the isotopic composition of Mo in different anatomical parts of plants showed for the first time that Mo isotope fractionation occurred during long-distance transport of Mo in plants. The magnitude of Mo isotope fractionation during translocation of Mo was different for different plant species. The authors considered the Mo isotope ratio data obtained by MC-ICP-MS could be used as a sensitive probe of processes controlling transport and distribution of Mo in plants. Heroin chemical signatures provide important information to law enforcement agencies. In the first report on the use of 87Sr/86Sr analysis of heroin for the profiling of samples from specific geographic regions, DeBord et al.163 extracted Sr using MAE and, if necessary, preconcentrated it by SPE prior to analysis by MC-ICP-MS. South American and Mexican heroin samples are currently the most difficult to identify based on their organic compositions but were correctly classified 82 and 77% of the time, respectively, when Sr isotope ratios were used. In addition, a value for 87Sr/86Sr in the NIST SRM 1570a (spinach leaves) was reported for the first time. This was 0.70905 ± 0.00002 (95% confidence interval, n = 3).
Analyses with tandem MS instruments included164 the determination of 12 trace elements (As, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, V, and Zn) in the water decoction of MFH plants. A detailed discussion of potential interferences was presented and how they were overcome by reaction with oxygen. The percentage extractions, determined following MAD of the residue, were all ≤30%. The methods were validated by the analyses of NIST SRMs 1515 (apple leaves) and NIST 1547 (peach leaves). The LODs ranged from 0.1 (V) to 7 (Zn) μg kg−1. In the determination of the toxic elements As, Cd, Hg and Pb in 50 common MFH plants by the same research group, samples were also prepared165 by MAD with HNO3–H2O2. A 400 μg L−1 Au standard solution was added to all sample and standard solutions to eliminate a Hg memory effect, and Bi, Sc and Tb were used as internal standards. The results were validated by the analysis of NIST SRM 1515 (apple leaves) and by comparison of the results for three samples with those obtained by AAS. The LODs were 1, 1, 0.8 and 0.6 for As, Cd, Hg and Pb, respectively.
Radiocaesium has been widely dispersed across the Earth due to nuclear operations since the first atmospheric nuclear weapons test in 1945. The strong affinity of Cs for particulates makes it a powerful tracer for a range of environmental processes but one of the principal challenges in the TIMS determination of 135Cs/137Cs isotope ratios is the elimination of Ba as an isobaric interference. A combination of double AMP–PAN separation and Sr-spec resin column purification was developed167 to provide excellent separation of Cs from the alkali metals in a range of environmental sample types. An emission activator of glucose solution for the ionisation of Cs gave the optimal combination of a stable Cs+ beam, minimal low-temperature polyatomic interferences and improved ionisation efficiency. The mass-spectrometric determination of low abundance 135Cs and 137Cs was compromised by the presence of the high abundance 133Cs+ beam, which could be scattered and caused significant spectral interferences. In addition, minor polyatomic interferences needed to be taken into account when the 133Cs+ intensity was large relative to those of 135Cs+ and 137Cs+. The method was evaluated by the analysis of samples and standards from regions affected by fallout from the Chernobyl (IAEA-330) and Fukushima nuclear accidents and was applied to the measurement of extremely low concentrations (137Cs approximately 12 fg g−1) in estuarine sediments from SW England.
A new laser-SNMS system combined168 the high spatial and good mass resolution of a TOF-SIMS instrument with the element-selective process of resonant laser ionisation and so met all requirements for spatially resolved ultra-trace analysis of radionuclides. Low saturation laser powers of several mW confirmed an ideal overlap between the three laser foci and the sputtered neutral particle cloud. The apparatus delivered a decision threshold of 5 × 108 to 5 × 109 atoms. Further potential for optimisation of the technique was indicated by the estimated decision thresholds derived from efficiency measurements on U that were two orders of magnitude lower. Furthermore, it was believed that improvements in the overall efficiency would be possible by reducing the oxidised fraction through use of suitable reduction agents or carriers or by modifying the ion extraction. The first analytical measurements by the laser-SNMS system demonstrated that isobaric contaminations, whether from organic background or other interfering elemental or molecular species, could be efficiently suppressed. As a consequence, the SNMS system will be applied to the detection, visualisation and ultra-trace analysis of radionuclide-containing NPs in environmental samples with a strongly reduced or even no need for chemical preparation.
Guezenoc et al.171 demonstrated the importance of variable selection on the prediction ability of LIBS quantitative partial least squares (PLS) models. The spectral lines of K at 766.49 nm and 769.90 nm were considered in the framework of an agricultural soils analysis. Whereas univariate models had very poor correlations between the peak areas of the K lines and the related concentration values, a series of PLS models improved significantly the prediction ability. This gain was significant because the two spectral lines of K exhibited unusual profiles. A PLS model with only variables related to the edges of these lines had a significantly better predictive ability than another PLS model which included variables related to only the central parts of these lines. Other such studies included variable selection in the determination of Ba, Cr and Cu in soil,172 Cd and Pb in soil,173 Ba, Ca, Cr, Cu and Mg in soil,174 Al, Ca, Fe, K, Mg, and Na in soil175 and Al, C, Ca, Fe, Mg and Mn in Chinese tea leaves.176 It would appear that there is as yet no agreement about which is the best method.
Calibration of LIBS remains challenging because the methodology is highly matrix dependent. The different approaches taken to meet this challenge included the use of PLS regression for the determination of Cd in polluted fresh leafy vegetables,177 the use of matrix-matched calibration standards in the determination of Ca in biochar-based fertilisers made from peanut shells, eucalyptus residue and banana fibres178 and the use of nonlinear multivariate calibration in the determination of Mg in soil.179
Various reports on the measurement of Pb indicated the matrix-dependency of LIBS. In the determination of Pb in soils, best results were provided180 by the combination of a multivariate calibration model based on three principal components and the use of the 3rd harmonic of a Nd:YAG laser. The LOD was 13 ppm. The concentration (40 ppm) determined for a soil sample agreed with that obtained by XRFS analysis. Calibration based on the intensity of a single line at 405.78 nm gave181 acceptable results for the determination of Pb in soil contaminated with drilling fuel. The LIBS results for five samples were not statistically different from those obtained using ICP-AES. However, this method did not work182 well in the determination of Pb in tea leaves. Best results were obtained using calibration by a multiple linear regression based on the intensities of (a) the Pb line at 405.78 nm, (b) the C line at 247.856 nm and (c) the sum of the Mg lines at 403.076, 403.307 and 403.449 nm. It was assumed in this study that the concentrations of C and Mg were constant across all samples. In addition, the concentrations of Pb were high at 0.3 to 3%. For the determination of Cd, Cr, Cu and Pb in soils, Chinese researchers employed183 a portable instrument modified to contain the plasma and increase the intensity of the emitted light, which was collected by a multi-channel optical fibre. Calibration was by the standard additions method with non-linear curve-fitting. In a later study on the spatial distribution of Cr, Cu and Pb in soil, the same research group found184 that calibration based on single spectral line intensities worked only for Cu and Pb. Results for Cr were more accurate when a calibration-free method was combined with the Saha equation. The Cr/Si concentration ratios correlated well with the total concentrations. Rehan et al.185 compared this calibration-free method with both a calibration curve method using normalisation to the intensity of the K atom line at 404.72 nm and an integrated intensity ratio method for the determination of 15 elements in betel leaves. The normalised calibration curve method gave results in closest agreement with those obtained by ICP-AES. The LODs for Cu, Cu and Zn were 1, 4 and 6 ppm, respectively.
It is important to study the effects of various operating parameters on LIBS analysis. Peng et al.186 investigated the role of laser wavelength in the determination of Cr in rice leaves. Calibration based on standardised normal variate transformation gave results for a 532 nm laser better than those obtained with a 1064 nm laser. In another study they found187 that the emission line intensity was only higher for irradiation at 532 nm for elements such as C (atom line at 193.03 nm, 7.685 eV) and Si (atom line at 212.41 nm, 6.616 eV) with higher upper energy levels. The opposite was the case for elements such as Fe (atom line at 368.75 nm, 4.220 eV) and Ti (atom line at 336.12 nm, 3.716 eV) with lower upper energy levels. Kim et al.188 obtained a significant enhancement in emission intensities when argon was supplied to the soil sample surfaces. Other researchers investigated189 not only the role of the surrounding gas but also its pressure. To detect Cs in soils from the vicinity of the Fukushima Nuclear Power Station, measurements were made with irradiation at 355 nm (Nd-YAG laser) and with nitrogen and helium as ambient gases at both atmospheric and low pressures. The emission spectra with 0.5 kPa nitrogen gave the best signal and an LOD of 0.3 ppm.
Chinese researchers investigated the effects of long pulse lengths. In one study, 500 μs pulses produced190 a “quasi-stable” plasma in local thermal equilibrium. The peak intensities for Cr and Pb occurred at about 320–350 μs. The plasma temperature was calculated to be 6612 K and the LODs for the analysis of soils were 40 and 35 ppm for Cr and Pb, respectively. In a second study, the pulse duration of 80 μs produced191 a plasma temperature of 15800 K and gave an improved LOD of 16 ppm for the determination of Cr in soil.
Improved detection capabilities can also be obtained with double pulses. To map the spatial distribution of Ag NPs, Ag and Cu in root tissues of Vicia faba, the initial plasma produced by a 266 nm Nd:YAG laser was reheated192 by radiation from a second Nd:YAG laser operating in the fundamental mode of 1064 nm. The LODs were 3, 18 and 4 pg for the Ag NPs, Ag and Cu, respectively. Co-linear and orthogonal beam-convergence double-pulse configurations were compared193 for the determination of Cl in plant materials. Suppression of interfering molecular bands in the latter configuration led to better sensitivity even though the S/N was slightly poorer.
Khumaeni et al. reported on two metal-assisted procedures for the analysis of soils. In the first,194 the soil was mixed with a coarse metal powder and irradiated with light from a CO2 laser at 10.6 μm. The LODs for Cr, Hg and Pb in loam were 0.8, 0.7 and 15 mg kg−1, respectively. In the second procedure,195 the powdered sample was packed into a cylindrical hole in an acrylic holder and covered by a stainless steel mesh (0.1 mm wires with 0.4 mm spaces). Using the same CO2 laser system, the S/N was better than that obtained with direct irradiation of a pressed soil pellet. The LODs for Cu and Hg were 3 and 10 mg kg−1, respectively.
The application of portable LIBS is becoming more common. Suspended particulate matter mobilised in a storm in an urban river was analysed196in situ by a handheld instrument in order to characterise the environmental impact of potentially hazardous elements. The spectra, analysed by PCA following outlier identification, made it possible to differentiate between monitoring sites. In addition, some of the discriminant variables allowed PTEs such as Cr, Cu, Ni, and Pb to be determined and their sources to be identified. The performance of a hand-held LIBS system was compared197 with that of a laboratory-based instrument for the determination of Cu, Pb and Zn in soils. Both emission intensity and stability were significantly lower for the handheld device, but IS calibration improved the stability of data to 6% and, for heavily contaminated soils, the measurement errors were <12%. The LODs of the laboratory system were <10 mg kg−1, though some sample preparation was needed to decrease the effects of moisture and soil porosity.
One of several reports dealing primarily with applications of LIBS was198 on the performance of the ChemCam instrument mounted on the Curiosity Rover for the determination of H on the surface of Mars. The emission intensity signal at 656.6 nm was measured. To improve discrimination between various mineral phases in the Martian soil, 198 naturally occurring hydrous samples covering a broad range of bulk compositions were used to create more robust prediction models based on the data acquired under Mars conditions. All normalisation and data processing methods for quantifying H resulted in models with statistically indistinguishable prediction errors (accuracies) of ±1.5% (w/w) so application was limited to discrimination between phases with large differences in hydration such as chlorites (12%), smectites (15–20%) and gypsum (20%). The determination of Al, Ca, Fe, K, Mg, Mn, Na and Ti in 32 surface sediments collected from Cienfuegos Bay, Cuba, in 2011–2013 made199 it possible to correlate a small number of anomalous results with external events. The signals for Ca, K and Mg in citrus plants correlated200 with damage caused by the bacterial disease huanglongbing. A simple calibration method sufficed201 for quantifying Cu in tobacco leaves with an LOD of 8 μg g−1. Multiple-linear regression was needed202 for calibration in the determination of Cu and Pb in the traditional medicine Ligusticum wallichii with LODs of 6 and 16 μg g−1, respectively. To characterise matcha and green tea powder, the LIBS spectra between 230 and 880 nm were analysed.203 Linear discriminant analysis based on four characteristic wavelengths (C I 247.94 nm; Ca II 393.45 nm; Fe II 766.68 nm; Mg II 279.60 nm) gave rapid identification of matcha produced by different manufacturers and of green tea powder made by different fixation methods. The LIBS results for Al, Ca, Cu, K, Mg and Mn in the leaf ionome of the black cottonwood poplar (Populus trichocarpa) were204 in agreement with those obtained by NAA but that for Cl was not. It was concluded that LIBS could be used in large-scale field phenotyping projects as a fast, high-spatial-resolution technique to quantify relevant elements.
The performance of silicone-coated silicon reflectors was superior206 to that of reflectors coated with poly(vinyl alcohol) for the TXRFS analysis of plant materials. Digests (HNO3–H2O2) of Mentha longifolia L. leaves collected from different cities in Saudi Arabia were applied to the reflectors and 15 elements quantified. A HNO3–H2O2 digestion was also used207 in the analysis of some teas and herbs. After mixing with Ga IS, 10 μL of the digest was transferred to a quartz glass reflector and evaporated to dryness on a hot plate. Thirteen elements were determined with LODs of 0.1–3 mg kg−1. A new method of direct analysis of tree leaves was evaluated208 as an approach to air quality monitoring. The leaves were prepared by a SMART STORE device that first compressed a leaf between two 75 μm thick organic foils, one of which was adhesive and metal free, and then cut out a 30 mm diameter disc. The disc was stuck to a reflector and inserted into the TXRF instrument. Results were compared with those obtained by microwave-assisted digestion and it was concluded that the non-destructive direct method allowed rapid screening of the leaf samples with acceptable LODs of 0.5–18 mg kg−1.
The micro-PIXE determination of Ca, Fe and Zn in genetically modified and wild-type sorghum grains was compared209 with ICP-AES analysis. Whereas no difference between the two types of grain was found from the ICP-AES analysis, the spatially resolved micro-PIXE data showed significantly lower levels of Zn in the bran layer of the transgenic grain than in the wild-type. The mineral accumulation during different developmental stages of Blepharispermum subsessile, an endangered medicinal plant from India, was followed210 by PIXE analysis. The results allowed researchers to formulate a medium to induce high-frequency regeneration of the plant species for its ex situ conservation.
In a paper in the Czech language with an English abstract, a reliable, fast, simple and low-cost analytical procedure based on radionuclide XRFS was proposed211 to identify and determine PTEs in various antitussive preparations of synthetic and biological origin. Some materials (tablets, granules, plants) could be analysed directly without the need for matrix removal so errors in the analytical process were minimised. Other materials such as effervescent tablets and hard capsules were more problematic in that homogeneous incorporation of standards was not possible. In these cases, external matrices which gave similar background noise levels and had the same levels of PTEs concentrations were used to prepare well-defined tablets containing standard additions of PTEs for calibration. The LODs had low single-digit μg g−1 values. The procedure was considered suitable for routine use in QC laboratories.
A review (108 references) of the applications of X-ray elemental mapping techniques to elucidate the ecophysiology of hyperaccumulator plants covered212 sample preparation methods and gave tutorial explanations of the basis of each technique and comparisons with non-X-ray techniques. The reviewers included a useful section entitled “cautionary notes about preparation of plant material for elemental microanalysis”. They concluded that characterising the cellular and tissue-level distribution of elements is challenging and that accurate X-ray analysis requires careful sample collection, preparation and selection of analytical conditions to avoid elemental redistribution, chemical changes or ultrastructural modification. The LODs of a new μXRFS instrument for in vivo elemental analysis in plants ranged213 from 0.04 (Fe) to 0.20 ng mm−2 (Pb) and so were comparable to those obtained with second-generation synchrotron facilities using a freestanding film reference. The spot size was <14 μm for the Rh K alpha line (20.214 keV). Relative sensitivities were determined for elements from S to Pb using the freestanding film reference and a 13-element standard dried-droplet deposition. A 3D-printed sample holder was used for in vivo measurements. An instrument with an ellipsoidal capillary was applied214 to the 2D mapping of Ca, Cu, Fe and K in a holly leaf. The theoretical calculations behind the design of the capillary, which produced a 176 μm spot size at a focal distance of 40 mm, were fully described. The S/N obtained was much superior to that obtained with a 200 μm pinhole for which no signal was observed. A new μXRF spectrometer with polycapillary microfocus X-ray beam excitation with a rhodium anode (50 kV, 1 mA) and a SDD was used215 to study both the location of Ca, Cu, Fe, K, Mn, Pb, and Zn in germinating maize seeds and the mobilisation of Pb in rock biofilms. Other applications included a study of the effects of two Zn fertilisers on the leaf tissues of green beans (Phaseolus vulgaris L.),216 a study of the Se toxicity and distribution of Ca, Cu, K, Mn and Na in cowpea plants,217 the characterisation of small Fe–Mn nodules in red clay soil218 and a study of Fe and Zn distributions in wheat grown in untreated and Zn-enriched soil.219
In a review (49 references) on the use of pXRFS for the characterisation of tropical soils, attention was drawn220 to the various factors that can influence results such as soil moisture content, particle size distributions, spectral interferences, scanning through plastic bags used for sampling and the scanned area and penetration depth of X-rays. Some of these factors were further evaluated221 in a follow-up study from the same research group. Whereas results for Fe2O3 and TiO2 correlated with those obtained following H2SO4 digestion, those for SiO2 and Al2O3 did not. It was considered that pXRFS had potential for use in some studies of tropical soils as it was faster, more economical and “greener” than traditional H2SO4 digestion. Although factors affecting pXRFS analysis such as object geometry, soil moisture content and soil heterogeneity are well documented, the effect of organic matter is poorly understood. Ravansari et al.222 evaluated their hypothesis that pXRF concentration measurements would be attenuated in proportion to the fraction of organic matter present by conducting a series of laboratory experiments using an organic-free soil with known concentrations of 13 elements. Twenty incremental additions were made for each of three organic matter surrogates (cellulose, graphite powder, and confectioner’s sugar) and concentrations of 13 elements measured as a function of the organic matter fractions. Plots of measured trace metal concentrations against organic matter fraction were generated for each organic matter surrogate and compared with theoretical response curves based on dilution of the soil with organic matter. Empirical correction functions subsequently developed to adjust pXRFS data for each of the 13 elements investigated were evaluated using four RMs with known organic matter content. The different response for each element highlighted the element-dependency of pXRFS analysis.
Although the pXRFS determination of contaminants in soils has the advantage of being rapid and convenient, problems of calibration need to be addressed. Soil pollution by metals and metalloids has rapidly become a major threat to food security with nearly all cultivated soils being at risk of metal accumulation. Measurements made by pXRFS under in situ, ex situ and sieved conditions were compared223 with data obtained by ICP-MS analysis of digested soils. Whereas the results for Cu, Mn and Zn were closely correlated, there were no significant correlations for As, Ni and Pb. The uncertainties in the pXRFS measurements arising from soil heterogeneity, moisture and particle size accounted for 20% of the total variability. It was found that greenhouse soils could be contaminated by Cu and Zn attributed to anthropogenic activities. Analysis using pXRFS was considered to be promising as a rapid and nondestructive in situ technique for assessing the potential risks of PTEs at trace concentrations in greenhouse soils. As pXRFS instruments can investigate a considerably greater number of sampling points more rapidly than alternative methodologies, they are useful for rapid planning in a soil survey process. In the characterisation of metal contamination in soils from near an abandoned iron mine in South Korea, hot spot analysis and a proposed group classification method were employed224 to map Cu contamination hot spots in a given study area and to support the development of soil sampling plans. The pXRFS data were validated by ICP-AES analyses. The relatively low accuracy of pXRFS data was a potential source of error so the hot spot analysis and group classification method were applied to each soil sampling phase, not only to identify the hot spot areas but also to identify results suspected to be in error.
Few studies have previously used pXRFS to evaluate metal and metalloid concentrations in vegetation which are of critical importance for establishing the toxicity or deficiency of essential elements in plants or for investigating harmful elements extracted from the soil. A pXRF spectrometer was used225 to scan 228 organic material samples (thatch, deciduous leaves, grasses, tree bark, and herbaceous plants) from smelter-impacted areas of Romania to demonstrate the application of pXRFS to the determination of element concentrations in vegetation. Samples were analysed as received from the field (moist), after drying at 70 °C, or after drying and milling to <2 mm size. After calibration with the reference 316 calibration alloy, the NIST SRMs 1515 (apple leaves) and 1547 (peach leaves) were analysed and correction factors for Ca, Fe, K, Mn, Pb, Sr and Zn calculated. Although the best agreement for Cd, Cu, Fe and Pb was obtained for the dried and ground samples, even under field moist conditions pXRFS could reasonably be used for the determination of Zn. This approach was considered useful as a quick assessment of element concentrations in vegetation.
The application of pXRFS in cultural heritage studies (works of art and archaeological artefacts) is considered5 in full in our companion XRFS ASU.
5. Analysis of geological materials
5.1 Reference materials and data quality
Well-characterised RMs are crucial in geochemical analysis, both for method development and in order to obtain precise and accurate results for unknown samples, since they can serve as calibrants and QC materials. This section focuses on newly developed RMs and the further characterisation of established RMs to provide additional information on their elemental and isotopic compositions.Renewed interest in REE mass fractions in coal and its by-products prompted a study226 to evaluate the consensus values for current and historical coal RMs. Two RMs, NIST SRM 1632a (historical bituminous coal) and USGS SBC-1 (Pennsylvania shale), were analysed by ICP-MS after lithium metaborate digestion. A major finding was that the published Tm concentration in NIST SRM 1632a (0.39 mg kg−1) could not be reproduced using modern analytical techniques and was likely to be too high. A revised value of 0.16 mg kg−1 Tm was recommended.
Reference material data sheets typically provide information on the uncertainty associated with each elemental reference value, together with a minimum recommended mass of material to be taken for chemical analysis. However, Rostron and Ramsey227 demonstrated that this minimum recommended mass of material may not be appropriate if beam techniques such as pXRFS are employed, because of the elemental heterogeneity of the small test portion masses sampled. When three IAG silicate RMs SdAR L2, M2, and H1 (metalliferous sediments) were analysed by pXRFS, the test portion masses were mainly significantly below the recommended minimum mass of 0.2 g at beam sizes of 8 and 3 mm. Elements with the highest uncertainty due to heterogeneity appeared to be those associated with either an accessory mineral (e.g. Zr in zircon) or low test portion mass (associated with low Z). Revised uncertainties on the reference values of these RMs were calculated using pXRFS data.
Knowledge of the heterogeneity of geological RMs is essential in the assessment of measurements by microanalytical techniques. Thus, the homogeneity of RM AuRM2, originally developed and certified for the bulk analysis of refined gold, was evaluated228 for its suitability as a RM for the microanalysis of native gold by LA-ICP-MS. Elements considered to be homogeneous in AuRM2 on the basis of a measurement repeatability of <10% RSD included Al, Cu, Fe, Mg, Ni, Pb, Pt, Rh, Sb, Se, Sn, Ti and Zn for a 3.4 μg test portion mass; other elements (As, Bi, Mn, Pd and Te) had repeatabilities of between 10 and 20% RSD. These heterogeneities were considered to be negligible compared with the range of chemical signatures observed in native gold. Thus RM AuRM2 was shown to be sufficiently homogenous for its intended purpose. A new synthetic chalcogenic glass was proposed229 as a RM for the determination of trace elements in base metal sulfides by LA-ICP-MS. Approximately 10 g of homogenous glass doped with relatively high concentrations (∼100 μg g−1) of PGEs, chalcogens (As, Bi, Sb, Se and Te) and transition metals was produced by melting a mixture of powders in a sealed silica ampoule at close to 1000 °C and then quenching over a total period of 30 h. The trace element homogeneity of the resulting glass material was verified by LA-ICP-MS and the absolute concentrations determined by solution ICP-MS and LA-ICP-MS. Although most of the doped elements were homogeneously distributed at 100 μg g−1, the concentrations of Mo, Re and W (∼10 μg g−1) had to be lower to prevent the formation of phases that would concentrate these elements and so make their distribution heterogeneous. Weber et al.230 investigated the micro-homogeneity of Sr isotopes in several carbonate and phosphate RMs to evaluate their suitability for microanalytical analysis. All materials were analysed by LA-MC-ICP-MS using two different LA systems (ns and fs lasers). Strontium isotope data for the same RMs obtained by solution MC-ICP-MS after column chemistry agreed within analytical uncertainty with data in the literature. A comparison with solution data showed that both LA systems provided reliable 87Sr/86Sr as well as 84Sr/86Sr ratios for the RMs with low REE contents (JCt-1, JCp-1 and MACS-3). The accuracy of these data, together with their homogeneity on a μg-scale, made them suitable for use as matrix-matched microanalytical RMs. However, RMs with a significant REE mass fraction (USGS MACS-1, MAPS-4 and MAPS-5) yielded biased Sr ratios, indicating that some REE interferences may compromise these data. The polycrystalline, hydrothermal Diamantina monazite (Espinhaço Range, SE Brazil), notable for its low Th and U content, was investigated231 as a potential RM for in situ matrix-matched U–Pb geochronology and Sm–Nd isotope geochemistry. Previous work had indicated that differences between the Th compositions of monazite RMs (usually containing high amounts of Th) and those of samples introduced bias to the data obtained. The analytical data sets reported indicated that crystal fragments of Diamantina monazite were homogeneous with respect to both their major and trace element content and their isotopic composition (U–Pb, Sm–Nd and O); BSE images and compositional maps showed no evidence of crystallographic zoning.
Santos et al.232 described a new potential zircon reference material named BB zircon from Sri Lanka for use in LA-ICP-MS U–Pb geochronology and Hf isotope geochemistry. They investigated 20 megacrysts by ID-TIMS, LA-ICP-MS and SIMS. The BB zircon was considered to be a suitable RM for both U–Pb and Lu–Hf isotope systems as long as analyses targeted pristine cathode-luminescence-dark grain domains. The within-grain REE composition appeared to be homogeneous but this needed to be confirmed by further analyses before the zircon could be considered also as a RM for in situ REE determinations. Results from SIMS analysis on the within- and between-grain homogeneity were promising enough to consider BB zircon as a possible calibrant for δ18O determinations by SIMS. Zircon OG1/OGC from the Owens Gully Diorite of the Pilbara Craton was found233 to be an ideal QC material for Hf isotope determinations in ancient zircons by LA-MC-ICP-MS. In addition, these zircons were considered to be particularly suitable for LA techniques such as concurrent Pb–Hf isotope determination or LASS in which U–Pb age and Hf isotope data are obtained during a single ablation. Another study presented234 (U–Th)/He data on the Penglai zircon which occurs as megacrysts in basalts in northern Hainan Province, China. A weighted mean age of 4.06 ± 0.35 Ma (2SD) was derived from measurements on 18 fragments of four zircon megacrysts using single-crystal laser fusion He determinations and an U–Th ID method. The Penglai zircon was considered a suitable RM for (U–Th)/He isotope geochronology on the grounds of the unlimited quantity of material, large grain size, mostly weak broad zoning, rapid cooling and homogenous (U–Th)/He ages. The trace element homogeneity of the zircon GJ-1 was evaluated235 from the micro- to atomic-scale using cathode luminescence imaging along with quantitative crystallographic orientation, LA-ICP-MS and atom probe tomography analyses. The results showed that GJ-1 was structurally and chemically homogeneous at all scales and had no mineral inclusions. Significant abundances of Hf, P, Y, Yb and U indicated that the gem quality GJ-1 reference zircon, commonly used for U–Th–Pb and Lu–Hf-isotopic studies, would also be suitable for microanalytical trace element analysis. To improve the accuracy of Ti-in-zircon determinations for temperature estimates of crystallisation conditions, a new suite of zircon RMs for Ti was presented.236 It was based on two well-characterised zircons (zircons 91500 and GZ7) for which LA-ICP-MS and SIMS data revealed sufficient homogeneity with respect to Ti/Si for them to be used as primary calibration materials. Recommended Ti concentrations based on ID-ICP-MS measurements were derived for these zircons. In addition, 16 other zircon RMs were characterised with Ti concentrations anchored to the new ID values established in this study, thereby enabling comparisons of Ti-in-zircon datasets whenever data for these secondary RMs are reported.
Efforts to determine isotope ratio data for well-known international geological RMs continued. He et al.237 reported stable Ca isotopic compositions for 10 geological RMs relative to NIST SRM 915a (calcium carbonate) and its replacement NIST SRM 915b, ranging in composition from peridotite to sandstone. By applying a 43Ca–48Ca double-spike technique, δ44Ca/42Ca and ε40Ca/44Ca were measured simultaneously by TIMS with a precision of ±0.03‰ and ±1.0, respectively (n ≥ 8). The comprehensive compilation of Ca isotopic compositions provided for these geological RMs should enhance future interlaboratory comparisons of Ca isotopic measurements. In a similar study,238 high-precision Ca isotope ratios of 35 geological RMs of varying compositions, including MPI-DING glasses and RMs supplied by GSJ and USGS, were determined by TIMS using a 42Ca–43Ca double spike protocol. It was observed that the δ44Ca/40Ca values of ultramafic rocks were positively correlated with their MgO contents and negatively correlated with their CaO contents. This was interpreted as indicating possible Ca isotopic fractionation during partial melting of mantle peridotites. An interlaboratory comparison between five laboratories yielded109δ25Mg and δ26Mg values for eight widely available RMs, including solids (limestone, soil, dolomite, vegetation) and waters (river, spring water and brine), representative of a wide range of Earth-surface materials from low-temperature environments. The data from the different laboratories agreed well despite differences in sample preparation, sample size, instrumentation and measurement procedures. Recommended δ25Mg and δ26Mg values proposed for these RMs were based on the results of this study and published values. The use of these RMs as QC materials was advocated given the small interlaboratory variability for all the RMs (≤0.17‰ for δ26Mg) and the fact that they cover a wide range of sample matrices. Zhang et al.239 evaluated the homogeneity of geological RM USGS BIR-1a (basalt) with respect to its Re–Os isotope composition. Over an 18 month period, 63 measurements were made using Carius tube digestion with and without HF desilicification prior to measurement by NTIMS and ICP-MS. From the results obtained for Re (0.691 ± 0.022 ng g−1, 2SD) and Os (0.343 ± 0.089 ng g−1, 2SD) mass fractions and 187Os/188Os isotope ratio (0.13371 ± 0.00092, 2SD) at the 0.2–1.0 g test portion level, the authors concluded that BIR-1a was more homogeneous than previously thought. Consequently, they recommended it as a suitable RM for method validation, QC and interlaboratory comparisons of Re–Os isotopic measurements of mafic geological samples when using test portions >0.4 g.
The Institute of Geology, Chinese Academy of Geological Sciences, Beijing produced240 and certified GSB 04-3258-2015, a new isotope reference solution for the calibration of 143Nd/144Nd isotope ratio measurements. A certified reference value of 0.512438 (combined expanded (k = 2) uncertainty of 5 × 10−6) was obtained from an interlaboratory comparison of data from 11 participating laboratories using MC-TIMS or MC-ICP-MS. A new Japanese Pb isotope standard solution with natural isotopic abundances, NMIJ CRM 3681, was characterised241 using EDTA titrimetry as the primary measurement method. The 208Pb/204Pb ratio in the 208Pb-204Pb double spike, used for the correction of mass discrimination in MC-ICP-MS measurement of the Pb ratios, was determined with an uncertainty of <0.006%. Consequently, the Pb isotope ratios of this new solution not only have combined standard uncertainties smaller than those in NIST SRM 981 (common Pb isotopic standard) but are also SI traceable.
Two new mica reference materials, USGS57 (biotite) and USGS58 (muscovite), were developed242 to replace the NBS 30 biotite RM for δ2H measurements, as supplies of this previously widely used material are now exhausted. Their δ2H values were determined in six laboratories using either glassy carbon-based or chromium-based online conversion to H2 prior to measurement. The results were confirmed by off-line conversion. These isotopically homogenous, hydrous mineral RMs cover typical ranges of δ2H that occur in materials of crustal and mantle origin, and so enable users to construct a two point calibration. Five new biotite RMs (UNIL_B1 to B5) were calibrated243 for 18O/16O measurements by SIMS against NBS-28 (quartz). Their δ18O compositions were expressed relative to VSMOW. Repeated SIMS analyses on <20 μm spots had a measurement repeatability of 0.3‰ (2SD). Different crystallographic orientations had no effect on the results.
5.2 Sample preparation, dissolution, separation and preconcentration
Digestion methods for challenging geological sample matrices have been improved by different approaches. A simplified method for the determination of ferrous Fe mass fraction in silicate rocks included244 a Schlenk line to expel air to prevent oxidation during sample digestion and titration with KMnO4. Closed-vessel acid digestion under an argon atmosphere proved to be a cost-effective method for the digestion of 21 geological RMs with a range of compositions. This environmentally friendly procedure gave only moderately accurate but highly precise data. The reported LOD of 0.35% for FeO was based on 3SD of seven measurements of the procedural blank. Bokhari and Meisel245 optimised the sodium peroxide sintering procedure for the complete digestion of rock samples containing refractory minerals such as zircon and chromite prior to analysis by ICP-MS. For zircon- and chromite-rich geological materials, the optimum ratio of sample to Na2O2 was 1:6. Recommended decomposition times for samples with and without refractory phases were 120 and 30–45 minutes, respectively. Mathur et al.246 compared two published dissolution procedures for the determination of Sn isotopes in cassiterite by MC-ICP-MS: (i) reduction to Sn metal with KCN at 800 °C followed by dissolution in HCl; and (ii) reduction to a Sn solution with HI at 100 °C. The KCN method consistently gave yields of >70% Sn and appeared not to fractionate the Sn isotopes over a 2 h period. In contrast, the yield for the HI method was <5% due to Sn volatilisation and a large degree of fractionation of the Sn isotopes occurred. Unsurprisingly, this methodology was not recommended.
O’Hara et al.247 evaluated reaction conditions for the decomposition of a range of solid inorganic matrices (glass, quartz, zircon, soil and pitchblende ore) using molten ammonium bifluoride (ABF) in sealed fluoropolymer vials. Use of the optimum ABF:sample mass ratio, which was dependent on the sample matrix, decomposed the sample (except for zircon) in just 1 h at 230 °C. Recoveries for 19 elements were assessed by the ICP-AES and ICP-MS analyses of digests of NIST soil SRMs 2709, 2710 and 2711 and three IAEA pitchblende RMs. Sample decomposition was effectively complete and loss through volatilisation did not occur. It was noted that the analytical precision for the NIST soils may have been degraded as only 50 mg was taken while the certified minimum test portion is 250 mg. Although this method was designed to provide a solution for ICP-AES and ICP-MS analyses, it would also be possible to prepare the sample as a solid for analysis by techniques such as LA-ICP-MS, LIBS, and XRFS. Further work by Grate et al.248 demonstrated the feasibility of preparing a solid suitable for LA-ICP-MS analyses from the product of the reaction between a low grade uranium ore and molten ABF in sealed PFA fluoropolymer vials at 230 °C. Single-element standard solution spikes included in the molten salt reaction were evenly distributed in the pressed powder pellets prepared from the new solids. The inclusion of a 235U spike improved the precision of the 238U determinations dramatically from 17% to 0.1%. The incorporation of internal standards in this manner opened up the possibility of using analytical methodologies such as standard addition and ID and the preparation of matrix-matched solid RMs for LA-based methods.
In spite of the improved detection capabilities of modern ICP-MS instruments, chemical separation and preconcentration are sometimes required to determine elements present at very low concentrations. A procedure to determine 37 minor and trace elements mass fractions in ultramafic rocks involved249 addition of Tm, HF–HClO4 digestion and coprecipitation with Mg(OH)2 and Fe(OH)3. Analysis of four ultramafic RMs demonstrated that the HR-ICP-MS analysis of a simple dilution of the acid digest produced accurate data for elements down to mass fractions of 0.1 μg g−1. The coprecipitation method extended the detection capabilities and provided reliable data for REEs, HFSEs, Pb, Th, U and some of the transition elements (Sc, Ti and V). Elements such as Ba, Co, Cr, Cs, Cu, Ga, Li, Mn, Ni, Rb, Sr and Zn were not fully coprecipitated and so could not be determined by this method. Data from two separate analyses (the coprecipitation method and direct digestion of the sample) provided a comprehensive dataset for trace elements at very low concentrations. A separation procedure for the determination of Ge in environmental samples was adapted112 from one developed for Si isotopes because of the similar chemical properties of these two elements. After sample digestion with a solid NaOH flux, the solutions were purified by cation-exchange column chemistry before measurement of 74Ge by ICP-MS. Method accuracy was difficult to evaluate because of the paucity of certified Ge data but was estimated to be ca. 5% with a precision of 3%. The procedure had the advantage that Si isotopes and Ge mass fractions could be measured, with care, using the same separation procedure. This would be a great asset in the interpretation of the Si cycle in which these elements are complementary tracers.
Many examples of studies devoted to improving the efficiency of separation and purification techniques prior to isotope ratio measurements are given in Table 6. A fully automated, low-pressure chromatographic system enabled250 the unattended processing of one sediment digest every 2 h for the separation of Nd, Pb and Sr in a single-stage extraction step using DGA resin columns. The procedure was validated using Chinese RM GBW-07313 (marine sediment) as a matrix-matched CRM as well as samples spiked with NIST SRMs 987 (Pb isotopes) and 981 (Sr isotopes) and GSJ JNdi-1 (Nd isotopes). Recoveries of >90% were achieved for all elements together with low procedural blanks (0.57, 0.03 and 0.07 μg L−1 for Nd, Pb and Sr, respectively). The DGA resin could be used for the separation of more than 10 sediment digests without significant carry-over or memory effects. However, the overlap of Pb and Sr elution peaks limited the yields for both elements. Yuan et al.251 increased the speed of elution from ion-exchange columns by pressurising the columns with high-purity N2. For the separation of Cu isotopes on AGMP-1 resin, the time was reduced from 12 to 4.5 h using a flow of 20 mL min−1 N2 while still achieving a recovery of >99.9%. An effective separation of Nd from Sr was also achieved within 10 h for the USGS RM AGV-2 (andesite). The isotope ratios obtained by MC-ICP-MS were within ±2SD of the reference values.
| Analyte | Matrix | Sample treatment | Technique | Analysis and figures of merit | Ref. |
|---|---|---|---|---|---|
| Ag | Geological RMs, Ag ores | Digestion with HF–HNO3 in Teflon beakers on a hot plate, further steps involving HNO3 and HCl, final solution in 0.5 M HCl. Two-column purification procedure involving anion-exchange resin AG1-X8 and cation exchange resin AG50W-X8. Mean Ag recovery obtained was 96.5 ± 2.5% (2SD, n ≥ 5) | MC-ICP-MS | Mass discrimination and instrument drift corrected by a combination of internal normalisation with Pd and SSB. Ag and Pd isotopes monitored using 6 FCs equipped with 1011 Ω resistors. External reproducibility for 109Ag/107Ag varied between ±0.006 and ±0.009‰ (n ≥ 10) | 385 |
| B | Rock RMs | Low temperature sample dissolution in mixture of HF, HNO3 and 2% mannitol. Subsequent steps included refluxing with HCl at 65 °C overnight. Two-step chromatography: (i) Bio-Rad AG 50W-X8 to separate B and Li from silicate matrix; (ii) B purification on IRA 743 ion-exchange resin, followed by modified B micro-sublimation technique. Recoveries were almost 100% and blanks <0.8 ng B | MC-ICP-MS | Sample introduction via dual quartz spray chamber with the commercial PFA-ST nebuliser. Concentrations of B adjusted to 10 ng mL−1, yielding a 11B intensity of 0.4–0.5 V. Mass bias correction performed by SSB. Long-term precisions for δ11B (5 ng of B) better than ±0.35‰ (2SD) | 386 |
| Ba, Sr | Rock RMs | Rock powders digested with HNO3–HF, refluxed with HCl, residue dissolved in aqua regia to oxidise all iron to Fe3+; final solution in 6M HCl. Purification of Ba and Sr from same aliquot using a three-step column chemistry with AG1-X8, Eichrom Sr resin and AG50W-X8. Procedural blanks 340 ± 70 pg Ba and <30 ± 5 pg Sr | TIMS | Ba and Sr isotope data for 3 geological RMs by TIMS in multi-dynamic mode on 9 FCs. Samples split into two aliquots: Sr fraction loaded onto single Re filaments using TaCl5 as activator, Ba fraction loaded onto Re double filaments. External reproducibility (2SD) was 19 ppm for 84Sr/86Sr, 2 ppm for 87Sr/86Sr and 4 ppm for 135Ba/136Ba | 387 |
| Ce | Rock RMs | Digestion with HF–HNO3, with further steps involving HCl, boric acid and HNO3; final solution in 3 M HNO3. Two-step chromatographic procedure: (i) bulk REE separated on Eichrom TRU resin; (ii) Ce purified on Eichrom Ln columns using KBrO3 to oxidise Ce to Ce4+. A thin layer of Eichrom pre-filter resin on top of the Eichrom Ln resin prevented the Ln resin from floating when 10 M HNO3 was added to the column | TIMS | Multi-static acquisition routine, with 1011 Ω amplifiers connected to all FCs except the centre cup which had a 1010 Ω amplifier for collection of the large 140CeO ion beam. Calculation of Ce isotope ratios involved subtraction of the contribution from the 140CeO tail on the 136CeO and 138CeO signals by exponential interpolation. Simultaneous correction for mass fractionation and isobaric XCe18O–X+2Ce16O interferences was performed off-line using 136Ce/142Ce = 0.01688. Reproducibility was 20–40 ppm (2SD) | 388 |
| Cr | Geological RMs, rocks, soils, sediments | Silicate samples digested with HF–HNO3 on hotplate. After repeated heating and sonication, further steps involved treatment with aqua regia and final solution in 2 M HNO3 mixed with H2O2. The digestion procedure was modified for other matrices. Improved three-step purification scheme: (i) cation (AG50W-X8) and anion (AG1-X8) columns in series to eliminate Ca, Fe, Ti and V; (ii) anion-exchange on AG1-X8 to separate Cr from Ti and V; (iii) purification of Cr on AG1-X8. Recovery 80–99% and blank <1 ng Cr | MC-ICP-MS | Measurements performed in medium or high-resolution mode on 7 FCs equipped with 1011 Ω amplifiers in static mode. Interferences of 50Ti and 50V on 50Cr, and 54Fe on 54Cr were monitored via49Ti, 51V, and 56Fe. A 50Cr–54Cr double spike was used to correct for isotope fractionation from chemical separation and instrumental mass bias. Long-term precision was 0.06‰ (2SD) | 389 |
| Fe | Rock RMs | Rock powders digested with HF–HNO3 on hotplate. After repeated heating and sonication with HNO3 and then 8 M HCl, final solution prepared in 8.2 M HCl mixed with 0.01% v/v H2O2. Recoveries >99%, procedural blank <7 ng Fe. Chemical separation by ion-exchange chromatography on AG MP-1M | MC-ICP-MS | All 16 FCs fitted with 1011 Ω resistors. 61Ni–60N double spike method of correcting for mass fractionation preferred to SSB, using high mass resolution to eliminate polyatomic interferences. Precisions of <0.04‰ (2SD) for δ56Fe and <0.06‰ (2SD) for δ57Fe were obtained for IRMM-014 (iron wire) | 390 |
| Ir | Standard solutions | Standard solutions sourced commercially or prepared from Ir bromide and metallic Ir powder. All solutions spiked with Tl (NIST SRM 997) and Re (NIST SRM 989) isotopic standards | MC-ICP-MS | Isotope measurements on FCs in static mode. State-of-the-art regression model used to correct for mass bias. New NRCC Ir CRM IRIS-1 proposed as delta zero standard for Ir isotope ratio measurements | 391 |
| K | Geological RMs, rocks, silicate minerals, evaporites | Procedure was applied to wide range of sample types, including silicate and evaporate minerals, seawater, and hair and plant RMs. Wide range of digestion procedures employed depending on matrix. Final solution in 0.2% HNO3. Separation of K from other matrix cations using a Dionex IC system fitted with a CS-16 cation-exchange column and methanesulfonic acid as the eluent | MC-ICP-MS | Instrument run in high resolution mode and cold plasma conditions (500–600 W) to suppress Ar ionisation. Instrumental mass bias correction via SSB with NIST SRM 999b (KCl). Total range of 41K/39K ratios in the materials analysed was ca. 2.6‰; long-term external reproducibility was 0.17‰ (2σ, N = 108) | 392 |
| Li | Rock RMs | Li fraction collected from the first column further purified by IC on AG 50W-X8 cation-exchange resin. Yields of Li were almost 100% and blanks <5 pg Li | MC-ICP-MS | Sample introduction via desolvation system. Mass bias correction performed by SSB. Long-term precision for δ7Li (1 ng of Li) better than ±0.27‰ (2SD) | 386 |
| Li, Mg | Seawater, river water, sediment, foraminifera and rock RMs | Wide range of digestion procedures employed depending on matrix. Final solutions in 0.7 M HCl. Ion-exchange chromatography on AGMP-50 for quantitative separation of Li and Mg from all matrices in a single-step elution with HCl. The Li and Mg cuts were dried down, refluxed with HNO3, dried down and then taken up in HNO3 for analysis | MC-ICP-MS | Different sample introduction systems used for Li and Mg isotope ratio measurements. A concentration-matched SSB technique was applied to correct for instrumental drift and mass bias using NIST SRMs L-SVEC and DSM-3 for Li and Mg, respectively. Long-term precisions (2σ) were ±0.39 and ±0.07‰ for δ7Li and δ26Mg, respectively | 393 |
| Mg, Ti | Ca–Al-rich inclusions in meteorites, rock RMs, olivine minerals | Silicate samples digested with HF–HNO3; further steps involved treatment with aqua regia and final solution in 7 M HNO3. Series of discrete IC steps with novel sample pre-treatment procedures developed for the separation of very small amounts of Mg (∼5 μg) and Ti (0.7–1 μg) from samples containing high amounts of Al, Ca, Fe and Mg. Recoveries >99.9% for Mg and >97% for Ti | MC-ICP-MS | Instrument run in high-resolution mode and SSB used to correct for instrumental mass bias. Procedures consume significantly less material than previous methods and represented a 6 to 10-fold improvement in precision. Mass-dependent compositions determined with a precision better than 60 ppm amu−1 for Mg and Ti | 394 |
| Nd | Rock RMs | Samples digested with HF–HNO3 on a hotplate. Final solution in 3M HNO3 loaded onto TODGA resin column. Nd eluted with 1.2 M HCl. Blanks of 30–70 pg Nd were negligible relative to Nd content of samples | MC-ICP-MS | Nine FCs employed; 140Ce monitored to correct interference of 142Ce on 142Nd, and 147Sm for interference correction of 144Nd, 148Nd and 150Nd. Internal precisions were ca. 15 × 10−6 for 142Nd/144Nd, 2 × 10−6 for 145Nd/144Nd, 3 × 10−6 for 148Nd/144Nd and 4 × 10−6 for 150Nd/144Nd | 395 |
| Nd | Basalt and carbonate RMs | After digestion, Nd was separated and purified on 3 columns: (i) TRU resin (Eichrom) to remove matrix elements; (ii) Ln resin (Eichrom) to separate Nd from other REEs; (iii) purification on Bio-Rad AG50W-X12. Recovery was 92% and blank 1 pg Nd | TIMS | New total evaporation normalisation TIMS technique applied. Signals monitored on 9 FC (1011 Ω resistors) in static multi-collection mode. Isotope fractionation corrected by internal normalisation using exponential law and 146Nd/144Nd = 0.7219. Samples as small as 0.5 ng Nd were analysed with precisions better than 100 ppm (2RSD) | 396 |
| Pd | Chondrites, meteorites, terrestrial rocks | A 106Pd–110Pd double-spike was added to sample powders in porcelain crucibles prior to digestion via NiS fire assay; NiS beads dissolved in HCl. Purification using AG1-X8 anion-exchange resin; Pd eluted in 11M HCl with a recovery of ca. 96% | MC-ICP-MS | Ion beams at 102Pd, 103Rh, 104Pd, 105Pd, 106Pd, 107Ag,108Pd, 109Ag, and 110Pd were simultaneously collected on FCs (1011 Ω resistors). Double spike correction for instrumental mass fractionation. External reproducibility of ±0.032‰ on δ106Pd demonstrated for the Allende chondrite | 397 |
| Pt | Fe meteorites | Samples digested with HNO3–HCl and refluxed on a hot plate for 48 h at 100 °C; final solution in HCl. A complex two stage separation scheme utilised anion-exchange resin AG1-X8. Firstly, Pt was separated from matrix elements including Fe and Ni; Pd also isolated at this stage. In the second stage, Ir was reduced with ascorbic acid and eluted from the column before Pt collection. Yields were typically 50–70%, with blanks of 0.1–0.3 ng Pt | MC-ICP-MS | Measurements in low-resolution mode with all Pt isotopes collected simultaneously, along with 188Os+ and 200Hg+ to correct for isobaric interferences, and 191Ir+ to assess tailing of Ir onto Pt isotopes. SBB was used for the correction of instrumental mass bias by applying the exponential law. Precisions of 0.73 for ε192Pt, 0.15 for ε194Pt and 0.09 for ε196Pt (2SD) | 398 |
| Se | Basalt and shale RMs | Rock powders mixed with 74Se–77Se double spike solution before dissolution in reverse aqua regia (3 + 1 HNO3–HCl) in a high-pressure asher. Purification using two-step ion-exchange chromatography (Eichrom AG1-X8 anion resin and AG50W-X8 resin) | MC-ICP-MS | Combination of double spike and HG sample introduction. Methane added to Ar Carrier gas to increase Se signal and supress background. Se and potential interferences monitored on 9 FCs equipped with 1011 Ω resistors. Reproducibility was <0.10‰ (2σ) for δ82 Se/76Se for a sample mass of 15 ng Se and 0.20‰ (2σ) for lower quantities down to 5 ng Se | 399 |
| Si | Geological RMs, meteorites (S and Fe-rich samples) | Geological RMs and meteorite powders dissolved using a NaOH fusion method. Final solution pH carefully controlled to avoid precipitation of Fe hydroxide. Purification by cation-exchange chromatography on Bio-Rad 50W-X8. Samples and RMs were doped with S to a fixed S/Si ratio to minimise differences in instrumental mass fractionation | MC-ICP-MS | Instrument in medium resolution mode with a resolving power of about 4000. Simultaneous measurements of 28Si, 29Si and 30Si. Instrumental mass bias and drift corrected using SSB with NBS 28 (Si and O isotopes in silica sand) as the bracketing calibrator | 400 |
| Sn | Geological RMs | 117Sn–122Sn double-spike added prior to digestion. Rock powders dissolved by HF–HNO3 method with final solution in 0.4M HCl. Purification of Sn by three-stage ion-exchange chromatography on BioRad AG1-X8. Recovery was 100 ± 5%; blanks of 0.70 ± 0.64 ng (2SD) were negligible compared to Sn content of samples | MC-ICP-MS | Simultaneous measurements of Sn, and potentially interfering Cd, Sb and Te isotopes on 9 FCs. Mass fractionation corrected by SSB and double-spike technique. Reproducibility for δ124Sn better than 0.06‰ (2SD) | 401 |
| Sn | Cassiterite, stannite, tin metal, tin bronze | Cassiterite reduced to Sn metal and stannite to Sn bronze by adding KCN and Cu powder, respectively, in a muffle furnace at 1000 °C. Metal beads were dissolved in HCl–H2O2 and diluted before analysis. The bronze solutions were purified by column chemistry with the TRU-Spec resin; recoveries were 100 ± 4% | MC-ICP-MS | Isotopic measurements on 9 FCs equipped with eight 1011 Ω and one 1010 Ω resistors. Comparison of three different mass bias corrections. Combined analytical uncertainty (2 s) of replicate dissolutions of bronze RMs (BAM 211, IARM-91D) was between 0.01 and 0.06‰ depending on the isotope ratio | 402 |
| Sn | Geological RMs | 117Sn–122Sn double-spike added to rock powders prior to digestion in HF–HNO3 in closed Teflon beakers on a hotplate at 120 °C. Final solution in 0.5 M HCl for anion-exchange chemistry. Chemical separation of Sn using Eichrom TRU resin gave variable yields of between 13 and 66% for geological RMs | MC-ICP-MS | Simultaneous measurement of 116Sn, 117Sn, 118Sn, 119Sn, 120Sn, 121Sb, 122Sn, 123Sb and 124Sn in a single cycle using FCs equipped with 1011 Ω resistors. Mass fractionation corrected by double-spike approach. External reproducibility of ±0.065‰ (2SD) on δ122Sn/118Sn for two rock RMs | 403 |
| Te | Chondrites, geological RMs | After the addition of a 125Te–128Te double spike, all samples except Mn nodules were digested in a series of dissolution steps involving HF, HNO3, aqua regia and HCl; Mn nodules were dissolved in HCl. After separation by anion-exchange on AG1-X8 resin, Te was further purified in two additional steps using AG1-X8 resin. Yields were ∼55% and blanks were ≤11 pg Te | MC-ICP-MS | Simultaneous measurements on FCs were performed at masses 125 (125Te), 126 (126Te, 126Xe), 128 (128Te, 128Xe), 129 (129Xe), 130 (130Te, 130Xe, 130Ba) and 135 (135Ba). Background and interference corrections were applied. The125Te–128Te double-spike methodology provided a long-term reproducibility (2SD) of 0.064‰ for δ130Te/125Te in an Alpha Aesar Te metal standard solution | 404 |
| W | Geological RMs | After addition of a 180W–183W double spike, samples were digested in HF–HNO3; further steps involved treatment with HCl and H2O2. Separation involved cation-exchange chromatography on Bio-Rad AG50W-X8 resin followed by purification of the W fraction on Bio-Rad AG1-X8 resin. Organics were destroyed with conc HNO3 + H2O2 before Eichrom TEVA resin was used to separate W from the remaining Hf, Ti and Zr. Yields for W were typically 60 to 95% and blanks ca. 50 pg | MC-ICP-MS | Measurements in low resolution mode on FCs with 1011 Ω resistors. Simultaneous measurements of 177Hf+, 181Ta+ and 188Os+ were used to correct for isobaric interferences from 180Hf+, 180Ta+, 184Os+ and 186Os+, respectively, by applying published relative isotope abundance data. After correction for mass-dependent fractionation using the double spike approach, results were expressed relative to NIST SRM 3163. External reproducibility of δ186W/184W in natural samples was ±0.018‰ | 405 |
New sample preparation procedures for making powder pellets for LA-ICP-MS included252 a ABF digestion procedure that produced ultrafine powders with a typical grain size d80 < 8.5 μm. The excellent cohesion and homogeneity of the resulting pressed powder pellets made them suitable for LA microanalysis without the addition of a binder, which would otherwise have diluted the sample. The results for six rock RMs covering a wide range of sample types agreed to within 10% with recommended values for most of the 10 major and 35 trace elements determined. The precision was <5% (1SD) for most major elements and <10% for most trace elements. Compared to other methods of sample preparation for bulk analysis by LA-ICP-MS, the ABF digestion method enabled complete dissolution of refractory minerals in 3 h with limited loss of volatile elements such as Pb and Zn. At the same time the risk of contamination, which can be a problem when preparing ultrafine powders by grinding, was minimised. Additional benefits included the potential to add an IS, reduced matrix effects because of the relatively uniform grain morphology and the ability to prepare matrix-matched calibrations. Bao et al.253 developed a method of preparing nanoparticulate pressed powder pellets from natural sulfide minerals of pyrite, chalcopyrite and sphalerite for in situ Pb and S isotope analyses. Powders with grain sizes of ∼75 μm were mixed with traces of galena before wet-milling to a grain size of d50 < 2.5 μm. The resulting material had excellent cohesion making it suitable for pressing without a binder. Homogeneity of the Pb and S isotope ratios was demonstrated by multiple spot analyses by LA-MC-ICP-MS. External precisions were <0.026% for 207Pb/206Pb and 208Pb/206Pb and <0.057% for 206Pb/204Pb, 207Pb/204Pb and 208Pb/204Pb (2RSD). The precision for 34S/32S (2SD) was <0.41%.
An alternative approach for bulk analysis by LA-ICP-MS is the preparation of fused beads. In a fusion procedure developed254 for the analysis of U ore concentrates and uraniferous ores with low silica contents, highly pure synthetic enstatite (MgSiO3) was added to ∼1.5 mg of sample powder in the proportion of 9 + 1 (enstatite + sample) to improve the fusion properties of these materials. The blended mixture was fused on an iridium strip resistance heater in an argon-purged chamber to form glass beads. The total preparation time was typically <10 minutes. Data for the REEs determined by LA-ICP-MS on glass beads prepared from a variety of samples, including eight geological RMs and three uranium CRMs, agreed well with published values. Although this procedure was designed to support nuclear forensic investigations, its speed of preparation, small sample mass requirement and suitability for microbeam analysis could be extended to other geochemical materials with inherently low silica levels, such as manganese nodules and polymetallic ores.
In a noteworthy study, gloves were examined255 as potential sources of contamination during sample preparation of geological and biological samples for trace element and Zn isotopic analyses. Fifteen different nitrile, latex, neoprene and vinyl gloves from major suppliers were tested by exposing the gloves for 40 h to dilute acid (20 mL of 0.4 M HNO3 + 0.05 M HF) to mimic the effects of leaching by acid fumes when handling samples in the laboratory. Several elements were released into the acid in significant amounts, e.g. up to 17 mg of Zn, 200 μg of Mg and 10–60 μg of Fe, Sr, Ti and Zr. Vinyl gloves released the lowest amounts of the elements other than In and Sn. The easy transfer of most elements from the gloves by simple contact with a pipette tip was particularly true for Zn so would potentially bias data for not only trace element content but also for isotopic composition. A single contact between glove and labware released on average ∼6 ng Zn so the δ66Zn value could be shifted significantly when the total amount of Zn processed through the procedure was <500 ng.
5.3 Instrumental analysis
In the field of isotope ratio determinations, Meng and Hu260 (72 references) reviewed the history of Ge isotope analysis, methods for sample digestion and purification, MS measurements and the notation and RMs used for Ge isotopes. The section devoted to MC-ICP-MS measurements compared different sample introduction systems, methods for minimising interferences and alternative approaches to mass bias corrections. Isotope ratio determinations of B by different MS techniques were discussed by Aggarwal and You261 (188 references). They described B isotope variations that occur in the environment and advances in analytical methodologies for B extraction and purification. The capabilities, advantages and limitations of different mass spectrometric techniques, including ICP-MS, LA-ICP-MS, SIMS and TIMS were compared. Current and future applications, inter-laboratory comparisons and RMs were also discussed.
One of the continuing debates in U–Pb dating of zircons is on the relative merits of zircon pretreatment. A study of microstructural and U–Pb systematics compared265 the behaviour of a 3500 Ma zircon which received either no pretreatment (pristine grains) or treatment by CA or TA. Hyper-spectral cathode luminescence and Raman spectroscopy were used for structural characterisation of the zircon grains and LA-ICP-MS for determination of trace element and U–Pb isotopic compositions. The effect of pretreatment was specific to individual samples and controlled by the microstructure of the original zircon grains. It was recommended that, rather than applying a standard pretreatment regime, a subset of pristine grains together with TA- and CA-treated grains be characterised by cathode luminescence imaging and Raman spectroscopy to determine the most suitable areas for reliable age determinations. It was emphasised that zircon RMs should be treated under the same conditions as samples of unknown composition. Guitreau et al.266 used data from LA-ICP-MS line-scans together with information on internal textures as revealed by BSE and cathode luminescence images to identify zones of well-preserved U–Pb isotope systematics within complex zircon crystals. Data from different zones were used to construct discordia lines and obtain age information related to crystallisation and metamorphism within a single zircon crystal. A workflow for dating minerals in situ without extracting the minerals from the host rock combined267 automated phase mapping by SEM/EDXRF with LA-ICP-MS. This approach was applicable to standard grain mounts, polished thin sections and hand specimens and did not require extensive manual mineral separation. The automated phase mapping provided rapid information on the chemical and mineralogical composition of the samples at high spatial resolution. The stage coordinates were converted to a file format readable by the LA system thereby locating the datable minerals in much shorter time than possible by conventional sample preparation procedures. This approach was applied to igneous, metamorphic and sedimentary samples. The high throughput was particularly advantageous for sedimentary provenance studies and could be applied to other accessory minerals such as apatite, monazite, rutile and titanite.
The use of multiple ion counters for U–Pb dating of zircons by LA-MC-ICP-MS made268 it possible to date zircons with a 238U signal of <0.5 mV with improved spatial resolution. The 202Hg, 204(Pb + Hg), 206Pb, 207Pb, 208Pb, 232Th and 238U isotope signals were measured simultaneously on only ion counters unlike previous studies that had used both ion counters and FCs. Tiny zircons and grains with small cores or narrow zonation could be analysed since three laser pulses (5 μm beam diameter) consumed <5 ng of zircon. A precision of <1% (2SD) for 206Pb/238U ages was achieved at a spatial resolution of 5.8–7.4 μm diameter and <3 μm depth.
The application of U–Pb geochronology to minerals other than zircon is often limited by the matrix dependence of analysis combined with the paucity of matrix-matched RMs. Burn et al.269 developed a novel approach to U–Th–Pb dating of allanite involving LA-ICP-MS measurement and data reduction using non-matrix-matched external calibration with the Plešovice zircon. Matrix-dependent downhole fractionation during LA was empirically quantified and corrected. The results obtained by this procedure on three allanite RMs had analytical precisions equal to those achieved by ion probe and LA-ICP-MS rastering. This calibration procedure could be employed for other non-matrix-matched pairs of a RM and a sample of unknown composition. An improved protocol for U–Th–Pb dating of xenotime by EPMA was developed270 using the monazite RM Moacyr for which a TIMS age is known. The similar crystal and chemical properties of xenotime and monazite, along with the negligible initial Pb content, made it possible to apply monazite EPMA methodologies to xenotime. It was assumed that measurements would not be affected by the fact that the LREEs rather than the HREEs were dominant. The proposed protocol achieved analytical uncertainties of <10% for Pb, Th and U measurements in xenotime. Ages obtained for co-existing xenotime and monazite were consistent with previously published data. Matrix effects in the SHRIMP U–Pb–Th analysis of xenotime can be significant because of the wide natural range of U and REE contents in this mineral, so Cross and Williams271 developed a matrix correction technique that required the concurrent analysis of three xenotime RMs with concordant U–Pb–Th compositions and different U and ∑REE concentrations. A least squares methodology was used to derive correction coefficients that related the SHRIMP matrix effects to the U and ∑REE concentrations. Calibrations using 206Pb+/270(UO2)+ and 208Pb+/248(ThO)+ were crucial to the success of this technique. A LA-ICP-MS procedure272 for the in situ dating of cassiterite, the main ore mineral in tin deposits, used 208Pb instead of 204Pb as a proxy for common Pb. The rationale behind this was that: (i) 208Pb is much more abundant and does not suffer from interference by 204Hg and (ii) cassiterite often has an extremely low Th concentration and so 208Pb could be assumed to be mostly of non-radiogenic origin. The analytical procedure did not rely on an independently dated matrix-matched cassiterite RM but instead utilised NIST SRM 612 (trace elements in glass) for calibration and a cassiterite from Russia (SPG) as a secondary RM. For each analytical session, a correction factor was determined from the difference between the Pb–Pb isochron date of the SPG cassiterite, accepted as a “true” age, and the biased U–Pb age resulting from the use of NIST SRM 612. The correction factor was then applied to correct the 238U/206Pb ratios for unknown samples.
Hornblende from the Lone Grove Pluton in Texas has acted as an irradiation RM in 40Ar/39Ar studies for many years. Blackburn et al.273 evaluated an apparent age bias between the U–Pb and 40Ar/39Ar geochronology systems using ID-TIMS to date zircons and titanites from the same rock from which the hornblende RM HB3gr was derived. The mean Th-corrected U–Pb date of the zircons was ∼7 Ma older than the dates derived from K–Ar or 40Ar/39Ar measurements. Analysis of single titanite grains yielded dates ∼6 Ma younger than the zircon age, indicating that the sample had experienced protracted cooling after initial crystallisation at 1090 Ma. Reconstruction of the cooling history suggested strongly that RM HB3gr was not an acceptable material for intersystem comparisons. It was also thought that any future use of this RM in 40Ar/39Ar studies should take into consideration the effects of Ar-loss through volume diffusion. Ellis et al.274 were able to analyse a single sanidine grain by splitting large crystals into up to four fragments. One split was used to perform high-precision 40Ar/39Ar dating. The other splits were used to determine: (i) major element concentrations by EPMA; (ii) trace element concentrations (in the sanidine and melt inclusions) and Pb isotope compositions by LA-ICP-MS; and (iii) Sr isotope analysis by TIMS. Although 40Ar/39Ar is one of the best chronometers for dating extra-terrestrial bodies, the method relies on neutron irradiation of the samples. A discussion of potential solutions for making in situ extra-terrestrial 40Ar/39Ar measurements included275 the challenges and feasibility of deploying a passive neutron source to planetary surfaces. Requirements for generating and shielding neutrons were described, as well as sample analysis and limitations such as mass, power and cost.
A recent study demonstrated276 that cosmogenic38Ar exposure dating of Ca-rich minerals could be a valuable new dating tool and provided the first terrestrial cosmochrons using the 38Ar isotope system. Previous application of this dating technique to terrestrial samples had been considered impracticable because of the high atmospheric 38Ar background on Earth. A new generation MC noble gas mass spectrometer enabled very precise analyses of cosmogenic 36Ar and 38Ar abundances in irradiated terrestrial pyroxene and apatite from Australia to be made. The 38Ar/36Ar ratios could be measured with a precision of ±0.30% (1SD). The measured exposure age overlapped, within uncertainty, with the known 40Ar/39Ar eruption age of the sampling site. It was concluded that cosmogenic 38Ar exposure dating on irradiated Ca-rich but Cl-free terrestrial minerals was potentially a valuable addition to the range of tools for dating cosmogenic exposure and examining geological surface processes on timescales of a few Ma.
Makonnen et al.278 investigated the characteristics of an Ar–N2–H2mixed gas plasma for the analysis of geological and environmental materials by ICP-AES. When 3.7% (v/v) N2 was added to the plasma gas and 1.1% (v/v) H2 to the sheath gas around the Ar nebuliser gas flow, the sensitivity for 26 elements increased by factors of between 1 and 4. Measurement of the MgII/MgI ratio demonstrated that the mixed gas plasma was significantly more robust than a conventional Ar plasma. The LODs for the mixed gas plasma were similar to those obtained with an Ar plasma. Quantitative multielement analyses of eight RMs (till, lake and stream sediments, Zn–Pb–Ag sulfide ore) demonstrated the superior analytical performance of the mixed-gas plasma for complex sample matrices without the need for an IS or matrix-matched calibration.
The challenges of quantifying precious metals in geological samples by LIBS have been explored. In the determination of Au in rock powders and drill cores, Rifai et al.281 established calibration curves using 44 compressed powder RMs of various chemical compositions with Au concentrations ranging between 0 and 1000 μg g−1. Principal component analysis revealed that the iron concentration accounted for about 83% of the variance in the spectra. The two different calibrations adopted for Si-rich samples (<5% Fe) and for Fe-rich samples (>13% Fe) had LODs of 0.8 and 1.5 μg g−1, respectively. Normalisation with respect to the integrated spectrum intensity or to the spectral background close to the Au line improved the regression fit of the calibrations. The technique was applied to the analysis of fine and granular Si-rich uncompressed ore powders and to the mapping of bulk drill cores to demonstrate that Au could be detected in mineral samples at levels relevant for the mining industry. However, issues related to calibration and sample preparation remained to be solved. Diaz et al.282 evaluated the performance of LIBS on pressed pellets of ore samples from a gold mine and surrogate samples doped with Ag and Au solutions. They achieved LODs of ∼1 μg g−1 for Ag and Au in the surrogate samples and 0.4–2.3 μg g−1 for Ag for the ore samples. However, because of the discrete nature of Au particles, it was not possible to detect Au in the pelletised ore samples or reach the target LOD of 1 μg g−1 for mining samples. The authors demonstrated that an alternative approach, developed for aerosol analysis (conditional analysis), could be applied to ore samples. More than 100 gold-containing particles needed to be sampled, so >105 laser pulses across a pellet were required. Under these conditions, Au could be measured at concentrations as low as 1 μg g−1 even in ore samples.
Cáceres et al.283 presented an imaging methodology based on LIBS to obtain the first megapixel images of large geological samples such as corals and speleothems and to provide resolution at the μg g−1 scale. Images of Mg, Na and Sr distributions were normalised to the Ca content. The instrumentation consisted of an optical microscope, a LIBS laser injection line, a standard optical-imaging apparatus and a 3D motorised platform for sample positioning. Compositional variations in marine mollusc shells were assessed284 by elemental mapping using an automated LIBS system to acquire Mg/Ca intensity ratios of whole shell sections with a spatial resolution of ∼90 μm and acquisition speed of 20000 spectra per hour. This approach, which can be extended to other elements relevant to environmental studies, was regarded as a time- and cost-effective alternative to other methods, such as LA-ICP-MS, for deriving elemental distribution maps. Moncayo et al.285 reported a methodology based on hyperspectral image analysis coupled to PCA for processing large raw data sets of LIBS spectra without spectral pre-processing or any prior knowledge of the sample composition. It was shown to be a powerful tool capable of identifying mineral phases and providing a deeper understanding of the sample, such as highlighting very delicate ion substitution in a mineral lattice.
5.3.5.1 Trace element determinations by solution-based ICP-MS. Methods for correcting for spectral and non-spectral interferences in ICP-MS have been investigated. Yokoyama et al.286 examined the influence of non-spectral matrix effects on the determination of 22 trace elements (Ba, Cs, Nd, Pb, Rb, REEs, Sr, Th, U and Y) in rock samples by quadrupole ICP-MS. They developed a new ID-IS method using a mixture of 113In- and 203Tl-enriched spikes to correct for mass-dependent sensitivity variations. When combined with corrections for oxide and hydroxide interferences, good agreement (±10%) with abundances reported in previous studies was achieved for eight GSJ rock RMs of varying composition. For the andesitic and basaltic rocks (JA-2, JB-2 and JB-3) the agreement was better than ±5%. To overcome287 Hf and Ta oxide interferences in the determination of trace amounts of Au in geological samples by ICP-MS, a combination of an aerosol dilution technique, in which Ar was added to the sample aerosol prior to the ICP, and addition of a CH4 plasma modifier to the nebuliser Ar gas flow was employed. Adding 3 mL min−1 CH4 increased the Au S/N by a factor of about 10 and resulted in a LOQ (10SD) of 0.014 ng g−1 Au. In an evaluation of the method using 39 soil, sediment and rock RMs, the levels of Au determined were consistent with the reference values, even though the Au was present at <1 ng g−1 in 11 of the geological RMs.
5.3.5.2 Trace element determinations by laser ablation ICP-MS. The potential added value of ultrafast lasers was evaluated288 in a topical review (168 references) on the use of fs lasers in LA-ICP-MS for elemental and isotopic analysis over the last 15 years. Benefits included greatly reduced chemical fractionation effects, negating the need for matrix-matched RMs. In addition, independence of laser–matter interactions from the optical properties of the target material provided efficient ablation of optically transparent materials like quartz. Apart from the fact that there are geochemical applications for which fs lasers do not confer any key advantage, e.g. zircon U–Pb geochronology, one of the important limitations is still the high price of fs LA systems compared to that of ns excimer systems.
In order to evaluate long term precision and accuracy, a new Matlab-based software called “LA-MINE” was developed289 to extract automatically RM data from large LA-ICP-MS datasets obtained over a 5 year period under a wide range of ablation and tuning conditions. This program required no user input and years of data could be processed within a few hours. Detailed consideration of data quality for Al, B, Ba, Fe, Li, Mg, Mn, Na, P, Pb, Rb, REE, Sr, Th, U, Y and Zn in 10 commonly analysed RMs highlighted where care needed to be taken, the relative effects of mass fractionation, and errors in the reported values of some elements in certain RMs. Recommendations for the improvement of data quality included the role of diatomic gas in the laser cell, the selection of appropriate RMs in calculating fractionation factors and choice of transport tubing material.
Several studies focused on improvements in multielemental imaging using LA-ICP-MS. A novel cylindrical ablation cell, with a 70 mm diameter was developed290 specifically for this type of application. Based on the two-volume cell strategy, the major innovation of the new cell design was that the sampling tube was coaxial with the laser beam and fixed to the laser assembly, thereby enabling the sampling point to be positioned constantly over the ablation spot. A viscous film was used to seal the junction between the lower and upper parts of the cell. The figures of merit were assessed by monitoring the transient signal of 238U from ablated NIST SRM 610 (trace elements in glass). The washout time was 210 ms. This cell could be fitted on the stage of standard LA systems. Improvements in cell design, in particular to achieve faster washout times, have made it possible291 to realise the benefits of combining LA with ICP-TOF-MS. A multielemental image of an area of 1 × 2 mm of a polished thin section of the mineral sphalerite (ZnS) was obtained at a spatial resolution of 5 μm in 80 minutes. The approach of Fox et al.292 was to use numerical methods to improve the spatial resolution of elemental maps. They demonstrated that sub-spot size resolution could be achieved through linear inversion methods. As multiple LA spots may sample the same location, the area sampled in an analysis was divided into pixels and the measured concentration defined as the average concentration of the pixels sampled by a spot. A system of linear equations used information from multiple overlapping spots to infer sub-spot size variations in element concentrations. Examples from magmatic ascent speeds and (U–Th)/He noble gas thermochronometry were used to demonstrate the utility of this approach. A novel tool called Monocle was devised293 for extracting quantitative elemental and isotope data from LA-ICP-MS maps. Histograms and density or probability plots of the element concentrations or isotope ratios contained within the area under a size- and shape-adjustable “virtual” loupe were displayed as the loupe was moved across the map. This tool was an add-on to the commercial Iolite data reduction program and it was expected that this approach to post-data acquisition analysis of images would increase the productivity of LA-ICP-MS facilities.
In the development of a protocol294 for in situ LA-ICP-MS analysis of fluid inclusions to determine concentrations of I in addition to those of Br and Cl, potential RMs (scapolite Sca17, Durango apatite, USGS GSE-1G and GSD-1G) were first characterised. Although low in I content, only Sca17 was a suitable RM for all three halogens. The method was then verified by analysis of synthetic inclusions with known Br, Cl and I concentrations before applying the triple-halogen analysis to natural inclusions. Results from the study of fluid inclusions from three geological settings revealed very significant differences in I/Cl ratios whereas the Br/Cl ratios from the different settings were indistinguishable. The ability to determine I concentrations in addition to those of Br and Cl opened up new possibilities for studying the source and migration of crustal fluids. Three significant sources of contamination highlighted295 in a practical guide to the LA-ICP-MS analysis of fluid inclusions were: the quality of the carrier gas; the material and cleanliness of ablation cell, tubing material and cones; and contamination of the sample surface during its preparation. A strategy for minimising contributions from these sources and optimising quantification of fluid inclusion data close to the LOD was advocated. Jian et al.296 used a freezing cell in the UV-fs-LA-ICP-MS analysis of CO2-rich fluid inclusions from a quartz vein-style Au–Mo deposit from central China. Transforming gaseous and liquid CO2 into the solid state by freezing reduced the internal pressure of the fluid inclusions significantly. The frozen state was maintained during ablation because the fs laser pulses transferred a minimal amount of heat. Consequently, the transient signals were more stable than those from the ns-LA-ICP-MS analysis of such inclusions at room temperature and improved the analytical performance.
In the determination of REE mass fractions in olivine by LA-ICP-MS, large spot sizes of 160 μm and laser repetition rates of 30 Hz were used297 to produce pit depths of ∼165 μm and to ablate ∼500 μg of material in order to maximise the REE signals. Typical LODs were 0.2–0.4 ng g−1 with biases of between 5 and 10%. The method was therefore suitable for studies of REE concentrations in olivines which could be as low as the ng g−1 level. In a study of the isobaric interferences that are a constant problem at many REE isotope masses, REE abundances were determined298 in both low (m/Δm = 300) and medium (m/Δm = 2500) resolution modes by LA-SF-ICP-MS. The results obtained in medium resolution mode were significantly more accurate and precise, particularly for RMs with low REE contents. A list of preferred masses that are least susceptible to isobaric interferences was given.
5.3.5.3 Isotope ratio determinations by ICP-MS. Judging by the number of published papers, the measurement of isotope ratios by MC-ICP-MS and other ICP-MS techniques continues to be a fruitful area of research in analytical geochemistry. Many of these publications record relatively modest improvements in methodologies, such as the modification of an existing column chemistry protocol or alternative detector configurations. These are summarised in Table 6 and deal exclusively with solution sample introduction into the instrumentation concerned.
Measurement of Sr isotope ratios by LA-ICP-MS continues to attract interest for a variety of geological applications. The advantages of using LA-MC-ICP-MS for determining 87Sr/86Sr ratios in speleothems included299 rapid data acquisition, higher spatial resolution, larger sample throughput and the absence of chemical treatment. The minimum Sr concentration required for reliable 87Sr/86Sr ratios was 300 μg g−1 but the best results were obtained when Sr concentrations exceeded 1000 μg g−1. Line-scans provided better precision than spot analysis. Although a fs LA system provided a more stable signal than a ns laser, and therefore more precise measurements, its use for samples with low Sr contents (<1400 μg g−1) was not recommended because of the lower signal intensities which led to less precise results. Another study compared300 the characteristics of ns and fs LA-ICP-MS systems for in situ Sr isotope measurements in transparent minerals such as feldspars. The results indicated that the fs LA rates were more consistent than those for the ns laser system and provided higher sensitivities at similar energy settings. Addition of N2 to the axial channel of the ICP suppressed the Kr and Rb signal intensities and the interferences from polyatomic and doubly charged ions such as CaAr+, Ca2+, Er2+ and Yb2+. Although the sensitivity for Sr was also suppressed by the addition of N2, this effect was less than for the other elements so the S/N was enhanced and the accuracy of the 84Sr/86Sr ratio measurements in particular improved. Samples with a Rb/Sr ratio of <0.5 could be analysed with an accuracy of <0.0002 for 87Sr/86Sr. Strontium isotope ratios were determined301 in basaltic glasses with Rb/Sr ratios <0.14 by LA-MC-ICP-MS using seven FCs to collect signals at m/z 82 to 88. The isobaric interference of 87Rb on 87Sr was corrected by the peak stripping method and instrumental mass fractionation on 87Sr/86Sr corrected using an exponential law. A linear correlation between the residual analytical bias on 87Sr/86Sr and the measured raw 87Rb/86Sr ratio was observed. Three USGS basaltic glasses with different Rb/Sr ratios were analysed to establish this relationship for each analytical run; the residual analytical biases on the 87Sr/86Sr ratios of the unknowns were then corrected by applying this linear relationship. The final 87Sr/86Sr ratios were accurate to ±100 ppm.
In situ measurements of Pb isotopes by LA-ICP-MS have been investigated extensively. A ns LA-MC-ICP-MS method was developed302 for high precision Pb and Sr isotope ratio determinations in archaeological glasses. Inaccurate data were obtained when glass RMs with basaltic matrices were used to correct for mass bias. Matrix-matched RMs were therefore regarded as indispensable. Four Corning soda-lime silicate glass RMs, mimicking the composition of archaeological glasses, were characterised for their Pb and Sr isotope compositions by solution and LA-MC-ICP-MS. Interferences from doubly charged REE ions affecting the Sr isotope ratios were circumvented by operating the instrument at higher mass resolution. The most accurate Pb isotope ratio results were obtained by correcting for instrumental mass discrimination with the exponential law, using Tl (introduced by pneumatic nebulisation) as an isotopic IS and Corning D glass as the bracketing isotopic calibrator. The in situ measurement303 of “common” Pb isotope ratios in silicate glasses and minerals using a 193 nm excimer LA system coupled to a double-focusing single-collector ICP-MS instrument was evaluated as a possible alternative to MC-ICP-MS. Lead isotope ratios were corrected for mass bias relative to NIST SRM 610 (trace elements in glass). It was recommended that unknowns and bracketing RMs should be analysed at identical spot sizes and to use spot sizes ≥25 μm. Analysis of a range of NIST, MPI-DING and USGS RM glasses identified analytical artifacts that were attributed to non-spectral matrix effects. The precision (2SD) of the Pb isotopic measurements was similar to that obtained in previous studies. However, the authors doubted that further improvement in the precision of measurement of the 20XPb/206Pb and 20XPb/204Pb ratios for materials with high (>5 μg g−1) Pb concentrations could be achieved with this approach and that, consequently, LA-MC-ICP-MS remained the preferred analytical technique.
Bauer and Horstwood304 assessed suitable methods for the combined determination of Lu–Hf and U–Pb isotope signatures in zircons by solution and LA-MC-ICP-MS. By reducing the pit volume during LA and taking consecutive U–Pb and Lu–Hf isotopic measurements, they demonstrated that acceptable uncertainty levels could be achieved at a spot size of 25 μm and a pit depth of 18 μm. This corresponded to ablation of ∼40 ng of zircon. The possibility of reducing the LA pit depth to 10 μm was demonstrated by decreasing the integration time for Hf analysis without seriously compromising the uncertainty. The method had the potential to yield geologically meaningful results from complex finely-zoned zircons. The LASS technique is an alternative approach that enables simultaneous collection of data from different isotope systems, such as U–Pb and Lu–Hf, on separate ICP-MS instruments. However, it has been noted305 that the full benefits of such simultaneous datasets are often not exploited as laboratories usually process each dataset separately. New features were developed within the data reduction software Iolite to load, synchronise and visualise concurrently two or more datasets acquired simultaneously. The utility of this new data reduction facility in Iolite was demonstrated in case studies to image U–Pb and Lu–Hf isotope distributions in zircon and U–Pb and Sm–Nd isotope compositions in monazite.
One of the first LA-MC-ICP-MS carbon isotope studies of natural minerals has been reported.306 The new method, which used an excimer LA system coupled to an MC-ICP-MS instrument, adopted a SSB method using magmatic calcites as bracketing RMs to correct for instrumental mass bias. The method was validated using a suite of magmatic and hydrothermal calcites with δ13C values ranging from −6.9 to 1.48‰ previously determined by IRMS. An analytical precision of ≤0.25‰ was achieved for homogenous magmatic calcites. The mean values obtained in the LA-MC-ICP-MS analyses of calcites and corals (aragonite) agreed within uncertainties with those determined by IRMS on bulk samples.
The influence of low recovery rates from column separation procedures on calcium isotope measurements by double spike TIMS was investigated.309 Three RMs (a seawater and two basalts), were processed through a PTFE micro-column containing Bio-Rad AG MP-50 resin. The eluant was divided into five aliquots containing approximately 20% of the total Ca content. After correction using the 42Ca–43Ca double spike technique with exponential law, the δ44Ca/40Ca of the individual aliquots or “subcuts” showed limited variation and were consistent within uncertainties with the results from full recovery. On the basis of this, a “peak cut” procedure was proposed in which the double spike was added to the sample and equilibrated prior to column separation and only the middle part of the Ca eluate was collected to obtain a pure Ca fraction. In this way isobaric interferences from elements like K and Sr were effectively eliminated. This was particularly important for samples with low CaO contents. Mondal and Chakrabarti310 proposed a novel sample loading method and protocol for Ca stable isotope ratio measurements by double spike TIMS. An external reproducibility for δ44Ca/40Ca of <0.1‰ for different sample matrices (seawater, carbonates and silicates) using a 43Ca–48Ca double spike was achieved, together with the simultaneous measurement of δ44Ca/42Ca. A sample loading technique employing a combination of Re and Ta filaments and a Ta2O5 activator provided an external reproducibility for δ44Ca/40Ca of better than 0.08‰ and was therefore preferred to the other two loading protocols tested for which the external reproducibility for δ44Ca/40Ca ranged from ±0.16 to 0.26‰.
A protocol for microscale87Sr/86Sr isotope ratios determinations by TIMS combined311 high-performance mechanical microsampling and high-precision TIMS measurements. A slurry prepared from a milled sample was collected with a micropipette and transferred to a Class 1000 clean room for digestion and purification. One of the most critical aspects of the TIMS determinations was the loading procedure of the very small samples onto single Re filaments. Measurements in dynamic mode were preferred over static mode because of the better long-term reproducibility. Although more time-consuming, this TIMS method gave more accurate and precise data than in situ LA-MC-ICP-MS analysis.
Further developments in high-precision isotope ratio determinations by TIMS are included in Table 6.
There is an increasing need for matrix-matched RMs for SIMS analysis just as for other microbeam techniques. Ramsey and Wiedenbeck313 proposed a method for quantifying the heterogeneity of candidate RMs at the pg scale. They illustrated their approach by measuring the oxygen isotope ratio of four quartz samples by SIMS using a large number (∼100) of closely spaced duplicated readings. Measurement repeatability was extracted from the total variability using ANOVA statistics. The use of a sub-area of the test material was preferable to using a piece of unrelated homogenous silicate glass for estimating and correcting instrumental drift. Three approaches for estimating measurement repeatability were compared and a strategy proposed for predicting the number of measurements on individual fragments of a material required to achieve a specified target uncertainty. A study of two baddeleyite megacrysts, Phalaborwa S0045 and Mogok S0069, as potential RMs for oxygen isotope analysis by SIMS showed314 repeated analysis of a number of fragments made it possible to measure the O isotopes with precisions (e.g. ±0.2 to ±0.25‰ per spot, 2SD) similar to those obtained in the analysis of zircons. Metamictisation, lattice orientation and chemical composition played no significant role in instrumental mass fractionation. Fourteen glass RMs comprising nine synthetic basaltic glasses, four natural basaltic glasses and one natural rhyolitic glass were characterised315 for the measurement of volatile elements C, Cl, F, H, P and S by SIMS. The elemental concentrations were designed to cover the range of data for volatile elements reported in previous studies. Although limited quantities of the synthetic glasses were prepared, the natural glasses were available to other laboratories on request.
Matrix effects on instrumental mass fractionation of O isotopes in olivine measured by SIMS were investigated316 over the entire olivine Fe–Mg solid-solution series. The matrix effects were reproducible between different SIMS instruments and between different analytical sessions. A model curve, developed to correct measurements on olivine samples with intermediate chemical composition for instrumental mass fractionation, achieved a precision and accuracy of ∼0.4‰ for δ18O at the 10–25 μm scale. Similarly, Sliwinski et al.317 explored the effects of cation composition on instrumental mass fractionation in the SIMS determination of δ13C and δ18O in carbonates of the magnesite–siderite solid-solution series (MgCO3–FeCO3). A suite of 12 new RMs was developed for calibration purposes. The calibration curves of both isotope systems were non-linear and mass bias was consistently most sensitive to changes in composition near the iron-free end member, with deviations up to 4.5‰ for δ13C and 14‰ for δ18O with increasing Fe content. The cause of variability in the calibration curve shape was not well understood. This study demonstrated the importance of having available a sufficient number of well-characterised RMs so that potential complexities of curvature could be adequately delineated.
Carr et al.318 evaluated possible effects of crystal orientation on measurements of U–Pb and O isotopic compositions in cassiterite (SnO2) by SIMS. Previous studies had shown large effects of crystal orientation on SIMS analyses of rutile, which is isostructural with cassiterite. However, for cassiterite none of the key measurement parameters such as Pb/UO, UO2/UO or 18O/16O ratios showed any dependence on crystallographic orientation. The contrasting behaviour of cassiterite and rutile provided new insights into the mechanisms involved when such effects are observed in SIMS.
Ishida et al.319 developed an in situ method for the simultaneous determination of C and N isotope ratios in organic matter by SIMS with a spatial resolution of ∼12 μm. Four molecular ions (12C2−, 12C13C−, 12C14N− and 12C15N−) were measured simultaneously using a new detector geometry and aperture settings. They evaluated the homogeneity of δ15N values in potential anthracite RMs, for which δ13C compositions were known to be homogeneous. The analytical precisions for δ13C and δ15N in RM UWLA-1 anthracite were 0.16 and 0.56‰ (2SD), respectively.
Alov and Sharanov323 proposed a novel procedure for the rapid analysis of Cu–Zn ores by TXRFS. Several non-aqueous high-viscosity liquids were tested and ethylene glycol selected to prepare suspensions of the powdered ore. A Ni IS was added and the suspension treated with ultrasonic radiation (to destroy ore agglomerates) and homogenised before a sub-sample was taken with a micropipette. The suspensions were stable long enough (∼2 minutes) for representative samples to be taken. This procedure made it possible to determine the elemental composition of solid ore samples rapidly because sample digestion was not necessary.
Advances in μ-EDXRFS mapping have opened up new opportunities for rapid analysis. Mineral distribution maps of thin sections of plutonic rocks were acquired324 by applying a classification algorithm called spectral angle mapper. This physically-based spectral classification determined the spectral similarity between two spectra by matching pixels to reference spectra. The results obtained were in accordance with results from automated mineralogy obtained by other techniques and demonstrated that μ-EDXRFS mapping was a suitable method for providing fast overviews of chemical, mineralogical and textural information with little sample preparation effort and short acquisition times. The so-called Maia Mapper325 was an automated laboratory μXRF mapping system for elemental imaging of drill core sections. It consisted of a Maia detector system, engineered several years ago for EDXRF and elemental imaging, a high brightness metal jet X-ray source from Excillum, a polycapillary lens and a sample scanning stage that took standard drill-core sections. The Maia Mapper was capable of high definition XRFS mapping of mineral samples across 500 mm at a spatial resolution of about 32 μm. The sensitivity was sufficient to capture fine details of trace element indicators and precious metal phases in mineralised veins.
A comparison of PIXE and XRFS techniques was conducted326 to assess the best approach for the determination of major elements and some minor elements in heterogeneous lake sediments. Any discrepancies between the two techniques were mainly related to the texture of the sediments and the intrinsic features of PIXE and XRFS. It was recommended that to get the best accuracy by either technique, a linking powder should be added to the sediments to fill the voids and increase the fluorescence intensities and that replicate measurements should be made. Although appropriate, neither suggestion could be considered novel. Similar sample preparation procedures have been described previously and it should be common practice, especially when analysing inhomogeneous samples, to perform repeat analyses.
6. Glossary of terms
| 2D | two dimensional |
| 3D | three dimensional |
| AAS | atomic absorption spectrometry |
| AB | arsenobetaine |
| ABF | ammonium bifluoride |
| AEC | anion exchange chromatography |
| AES | atomic emission spectrometry |
| AFS | atomic fluorescence spectrometry |
| AMS | accelerator mass spectrometry |
| amu | atomic mass unit |
| ANOVA | analysis of variance |
| APDC | ammonium pyrrolidine dithiocarbamate |
| ASU | Atomic Spectrometry Update |
| AVEC | attenuation versus evolved carbon |
| BCR | Community Bureau of Reference (EU) |
| BP | bulk particle |
| BSE | back-scattered electron |
| C18 | octadecyl bonded silica |
| CA | chemical abrasion |
| CE | capillary electrophoresis |
| CF | continuous flow |
| CLD | chemiluminescence detection |
| CNT | carbon nanotube |
| CPE | cloud point extraction |
| cps | counts per second |
| CRM | certified reference material |
| CS | continuum source |
| CT | computed tomography |
| CV | cold vapour |
| CVG | chemical vapour generation |
| DBT | dibutyltin |
| DDC | diethyldithiocarbamate |
| DES | deep eutectic solvent |
| DGA | diglycolamide |
| DGT | diffusive gradient in thin films |
| DLLME | dispersive liquid–liquid microextraction |
| DMA | dimethylarsenic |
| DME | dispersive microextraction |
| DMF | dimethylformamide |
| DOC | dissolved organic carbon |
| DRI | Desert Research Institute (USA) |
| EDB | electrodynamic balance |
| EDTA | ethylenediaminetetraacetic acid |
| EDXRFS | energy dispersive X-ray fluorescence spectrometry |
| EN | European Committee for Standardisation |
| EPMA | electron probe microanalysis |
| ERM | Environmental Resources Management |
| ETAAS | electrothermal atomic absorption spectrometry |
| EtHg | ethylmercury |
| ETV | electrothermal vaporisation |
| EU | European Union |
| FAAS | flame atomic absorption spectrometry |
| FC | Faraday cup |
| FFF | field flow fractionation |
| FI | flow injection |
| FIA | flow injection analysis |
| fs | femtosecond |
| FTIR | Fourier transform infrared |
| GC | gas chromatography |
| GD | glow discharge |
| GLS | gas liquid separator |
| GRIP | Greenland Ice Core Project |
| GSJ | Geological Survey of Japan |
| HEN | high efficiency nebuliser |
| HFSE | high field strength element |
| HG | hydride generation |
| HPS | High-Purity Standards |
| HPLC | high performance liquid chromatography |
| HR | high resolution |
| HREE | heavy rare earth element |
| IAEA | International Atomic Energy Agency |
| IAG | International Association of Geoanalysts |
| IC | ion chromatography |
| ICP | inductively coupled plasma |
| ICP-AES | inductively coupled plasma atomic emission spectrometry |
| ICP-MS | inductively coupled plasma mass spectrometry |
| ICP-MS/MS | inductively coupled plasma tandem mass spectrometry |
| id | internal diameter |
| ID | isotope dilution |
| IDA | isotope dilution analysis |
| IEC | ion exchange chromatography |
| IGI | Institute of Geochemistry, Irkutsk (Russia) |
| IMEP | International Measurement Evaluation Programme |
| INAA | instrumental neutron activation analysis |
| INCT | Institute of Nuclear Chemistry and Technology (Poland) |
| IR | infrared |
| IRA | isotope ratio analysis |
| IRMM | Institute for Reference Materials and Measurements (EU) |
| IRMS | isotope ratio mass spectrometry |
| IS | internal standard |
| ISBN | international standard book number |
| ISM | incremental sampling methodology |
| ISO | International Organization for Standardisation |
| LA | laser ablation |
| LASS | laser ablation split stream |
| LGC | Laboratory of the Government Chemist (UK) |
| LIBS | laser induced breakdown spectroscopy |
| LIFS | laser induced fluorescence spectrometry |
| LLE | liquid–liquid extraction |
| LLME | liquid–liquid microextraction |
| LOD | limit of detection |
| LOQ | limit of quantification |
| LPME | liquid phase microextraction |
| LREE | light rare earth element |
| LSDBD | liquid spray dielectric barrier discharge |
| MAD | microwave assisted digestion |
| MAE | microwave assisted extraction |
| MBT | monobutyltin |
| MC | multicollector |
| MDL | method detection limit |
| MeHg | methyl mercury |
| MFH | medicine food homology |
| MIL | magnetic ionic liquid |
| MIO | magnetic iron oxide |
| MIP | microwave induced plasma |
| MLLE | micro liquid–liquid extraction |
| MMA | monomethylarsenic |
| MP | microwave plasma |
| MPI | Max Planck Institute (Germany) |
| MRM | multi reaction monitoring |
| MS | mass spectrometry |
| MS/MS | tandem mass spectrometry |
| MSPE | magnetic solid phase extraction |
| MU | measurement uncertainty |
| MWCNT | multiwalled carbon nanotube |
| m/z | mass to charge ratio |
| NAA | neutron activation analysis |
| NBS | National Bureau of Standards (USA) |
| NCS | National Analysis Center for Iron and Steel (China) |
| Nd:YAG | neodymium doped: yttrium aluminium garnet |
| NIES | National Institute for Environmental Studies (Japan) |
| NIOSH | National Institute of Occupational Safety and Health (USA) |
| NIR | near infrared |
| NIST | National Institute of Standards and Technology (USA) |
| NMIJ | National Metrology Institute of Japan |
| NP | nanoparticle |
| NRCC | National Research Council Canada |
| NRCCRM | National Research Centre for Certified Reference Materials (China) |
| ns | nanosecond |
| NTIMS | negative thermal ionisation mass spectrometry |
| NWRI | National Water Research Institute (Canada) |
| PAH | polyaromatic hydrocarbon |
| PCA | principal component analysis |
| PFA | perfluoroalkoxy alkanes |
| PGE | platinum group element |
| PhHg | phenylmercury |
| PIXE | particle-induced X-ray emission |
| PLS | partial least squares |
| PM2.5 | particulate matter (with an aerodynamic diameter of up to 2.5 μm) |
| PM4 | particulate matter (with an aerodynamic diameter of up to 4.0 μm) |
| PM10 | particulate matter (with an aerodynamic diameter of up to 10 μm) |
| ppm | parts per million |
| PSL | polystyrene latex |
| PTE | potentially toxic element |
| PTFE | poly(tetrafluorethylene) |
| PTR | proton transfer reaction |
| PVG | photochemical vapour generation |
| pXRF | portable X-ray fluorescence |
| pXRFS | portable X-ray fluorescence spectrometry |
| QA | quality assurance |
| QC | quality control |
| QCL | quantum cascade laser |
| QCLAS | quantum cascade laser absorption spectrometry |
| RCS | respirable crystalline silica |
| REE | rare earth element |
| RIMS | resonance ionisation mass spectrometry |
| RM | reference material |
| RMSEP | route mean square error of prediction |
| RSD | relative standard deviation |
| SARM | South African Reference Material |
| SAXS | small angle X-ray scattering |
| SBET | simplified bioaccessibility extraction test |
| SBME | solvent bar micro-extraction |
| SCF | supercritical fluid |
| SD | standard deviation |
| SDD | silicon drift detector |
| SDS | sodium dodecyl sulfate |
| SEM | scanning electron microscopy |
| SES | spark emission spectroscopy |
| SF | sector field |
| SFOD | solidification of a floating organic droplet |
| SHRIMP | sensitive high resolution ion microprobe |
| SI | système international d’unités |
| SIMS | secondary ion mass spectrometry |
| SLME | solid–liquid microextraction |
| SMPS | scanning mobility particle sizer |
| S/N | signal-to-noise ratio |
| SNMS | sputtered neutral mass spectrometry |
| sp | single particle |
| SPAMS | single particle aerosol mass spectrometry |
| SPE | solid phase extraction |
| SPME | solid phase microextraction |
| SPS | Spectrapure Standards |
| SRM | standard reference material |
| SSB | sample-standard bracketing |
| SSME | supramolecular solvent microextraction |
| SSID | species specific isotope dilution |
| SSME | supramolecular solvent microextraction |
| TA | thermal annealing |
| TBT | tributyl tin |
| TD | thermal desorption |
| TEM | transmission electron microscopy |
| TEOM | tapered element oscillating microbalance |
| TIMS | thermal ionisation mass spectrometry |
| TMAH | tetramethylammonium hydroxide |
| TML | tetramethyllead |
| TOF | time-of-flight |
| TXRF | total-reflection X-ray fluorescence |
| TXRFS | total-reflection X-ray fluorescence spectrometry |
| UA | ultrasound-assisted |
| UAE | ultrasound-assisted extraction |
| UBMG | unified bioaccessibility method (gastric fluid only) |
| USGS | United States Geological Survey |
| US EPA | United States Environmental Protection Agency |
| USN | ultrasonic nebuliser |
| UME | National Metrology Institute of Turkey |
| UV | ultraviolet |
| VG | vapour generation |
| VSMOW | Vienna Standard Mean Ocean Water |
| WHO | World Health Organisation |
| XANES | X-ray absorption near-edge structure |
| XAS | X-ray absorption spectroscopy |
| XRD | X-ray diffraction |
| XRF | X-ray fluorescence |
| XRFS | X-ray fluorescence spectrometry |
| Z | atomic number |
7. Conflicts of interest
There are no conflicts to declare.8. References
- O. T. Butler, W. R. L. Cairns, J. M. Cook, C. M. Davidson and R. Mertz-Kraus, J. Anal. At. Spectrom., 2018, 33(1), 8–56 RSC.
- A. Taylor, N. Barlow, M. P. Day, S. Hill, N. Martin and M. Patriarca, J. Anal. At. Spectrom., 2017, 33(3), 338–382 RSC.
- E. H. Evans, J. Pisonero, C. M. M. Smith and R. N. Taylor, J. Anal. At. Spectrom., 2018, 33(5), 684–705 RSC.
- R. Clough, C. F. Harrington, S. J. Hill, Y. Madrid and J. F. Tyson, J. Anal. At. Spectrom., 2017, 33(7), 1103–1149 RSC.
- C. Vanhoof, J. R. Bacon, A. T. Ellis, L. Vincze and P. Wobrauschek, J. Anal. At. Spectrom., 2018, 33(9), 1413–1431 RSC.
- S. Carter, A. Fisher, B. Gibson, J. Marshall, B. Russell and I. Whiteside, J. Anal. At. Spectrom., 2017, 32(11), 2068–2117 RSC.
- A.-K. Viitanen, S. Uuksulainen, A. J. Koivisto, K. Hämeri and T. Kauppinen, Ann. Work Exposures Health, 2017, 61(7), 749–758 CrossRef PubMed.
- B. Krause, T. Meyer, H. Sieg, C. Kastner, P. Reichardt, J. Tentschert, H. Jungnickel, I. Estrela-Lopis, A. Burel, S. Chevance, F. Gauffre, P. Jalili, J. Meijer, L. Bohmert, A. Braeuning, F. Thunemann, F. Emmerling, V. Fessard, P. Laux, A. Lampen and A. Luch, RSC Adv., 2018, 8(26), 14377–14388 RSC.
- E. S. Galvao, J. M. Santos, A. T. Lima, N. C. Reis, M. T. D. Orlando and R. M. Stuetz, Chemosphere, 2018, 199, 546–568 CrossRef CAS PubMed.
- A. R. Metcalf, S. Narayan and C. S. Dutcher, Aerosol Sci. Technol., 2018, 52(3), 310–329 CrossRef CAS.
- M. Chen, F. J. Romay and V. A. Marple, Aerosol Sci. Technol., 2018, 52(2), 192–197 CrossRef CAS.
- S. Kelleher, C. Quinn, D. Miller-Lionberg and J. Volckens, Atmos. Meas. Tech., 2018, 11(2), 1087–1097 CrossRef.
- P. Stacey, A. Thorpe, P. Roberts and O. Butler, Atmos. Environ., 2018, 182, 51–57 CrossRef CAS.
- D. S. McLagan, C. P. J. Mitchell, H. Y. Huang, B. A. Hussain, Y. D. Lei and F. Wania, Atmos. Meas. Tech., 2017, 10(10), 3651–3660 CrossRef CAS.
- M. Shirdel, H. Wingfors, B. M. Andersson, J. N. Sommar, I. A. Bergdahl and I. E. Liljelind, Ann. Work Exposures Health, 2017, 61(8), 1029–1034 CrossRef PubMed.
- O. Black, J. J. Chen, A. Scircle, Y. Zhou and J. V. Cizdziel, Environ. Sci. Pollut. Res., 2018, 25(13), 13195–13202 CrossRef CAS PubMed.
- A. W. Birdsall, U. K. Krieger and F. N. Keutsch, Atmos. Meas. Tech., 2018, 11(1), 33–47 CrossRef CAS.
- J. Charoud-Got, G. Emma, J. Seghers, M. F. Tumba-Tshilumba, A. Santoro, A. Held, J. Snell and H. Emteborg, Anal. Bioanal. Chem., 2017, 409(30), 7121–7131 CrossRef CAS PubMed.
- Y. Giesen, C. Monse, F. Nurnberger, B. Maybaum, C. Hagemann, J. Poprizki, K. Pitzke, C. Monz, M. Kirchner, K. Timm and D. Breuer, Gefahrstoffe - Reinhalt. Luft, 2018, 78(4), 133–137 CAS.
- A. Cabal, S. Legrand, B. Van den Bril, K. Tote, K. Janssens and P. Van Espen, X-Ray Spectrom., 2017, 46(5), 461–466 CrossRef CAS.
- C. A. Bizzi, M. F. Pedrotti, J. S. Silva, J. S. Barin, J. A. Nobrega and E. M. M. Flores, J. Anal. At. Spectrom., 2017, 32(8), 1448–1466 RSC.
- K. Pitzke, D. Breuer, C. Hagemann, T. Schwank and C. Stolzenberg, Gefahrstoffe - Reinhalt. Luft, 2018, 78(1–2), 14–18 Search PubMed.
- K. Pitzke, K. Gusbeth, R. Hebisch, M. Kirchner, C. Schuh, T. Schwank, R. Sonnenburg, K. Timm and D. Breuer, Gefahrstoffe - Reinhalt. Luft, 2018, 78(4), 138–150 CAS.
- B. Berlinger and M. Harper, J. Occup. Environ. Hyg., 2018, 15(2), 152–156 CrossRef PubMed.
- M. Dufka and B. Docekal, J. Anal. Methods Chem., 2018, 9698710 Search PubMed.
- J. A. H. Alpofead, C. M. Davidson and D. Littlejohn, Anal. Bioanal. Chem., 2017, 409(12), 3165–3174 CrossRef CAS PubMed.
- S. S. Fick, F. V. Nakadi, F. Fujiwara, P. Smichowski, M. G. R. Vale, B. Welz and J. B. de Andrade, J. Anal. At. Spectrom., 2018, 33(4), 593–602 RSC.
- J. L. Ambrose, Atmos. Meas. Tech., 2017, 10(12), 5063–5073 CrossRef.
- L. Zheng and P. Kulkarni, Anal. Chem., 2017, 89(12), 6551–6558 CrossRef CAS PubMed.
- L. N. Zheng, P. Kulkarni and P. Diwakar, Spectrochim. Acta, Part B, 2018, 144, 55–62 CrossRef CAS.
- L. Zheng, P. Kulkarni and D. D. Dionysiou, J. Anal. At. Spectrom., 2018, 33(3), 404–412 RSC.
- J. B. Sirven, P. Dewalle, C. Quere, V. Fauvet, M. Tabarant, S. Motellier, L. Golanski, A. Guiot, M. Amdaoud, S. Clavaguera, A. Roynette, S. Pontreau, F. X. Ouf and F. Gensdarmes, J. Anal. At. Spectrom., 2017, 32(10), 1868–1877 RSC.
- M. Tarik, D. Foppiano, A. Hess and C. Ludwig, J. Visualized Exp., 2017,(125), e55487 Search PubMed.
- Y. Suzuki, R. Ohara and K. Matsunaga, Spectrochim. Acta, Part B, 2017, 135, 82–90 CrossRef CAS.
- S. K. Mishra, B. Chattopadhyay, A. F. Kadjo and P. K. Dasgupta, Talanta, 2018, 177, 197–202 CrossRef CAS PubMed.
- B. T. Manard, C. D. Quarles, E. M. Wylie and N. Xu, J. Anal. At. Spectrom., 2017, 32(9), 1680–1687 RSC.
- A. Bazzano, K. Latruwe, M. Grotti and F. Vanhaecke, J. Anal. At. Spectrom., 2017, 32(5), 1004–1008 RSC.
- A. Drincic, J. Scancar, T. Zuliani, I. Nikolic and R. Milacic, J. Anal. At. Spectrom., 2017, 32(11), 2200–2209 RSC.
- D. Huang, X. Hua, G. L. Xiu, Y. J. Zheng, X. Y. Yu and Y. T. Long, Anal. Chim. Acta, 2017, 989, 1–14 CrossRef CAS PubMed.
- X. L. Shen, R. Ramisetty, C. Mohr, W. Huang, T. Leisner and H. Saathoff, Atmos. Meas. Tech., 2018, 11(4), 2325–2343 CrossRef.
- S. L. Lu, Z. Y. Tan, P. W. Liu, H. Zhao, D. Y. Liu, S. Yu, P. Cheng, M. S. Win, J. W. Hu, L. W. Tian, M. H. Wu, S. Yonemochi and Q. Y. Wang, Chemosphere, 2017, 186, 278–286 CrossRef CAS PubMed.
- J. P. Yan, L. Q. Chen, Q. Lin, S. H. Zhao and L. Li, J. Environ. Sci., 2018, 64, 252–263 CrossRef PubMed.
- H. Zhu, M. Liu, T. Guo, L. Xu, J. G. Dong, J. L. Feng, P. Cheng and Z. Zhou, J. Anal. At. Spectrom., 2018, 33(7), 1158–1167 RSC.
- M. Mekic, B. Temime-Roussel, A. Monod and R. S. Strekowski, Int. J. Mass Spectrom., 2018, 424, 10–15 CrossRef CAS.
- A. Ganeev, A. Gubal, B. Korotetski, O. Bogdanova, B. Burakov, A. Titova, N. Solovyev, N. Ivanenko, E. Drobyshev, E. Iakovleva and M. Sillanpaa, Microchem. J., 2017, 132, 286–292 CrossRef CAS.
- Y. S. Zhou, Y. Lu, M. M. Wang, L. Liu, X. Huang, L. J. Jiang, L. Jiang, J. F. Silvain and Y. F. Lu, J. Anal. At. Spectrom., 2017, 32(10), 1932–1937 RSC.
- J. Prost, P. Wobrauschek and C. Streli, X-Ray Spectrom., 2017, 46(5), 454–460 CrossRef CAS.
- M. Xu, Y. J. Chu, G. F. Wang, M. L. Qiu and L. Zheng, X-Ray Spectrom., 2018, 47(1), 79–85 CrossRef CAS.
- A. H. Tremper, A. Font, M. Priestman, S. H. Hamad, T. C. Chung, A. Pribadi, R. J. C. Brown, S. L. Goddard, N. Grassineau, K. Petterson, F. J. Kelly and D. C. Green, Atmos. Meas. Tech., 2018, 11(6), 3541–3557 CrossRef.
- R. J. C. Brown, S. Beccaceci, D. M. Butterfield, P. G. Quincey, P. M. Harris, T. Maggos, P. Panteliadis, A. John, A. Jedynska, T. A. J. Kuhlbusch, J. P. Putaud and A. Karanasiou, Environ. Sci.: Processes Impacts, 2017, 19(10), 1249–1259 RSC.
- J. C. Chow, J. G. Watson, M. C. Green, X. L. Wang, L. W. A. Chen, D. L. Trimble, P. M. Cropper, S. D. Kohl and S. B. Gronstal, J. Air Waste Manage. Assoc., 2018, 68(5), 494–510 CrossRef CAS PubMed.
- H. A. Azeem, J. Martinsson, K. E. Stenstrom, E. Swietlicki and M. Sandahl, Anal. Bioanal. Chem., 2017, 409(17), 4293–4300 CrossRef PubMed.
- M. K. Lappi and J. M. Ristimaki, J. Air Waste Manage. Assoc., 2017, 67(12), 1298–1318 CrossRef CAS PubMed.
- E. M. G. Nicolosi, P. Quincey, A. Font and G. W. Fuller, Atmos. Environ., 2018, 175, 145–153 CrossRef CAS.
- G. M. Riggio, J. C. Chow, P. M. Cropper, X. L. Wang, R. L. N. Yatavelli, X. F. Yang and J. G. Watson, J. Air Waste Manage. Assoc., 2018, 68(5), 463–476 CrossRef CAS PubMed.
- A. Vlachou, K. R. Daellenbach, C. Bozzetti, B. Chazeau, G. A. Salazar, S. Szidat, J. L. Jaffrezo, C. Hueglin, U. Baltensperger, I. El Haddad and A. S. H. Prevot, Atmos. Chem. Phys., 2018, 18(9), 6187–6206 CrossRef CAS.
- S. Wagner, J. Brandes, A. I. Goranov, T. W. Drake, R. G. M. Spencer and A. Stubbins, Limnol. Oceanogr.: Methods, 2017, 15(12), 995–1006 CrossRef CAS.
- A. L. Miller, A. T. Weakley, P. R. Griffiths, E. G. Cauda and S. Bayman, Appl. Spectrosc., 2017, 71(5), 1014–1024 CrossRef CAS PubMed.
- L. N. Zheng, P. Kulkarni, M. E. Birch, K. Ashley and S. J. Wei, Anal. Chem., 2018, 90(10), 6229–6239 CrossRef CAS PubMed.
- S. J. Wei, P. Kulkarni, K. Ashley and L. N. Zheng, Sci. Rep., 2017, 7(1), 13860 CrossRef PubMed.
- E. Ibraim, E. Harris, S. Eyer, B. Tuzson, L. Emmenegger, J. Six and J. Mohn, Isot. Environ. Health Stud., 2018, 54(1), 1–15 CrossRef CAS PubMed.
- P. M. Hundt, M. Muller, M. Mangold, B. Tuzson, P. Scheidegger, H. Looser, C. Huglin and L. Emmenegger, Atmos. Meas. Tech., 2018, 11(5), 2669–2681 CrossRef.
- J. W. Birks, P. C. Andersen, C. J. Williford, A. A. Turnipseed, S. E. Strunk, C. A. Ennis and E. Mattson, Atmos. Meas. Tech., 2018, 11(5), 2821–2835 CrossRef.
- M. He, L. J. Huang, B. S. Zhao, B. B. Chen and B. Hu, Anal. Chim. Acta, 2017, 973, 1–24 CrossRef CAS PubMed.
- A. S. A. Ibrahim, R. Al-Farawati, U. Hawas and Y. Shaban, Open Chem., 2017, 15(1), 103–122 Search PubMed.
- A. Pires Santos, M. das Graças Andrade Korn and V. Azevedo Lemos, Environ. Monit. Assess., 2017, 189(9), 444 CrossRef PubMed.
- N. Phattamajintatamrong, M. Puanngam and A. Imyim, Curr. Anal. Chem., 2017, 13(2), 137–143 CrossRef.
- M. Firat, S. Bakirdere, S. Sel, D. S. Chormey, O. Elkiran, F. Erulas and F. Turak, Int. J. Environ. Anal. Chem., 2017, 97(7), 684–693 CrossRef CAS.
- G. Gilmartin and D. Gingrich, J. Chromatogr. B: Anal. Technol. Biomed. Life Sci., 2018, 1083, 20–27 CrossRef CAS PubMed.
- L. H. Liu, A. F. Liu, Q. H. Zhang, J. B. Shi, B. He, Z. J. Yun and G. B. Jiang, J. Chromatogr. A, 2017, 1497, 81–86 CrossRef CAS PubMed.
- E. Grygo-Szymanko, A. Tobiasz, N. Miliszkiewicz, D. Dudek-Adamska and S. Walas, Anal. Lett., 2017, 50(13), 2147–2160 CrossRef CAS.
- R. Serbin, Y. Bazel and S. Ruzickova, Talanta, 2017, 175, 46–52 CrossRef CAS PubMed.
- J. Teran-Baamonde, S. Bouchet, E. Tessier and D. Amouroux, J. Chromatogr. A, 2018, 1547, 77–85 CrossRef CAS PubMed.
- C. Zhao, X. Zhang, Z. H. Sun, G. Yang, Y. H. Zhu, B. Si and H. L. Zheng, Spectrosc. Spectral Anal., 2017, 37(4), 1021–1026 CAS.
- I. Hagarova, Anal. Methods, 2017, 9(24), 3594–3601 RSC.
- M. Bundschuh, J. Filser, S. Lüderwald, M. S. McKee, G. Metreveli, G. E. Schaumann, R. Schulz and S. Wagner, Environ. Sci. Eur., 2018, 30(6) DOI:10.1186/512302-018-0132-6.
- R. Aznar, F. Barahona, O. Geiss, J. Ponti, T. J. Luis and J. Barrero-Moreno, Talanta, 2017, 175, 200–208 CrossRef CAS PubMed.
- L. Luo, Y. Yang, H. P. Li, R. Ding, Q. Wang and Z. G. Yang, Sci. Total Environ., 2018, 612, 1215–1222 CrossRef CAS PubMed.
- E. Zambrzycka-Szelewa, M. Lulewicz and B. Godlewska-Zylkiewicz, Spectrochim. Acta, Part B, 2017, 133, 81–87 CrossRef CAS.
- F. Cacho, L. Machynak, M. Nemecek and E. Beinrohr, Spectrochim. Acta, Part B, 2018, 144, 63–67 CrossRef CAS.
- L. W. Liu, H. L. Zheng, B. C. Xu, L. Xiao, C. G. Yong and Y. L. Zhangluo, Talanta, 2018, 179, 86–91 CrossRef CAS PubMed.
- L. Liu, X. A. Yang, X. P. Lu and W. B. Zhang, Talanta, 2017, 171, 90–100 CrossRef CAS PubMed.
- J. Luo, F. J. Xu, J. P. Tu, X. Wu and X. D. Hou, Microchem. J., 2017, 132, 245–250 CrossRef CAS.
- C. H. Lin, Y. Chen, Y. A. Su, Y. T. Luo, T. T. Shih and Y. C. Sun, Anal. Chem., 2017, 89(11), 5892–5900 Search PubMed.
- D. Martinez, D. Torregrosa, G. Grindlay, L. Gras and J. Mora, Talanta, 2018, 176, 374–381 CrossRef CAS PubMed.
- B. Wen, J. W. Zhou, A. G. Zhou, C. F. Liu and L. G. Li, Int. Biodeterior. Biodegrad., 2018, 128, 109–116 CrossRef CAS.
- B. P. Jackson, J. Anal. At. Spectrom., 2018, 33(5), 897–900 RSC.
- N. L. A. Jamari, J. F. Dohmann, A. Raab, E. M. Krupp and J. Feldmann, J. Anal. At. Spectrom., 2017, 32(5), 942–950 RSC.
- J. X. Qiao and Y. H. Xu, Talanta, 2018, 183, 18–23 CrossRef CAS PubMed.
- K. V. Baskaran, G. Blanchet-Chouinard and D. Lariviere, J. Anal. At. Spectrom., 2018, 33(4), 603–612 RSC.
- E. M. van Es, B. C. Russell, P. Ivanov and D. Read, Appl. Radiat. Isot., 2017, 126, 31–34 CrossRef CAS PubMed.
- Z. Abrego, N. Unceta, A. Sanchez, A. Gomez-Caballero, L. M. Berrio-Ochoa, M. A. Goicolea and R. J. Barrio, Analyst, 2017, 142(7), 1157–1164 RSC.
- D. Della Lunga, W. Muller, S. O. Rasmussen, A. Svensson and P. Vallelonga, Cryosphere, 2017, 11(3), 1297–1309 CrossRef.
- M. Dronov, T. Koza, A. Schwiers, T. C. Schmidt and J. Schram, Rapid Commun. Mass Spectrom., 2018, 32(2), 149–158 CrossRef CAS PubMed.
- H. G. M. Eggenkamp and P. Louvat, Rapid Commun. Mass Spectrom., 2018, 32(8), 612–618 CrossRef CAS PubMed.
- T. L. Yu, B. S. Wang, C. C. Shen, P. L. Wang, T. F. Yang, G. S. Burr and Y. G. Chen, Anal. Chim. Acta, 2017, 988, 34–40 CrossRef CAS PubMed.
- D. Bolek, S. U. Yesiller and S. Yalcin, Anal. Lett., 2018, 51(10), 1605–1621 CrossRef CAS.
- T. T. Gan, N. J. Zhao, G. F. Yin, X. Xiao, Y. J. Zhang, W. Zhang, J. G. Liu and W. G. Liu, Spectrosc. Spectral Anal., 2017, 37(6), 1912–1918 CAS.
- K. Sanyal and N. L. Misra, J. Anal. At. Spectrom., 2018, 33(5), 876–882 RSC.
- F. Oliva, M. Peros and A. Viau, Prog. Phys. Geogr., 2017, 41(2), 171–190 CrossRef.
- G. Heltai, Z. Gyori, I. Fekete, G. Halasz, K. Kovacs, A. Takacs, N. Boros and M. Horvath, Microchem. J., 2018, 136, 85–93 CrossRef CAS.
- I. Herath, M. Vithanage, S. Seneweera and J. Bundschuh, Environ. Int., 2018, 115, 370–386 CrossRef CAS PubMed.
- W. A. Maher, F. Krikowa, S. D. Foster, M. J. Ellwood and W. W. Bennett, J. Anal. At. Spectrom., 2018, 33(5), 706–712 RSC.
- P. Smichowski and A. Londonio, Microchem. J., 2018, 136, 113–120 CrossRef CAS.
- A. J. R. Bauer and S. G. Buckley, Appl. Spectrosc., 2017, 71(4), 553–566 CrossRef CAS PubMed.
- G. S. Senesi, L. Martin-Neto, P. R. Villas-Boas, G. Nicolodelli and D. Milori, J. Soils Sediments, 2018, 18(4), 1292–1302 CrossRef CAS.
- B. Busser, S. Moncayo, J. L. Coll, L. Sancey and V. Motto-Ros, Coord. Chem. Rev., 2018, 358, 70–79 CrossRef CAS.
- D. Pozebon, G. L. Scheffler and V. L. Dressler, J. Anal. At. Spectrom., 2017, 32(5), 890–919 RSC.
- N. Shalev, J. Farkas, J. Fietzke, M. Novak, J. A. Schuessler, P. A. P. von Strandmann and P. B. Torber, Geostand. Geoanal. Res., 2018, 42(2), 205–221 CrossRef CAS.
- J. Olson, M. Adamic, D. Snyder, J. Brookhart, P. Hahn and M. Watrous, Appl. Radiat. Isot., 2017, 126, 54–57 CrossRef CAS PubMed.
- L. Benedik and M. Trdin, Appl. Radiat. Isot., 2017, 126, 208–213 CrossRef CAS PubMed.
- C. Delvigne, B. Angeletti, A. Guihou, I. Basile-Doelsch and J. D. Meunier, Geostand. Geoanal. Res., 2018, 42(1), 139–149 CrossRef CAS.
- M. Shen, L. Y. Chen, W. L. Han and A. D. Ma, Microchem. J., 2018, 139, 295–300 CrossRef CAS.
- T. Karak, F. R. Kutu, J. R. Nath, I. Sonar, R. K. Paul, R. K. Boruah, S. Sanyal, S. Sabhapondit and A. K. Dutta, Crit. Rev. Food Sci. Nutr., 2017, 57(14), 2996–3034 CrossRef CAS PubMed.
- J. An, M. J. Trujillo-Rodriguez, V. Pino and J. L. Anderson, J. Chromatogr. A, 2017, 1500, 1–23 CrossRef CAS PubMed.
- E. Duborska and M. Bujdos, Chem. Listy, 2018, 112(4), 222–226 CAS.
- L. G. R. Albuquerque, A. M. Fioroto, T. Paixao and P. V. Oliveira, Commun. Soil Sci. Plant Anal., 2017, 48(18), 2210–2217 CrossRef CAS.
- C. N. Toth, S. Harangi, A. Karolyi, I. Fabian and E. Baranyai, Stud. Univ. Babes-Bolyai, Chem., 2017, 62(4), 483–494 CrossRef.
- D. Zhu, X. Ke, L. H. Wu, Y. J. Huang, P. Christie and Y. M. Luo, Pedosphere, 2017, 27(3), 491–501 CrossRef.
- M. Truskolaska and K. Jankowski, Int. J. Environ. Anal. Chem., 2018, 98(4), 295–307 CrossRef CAS.
- H. Celik, M. A. Turan, B. B. Asik and A. V. Katkat, Commun. Soil Sci. Plant Anal., 2017, 48(21), 2573–2581 CrossRef CAS.
- S. Popovic, A. Pantelic, Z. Milovanovic, J. Milinkov and M. Vidovic, Anal. Lett., 2017, 50(16), 2619–2633 CrossRef CAS.
- V. C. Costa, E. G. P. Silva, D. C. Lima, M. Franco, R. M. Jesus, M. A. Bezerra and F. A. C. Amorim, J. Braz. Chem. Soc., 2018, 29(6), 1189–1198 CAS.
- A. F. de Oliveira, C. S. da Silva, S. R. Bianchi and A. R. A. Nogueira, J. Food Compos. Anal., 2018, 66, 7–12 CrossRef.
- T. L. Marques and J. A. Nobrega, Microchem. J., 2017, 134, 27–34 CrossRef CAS.
- D. Adamczyk-Szabela, P. Anielak and W. M. Wolf, J. Anal. Methods Chem., 2017, 6947376 Search PubMed.
- H. Banning, M. Stelling, S. Konig, R. Schonberg and T. Neumann, PLoS One, 2018, 13(3), e0193826 CrossRef PubMed.
- Y. Q. Deng, E. J. Petersen, K. E. Challis, S. A. Rabb, R. D. Holbrook, J. F. Ranville, B. C. Nelson and B. S. Xing, Environ. Sci. Technol., 2017, 51(18), 10615–10623 CrossRef CAS PubMed.
- L. S. F. Pereira, M. S. P. Enders, G. D. Iop, P. A. Mello and E. M. M. Flores, J. Anal. At. Spectrom., 2018, 33(4), 649–657 RSC.
- L. S. F. Pereira, M. F. Pedrotti, P. D. Vecchia, J. S. F. Pereira and E. M. M. Flores, Anal. Chim. Acta, 2018, 1010, 29–36 CrossRef CAS PubMed.
- G. Yang, H. Tazoe and M. Yamada, Anal. Chim. Acta, 2018, 1008, 66–73 CrossRef CAS PubMed.
- W. A. Maher, S. Eggins, F. Krikowa, R. Jagtap and S. Foster, J. Anal. At. Spectrom., 2017, 32(6), 1129–1134 RSC.
- P. L. Mahanta, A. K. Singh, R. Radhamani and D. P. Rao, At. Spectrosc., 2017, 38(4), 99–105 CAS.
- A. Habibi, N. Cariou, B. Boulet, C. Cossonnet, R. Gurriaran, M. Gleizes, G. Cote and D. Lariviere, J. Radioanal. Nucl. Chem., 2017, 314(1), 127–139 CrossRef CAS.
- M. Y. Luo, S. Xing, Y. G. Yang, L. J. Song, Y. Ma, Y. D. Wang, X. X. Dai and S. Happel, J. Environ. Radioact., 2018, 187, 73–80 CrossRef CAS PubMed.
- E. M. Kroukamp, T. W. Godeto and P. B. C. Forbes, Environ. Monit. Assess., 2017, 189(9), 451 CrossRef CAS PubMed.
- R. S. Dybczynski, Z. Samczynski, I. Bartosiewicz, K. Kulisa, H. Polkowska-Motrenko, M. Pyszynska and I. Zuba, Nukleonika, 2017, 62(3), 199–211 CAS.
- N. Zhang, Z. X. Li, J. B. Zheng, X. M. Yang, K. Shen, T. K. Zhou and Y. Zhang, Microchem. J., 2017, 134, 68–77 CrossRef CAS.
- N. Zhang, K. Shen, X. M. Yang, Z. X. Li, T. K. Zhou, Y. Zhang, Q. L. Sheng and J. B. Zheng, Food Chem., 2018, 264, 462–470 CrossRef CAS PubMed.
- Y. Y. Ni, J. Zheng, Q. J. Guo, H. Wang, K. Tagami and S. Uchida, J. Radioanal. Nucl. Chem., 2018, 315(3), 643–651 CrossRef CAS.
- T. Ren, X. Chen, Y. Ge, L. J. Zhao and R. G. Zhong, Anal. Methods, 2017, 9(27), 4033–4043 RSC.
- L. Husakova, I. Urbanova, M. Safrankova and T. Sidova, Talanta, 2017, 175, 93–100 CrossRef CAS PubMed.
- C. Waterlot, Anal. Chim. Acta, 2018, 1003, 26–33 CrossRef CAS PubMed.
- C. Buyukpinar, E. Maltepe, D. S. Chormey, N. San and S. Bakirdere, Microchem. J., 2017, 132, 167–171 CrossRef CAS.
- C. M. M. Santos, M. A. G. Nunes, A. B. Costa, D. Pozebon, F. A. Duarte and V. L. Dressler, Anal. Methods, 2017, 9(23), 3497–3504 RSC.
- A. Al Hejami and D. Beauchemin, J. Anal. At. Spectrom., 2018, 33(5), 738–744 RSC.
- G. M. Thyssen, M. Holtkamp, H. Kaulfurst-Soboll, C. A. Wehe, M. Sperling, A. von Schaewen and U. Karst, Metallomics, 2017, 9(6), 676–684 RSC.
- J. Chirinos, D. Oropeza, J. Gonzalez, V. Zorba and R. E. Russo, Appl. Spectrosc., 2017, 71(4), 709–720 CrossRef CAS PubMed.
- K. Ido, T. Ariga, Y. B. Zhu, A. Hokura and K. Inagaki, Bunseki Kagaku, 2018, 67(1), 19–25 CrossRef CAS.
- C. B. Williams, T. G. Wittmann, T. McSweeney, P. Elliott, B. T. Jones and G. L. Donati, Microchem. J., 2017, 132, 15–19 CrossRef CAS.
- N. P. Zaksas and A. F. Veryaskin, Anal. Sci., 2017, 33(5), 605–609 CrossRef CAS PubMed.
- L. A. Mendes, M. W. Franco, F. A. R. Barbosa, P. I. A. de Carvalho, J. C. de Lena and C. C. Windmoller, Anal. Methods, 2018, 10(1), 91–100 RSC.
- M. Jimenez-Moreno, M. A. Lominchar, M. J. Sierra, R. Millan and R. C. R. Martin-Doimeadios, Talanta, 2018, 176, 102–107 CrossRef CAS PubMed.
- A. Virgilio, J. A. Nobrega and G. L. Donati, Anal. Bioanal. Chem., 2018, 410(3), 1157–1162 CrossRef CAS PubMed.
- C. B. Williams and G. L. Donati, J. Anal. At. Spectrom., 2018, 33(5), 762–767 RSC.
- X. Liu, Z. L. Zhu, H. L. Li, D. He, Y. T. Li, H. T. Zheng, Y. Q. Gan, Y. X. Li, N. S. Belshaw and S. H. Hu, Anal. Chem., 2017, 89(12), 6827–6833 CrossRef CAS PubMed.
- H. Y. He, X. H. Peng, Y. Yu, Z. M. Shi, M. Xu, S. J. No and Y. Gao, Anal. Chem., 2018, 90(9), 5737–5743 CrossRef CAS PubMed.
- J. Hu, R. E. Sturgeon, K. Nadeau, X. D. Hou, C. B. Zheng and L. Yang, Anal. Chem., 2018, 90(6), 4112–4118 CrossRef CAS PubMed.
- Y. Zakon, Z. Ronen, L. Halicz and F. Gelman, Chemosphere, 2017, 184, 192–196 CrossRef CAS PubMed.
- J. F. Chen, F. F. Fu, S. Y. Wu, J. Wang and Z. W. Wang, J. Sep. Sci., 2017, 40(19), 3898–3904 CrossRef CAS PubMed.
- S. Faucher, G. Charron, E. Lutzen, P. Le Coustumer, D. Schaumloffel, Y. Sivry and G. Lespes, Anal. Chim. Acta, 2018, 1028, 104–112 CrossRef CAS PubMed.
- D. Malinovsky and N. A. Kashulin, Anal. Methods, 2018, 10(1), 131–137 RSC.
- J. DeBord, A. Pourmand, S. C. Jantzi, S. Panicker and J. Almirall, Inorg. Chim. Acta, 2017, 468, 294–299 CrossRef CAS.
- L. Fu, S. Y. Shi and X. Q. Chen, Spectrochim. Acta, Part B, 2017, 133, 34–39 CrossRef CAS.
- L. Fu, S. Y. Shi and X. Q. Chen, Food Chem., 2018, 245, 692–697 CrossRef CAS PubMed.
- P. Schonberg, C. Mokry, J. Runke, D. Schonenbach, N. Stobener, P. Thorle-Pospiech, N. Trautmann and T. Reich, Anal. Chem., 2017, 89(17), 9077–9082 CrossRef PubMed.
- J. A. Dunne, D. A. Richards and H. W. Chen, Talanta, 2017, 174, 347–356 CrossRef CAS PubMed.
- M. Franzmann, H. Bosco, L. Hamann, C. Walther and K. Wendt, J. Anal. At. Spectrom., 2018, 33(5), 730–737 RSC.
- M. Markiewicz-Keszycka, X. Cama-Moncunill, M. P. Casado-Gavalda, Y. Dixit, R. Cama-Moncunill, P. J. Cullen and C. Sullivan, Trends Food Sci. Technol., 2017, 65, 80–93 CrossRef CAS.
- X. L. Yu and Y. He, Appl. Spectrosc. Rev., 2017, 52(7), 605–622 CrossRef.
- J. Guezenoc, L. Bassel, A. Gallet-Budynek and B. Bousquet, Spectrochim. Acta, Part B, 2017, 134, 6–10 CrossRef CAS.
- F. J. Duan, X. Fu, J. J. Jiang, T. T. Huang, L. Ma and C. Zhang, Spectrochim. Acta, Part B, 2018, 143, 12–17 CrossRef CAS.
- L. R. Xiang, Z. H. Ma, X. Y. Zhao, F. Liu, Y. He and L. Feng, Spectrosc. Spectral Anal., 2017, 37(12), 3871–3876 Search PubMed.
- X. Fu, F. J. Duan, T. T. Huang, L. Ma, J. J. Jiang and Y. C. Li, J. Anal. At. Spectrom., 2017, 32(6), 1166–1176 RSC.
- K. Q. Yu, Y. R. Zhao, F. Liu and Y. He, Spectrosc. Spectral Anal., 2017, 37(9), 2879–2884 CAS.
- H. Y. Zhang, Q. B. Zhu, M. Huang and Y. Guo, Int. J. Agric. Biol. Eng., 2018, 11(3), 154–158 Search PubMed.
- M. Y. Yao, H. Yang, L. Huang, T. B. Chen, G. F. Rao and M. H. Liu, Appl. Optic., 2017, 56(14), 4070–4075 CrossRef PubMed.
- C. P. de Morais, A. I. Barros, D. Junior, C. A. Ribeiro, M. S. Crespi, G. S. Senesi, J. A. G. Neto and E. C. Ferreira, Microchem. J., 2017, 134, 370–373 CrossRef.
- J. Yongcheng, S. Wen, Z. Baohua and L. Dong, J. Appl. Spectrosc., 2017, 84(4), 731–737 CrossRef CAS.
- S. M. Zaytsev, I. N. Krylov, A. M. Popov, N. B. Zorov and T. A. Labutin, Spectrochim. Acta, Part B, 2018, 140, 65–72 CrossRef CAS.
- I. Rehan, M. A. Gondal and K. Rehan, Talanta, 2018, 182, 443–449 CrossRef CAS PubMed.
- J. Wang, M. Shi, P. Zheng and S. Xue, J. Appl. Spectrosc., 2017, 84(1), 188–193 CrossRef CAS.
- L. Fang, N. J. Zhao, M. J. Ma, D. S. Meng, Y. H. Gu, Y. Jia, W. Q. Liu and J. G. Liu, Spectrosc. Spectral Anal., 2017, 37(10), 3274–3280 CAS.
- Y. H. Gu, N. J. Zhao, M. J. Ma, D. S. Meng, Y. Jia, L. Fang, J. G. Liu and W. Q. Liu, Spectrosc. Spectral Anal., 2018, 38(3), 982–989 CAS.
- I. Rehan, K. Rehan, S. Sultana, M. Z. Khan and R. Muhammad, Appl. Phys. B, 2018, 124(5), 76 CrossRef.
- J. Y. Peng, F. Liu, T. T. Shen, L. H. Ye, W. W. Kong, W. Wang, X. D. Liu and Y. He, Sensors, 2018, 18(2), E621 CrossRef PubMed.
- G. Nicolodelli, G. S. Senesi, R. A. Romano, J. Cabral, I. L. O. Perazzoli, B. S. Marangoni, P. R. Villas-Boas and D. Milori, Appl. Phys. B, 2017, 123(4), 127 CrossRef.
- G. Kim, Y. J. Yoon, H. A. Kim, H. J. Cho and K. Park, Spectrochim. Acta, Part B, 2017, 134, 17–24 CrossRef CAS.
- M. Ramli, A. Khumaeni, K. H. Kurniawan, M. O. Tjia and K. Kagawa, Spectrochim. Acta, Part B, 2017, 132, 8–12 CrossRef CAS.
- S. N. Xu, R. Jiang, R. B. Ning, Q. Li and C. X. Li, Spectrosc. Spectral Anal., 2017, 37(10), 3204–3208 CAS.
- S. Xu, W. Duan, R. Ning, Q. Li and R. Jiang, J. Appl. Spectrosc., 2017, 84(1), 35–39 CrossRef CAS.
- L. Krajcarova, K. Novotny, M. Kummerova, J. Dubova, V. Gloser and J. Kaiser, Talanta, 2017, 173, 28–35 CrossRef CAS PubMed.
- A. S. Zakuskin, A. M. Popov, S. M. Zaytsev, N. B. Zorov, M. V. Belkov and T. A. Labutin, J. Appl. Spectrosc., 2017, 84(2), 319–323 CrossRef CAS.
- A. Khumaeni, M. Tani, K. Kurihara, K. Kagawa and H. Niki, Anal. Lett., 2017, 50(12), 1992–1999 CrossRef CAS.
- A. Khumaeni, H. Sugito, W. S. Budi and A. Y. Wardaya, J. Appl. Spectrosc., 2018, 84(6), 1108–1113 CrossRef CAS.
- S. F. O. de Vallejuelo, A. Gredilla, L. Gomez-Nubla, E. Ruiz-Romera, A. Zabaleta and J. M. Madariaga, Microchem. J., 2017, 135, 171–179 CrossRef.
- D. S. Meng, N. J. Zhao, M. J. Ma, L. Fang, Y. H. Gu, Y. Jia, J. G. Liu and W. Q. Liu, Appl. Optic., 2017, 56(18), 5204–5210 CrossRef PubMed.
- C. R. Ytsma and M. D. Dyar, Spectrochim. Acta, Part B, 2018, 139, 27–37 CrossRef CAS.
- M. Pena-Icart, M. S. Pomares-Alfonso, F. W. B. de Aquino, C. Alonso-Hernandez, Y. Bolanos-Alvarez and E. R. Pereira, Anal. Methods, 2017, 9(24), 3713–3719 RSC.
- A. C. Ranulfi, R. A. Romano, A. B. Magalhaes, E. J. Ferreira, P. R. Villas-Boas and D. Milori, Appl. Spectrosc., 2017, 71(7), 1471–1480 CrossRef CAS PubMed.
- Z. H. Lu, R. P. Chen, L. Z. Ma, H. F. Sun, S. Wang, K. K. Chang, Q. Q. Guo, X. H. Feng and J. D. Hu, Spectrosc. Spectral Anal., 2017, 37(10), 3214–3219 CAS.
- J. M. Wang, S. W. Xue, P. C. Zheng, Y. Y. Chen and R. Peng, Anal. Lett., 2017, 50(12), 2000–2011 CrossRef CAS.
- X. L. Yu, J. Y. Peng, F. Liu and Y. He, Spectrosc. Spectral Anal., 2017, 37(6), 1908–1911 CAS.
- M. Z. Martin, D. C. Glasgow, T. J. Tschaplinski, G. A. Tuskan, L. E. Gunter, N. L. Engle, A. M. Wymore and D. J. Weston, Spectrochim. Acta, Part B, 2017, 138, 46–53 CrossRef CAS.
- J. E. Favorito, T. P. Luxton, M. J. Eick and P. R. Grossl, Environ. Pollut., 2017, 229, 911–921 CrossRef CAS PubMed.
- A. A. Shaltout, E. S. Abdel-Hameed, M. Salman, P. Kregsamer, P. Wobrauschek and C. Streli, Anal. Lett., 2018, 51(9), 1433–1444 CrossRef CAS.
- R. Dalipi, L. Borgese, K. Tsuji, E. Bontempi and L. E. Depero, J. Food Compos. Anal., 2018, 67, 128–134 CrossRef CAS.
- F. Bilo, L. Borgese, R. Dalipi, A. Zacco, S. Federici, M. Masperi, P. Leonesio, E. Bontempi and L. E. Depero, Chemosphere, 2017, 178, 504–512 CrossRef CAS PubMed.
- R. Ndimba, K. Cloete, L. Mehlo, J. Kossmann, C. Mtshali and C. Pineda-Vargas, Nucl. Instrum. Methods Phys. Res., Sect. B, 2017, 404, 121–124 CrossRef CAS.
- P. Nayak, S. S. Ram and C. Kalidass, X-Ray Spectrom., 2017, 46(6), 529–536 CrossRef.
- O. Lukacovicova and P. Mikus, Chem. Listy, 2018, 112(4), 242–245 CAS.
- A. van der Ent, W. J. Przybylowicz, M. D. de Jonge, H. H. Harris, C. G. Ryan, G. Tylko, D. J. Paterson, A. D. Barnabas, P. M. Kopittke and J. Mesjasz-Przybylowicz, New Phytol., 2018, 218(2), 432–452 CrossRef CAS PubMed.
- U. E. A. Fittschen, H. H. Kunz, R. Hohner, I. M. B. Tyssebotn and A. Fittschen, X-Ray Spectrom., 2017, 46(5), 374–381 CrossRef CAS.
- J. Yang, Y. D. Li, X. Y. Wang, X. Y. Zhang and X. Y. Lin, Nucl. Instrum. Methods Phys. Res., Sect. B, 2017, 401, 25–28 CrossRef CAS.
- L. Q. Luo, Y. T. Shen, Y. H. Ma, T. Xu, B. B. Chu, Y. Zeng and J. Liu, Spectrosc. Spectral Anal., 2017, 37(4), 1003–1008 CAS.
- A. Minnocci, A. Francini, S. Romeo, A. D. Sgrignuoli, G. Povero and L. Sebastiani, Sci. Hortic., 2018, 231, 15–21 CrossRef CAS.
- V. M. Silva, E. H. M. Boleta, M. Lanza, J. Lavres, J. T. Martins, E. F. Santos, F. L. M. dos Santos, F. F. Putti, E. Furlani, P. J. White, M. R. Broadley, H. W. P. de Carvalho and A. R. dos Reis, Environ. Exp. Bot., 2018, 150, 172–182 CrossRef CAS.
- L. H. Yang, X. M. Zheng and W. Ye, Spectrosc. Spectral Anal., 2017, 37(6), 1955–1959 CAS.
- P. Cardoso, T. C. Mateus, G. Velu, R. P. Singh, J. P. Santos, M. L. Carvalho, V. M. Lourenco, F. Lidon, F. Reboredo and M. Guerra, Spectrochim. Acta, Part B, 2018, 141, 70–79 CrossRef CAS.
- B. T. Ribeiro, S. H. G. Silva, E. A. Silva and L. R. G. Guilherme, Cienc. Agrotecnol., 2017, 41(3), 245–254 CrossRef.
- S. H. G. Silva, E. A. Silva, G. C. Poggere, L. R. G. Guilherme and N. Curi, Cienc. Agrotecnol., 2018, 42(1), 80–92 CrossRef.
- R. Ravansari and L. D. Lemke, Geoderma, 2018, 319, 175–184 CrossRef CAS.
- K. Tian, B. Huang, Z. Xing and W. Y. Hu, Environ. Sci. Pollut. Res., 2018, 25(11), 11011–11022 CrossRef CAS PubMed.
- S. M. Kim and Y. Choi, Int. J. Environ. Res. Public Health, 2017, 14(6), 654 CrossRef PubMed.
- C. McGladdery, D. C. Weindorf, S. Chakraborty, B. Li, L. Paulette, D. Podar, D. Pearson, N. Y. O. Kusi and B. Duda, J. Environ. Manage., 2018, 210, 210–225 CrossRef CAS PubMed.
- E. Roth, T. L. Bank and E. Granite, Geostand. Geoanal. Res., 2018, 42(2), 263–269 CrossRef CAS.
- P. D. Rostron and M. H. Ramsey, Geostand. Geoanal. Res., 2017, 41(3), 459–473 CrossRef CAS.
- M. Tetland, J. Greenough, B. Fryer, M. Hinds and M. E. Shaheen, Geostand. Geoanal. Res., 2017, 41(4), 689–700 CrossRef CAS.
- Y. A. Liu, J. M. Brenan and C. J. Bray, Econ. Geol., 2017, 112(8), 2005–2021 CrossRef.
- M. Weber, F. Lugli, K. P. Jochum, A. Cipriani and D. Scholz, Geostand. Geoanal. Res., 2018, 42(1), 77–89 CrossRef CAS.
- G. O. Goncalves, C. Lana, R. Scholz, I. S. Buick, A. Gerdes, S. L. Kamo, F. Corfu, D. Rubatto, M. Wiedenbeck, H. A. Nalini and L. C. A. Oliveira, Geostand. Geoanal. Res., 2018, 42(1), 25–47 CrossRef CAS.
- M. M. Santos, C. Lana, R. Scholz, I. Buick, M. D. Schmitz, S. L. Kamo, A. Gerdes, F. Corfu, S. Tapster, P. Lancaster, C. D. Storey, M. A. Basei, E. Tohver, A. Alkmim, H. Nalini, K. Krambrock, C. Fantini and M. Wiedenbeck, Geostand. Geoanal. Res., 2017, 41(3), 335–358 CrossRef CAS.
- A. I. S. Kemp, J. D. Vervoort, K. E. Bjorkman and L. M. Iaccheri, Geostand. Geoanal. Res., 2017, 41(4), 659–673 CrossRef CAS.
- Y. J. Li, D. W. Zheng, Y. Wu, Y. Wang, H. Y. He, J. Z. Pang, Y. Z. Wang and J. X. Yu, Geostand. Geoanal. Res., 2017, 41(3), 359–365 CrossRef CAS.
- S. Piazolo, E. Belousova, A. La Fontaine, C. Corcoran and J. M. Cairney, Chem. Geol., 2017, 456, 10–18 CrossRef CAS.
- D. Szymanowski, M. A. Fehr, M. Guillong, M. A. Coble, J. F. Wotzlaw, L. Nasdala, B. Ellis, O. Bachmann and M. Schonbachler, Chem. Geol., 2018, 481, 146–154 CrossRef CAS.
- Y. S. He, Y. Wang, C. W. Zhu, S. C. Huang and S. G. Li, Geostand. Geoanal. Res., 2017, 41(2), 283–302 CrossRef CAS.
- F. Liu, H. L. Zhu, X. Li, G. Q. Wang and Z. F. Zhang, Geostand. Geoanal. Res., 2017, 41(4), 675–688 CrossRef CAS.
- J. Zhang, J. Li, X. P. Long, S. L. Sun, L. Yin and M. N. Dai, Geostand. Geoanal. Res., 2017, 41(4), 649–658 CrossRef CAS.
- J. Li, S. H. Tang, X. K. Zhu and C. X. Pan, Geostand. Geoanal. Res., 2017, 41(2), 255–262 CrossRef CAS.
- N. Nonose, T. Suzuki, K. C. Shin, T. Miura and A. Hioki, Anal. Chim. Acta, 2017, 974, 27–42 CrossRef CAS PubMed.
- H. P. Qi, T. B. Coplen, M. Gehre, T. W. Vennemann, W. A. Brand, H. Geilmann, G. Olack, I. N. Bindeman, J. Palandri, L. Huang and F. J. Longstaffe, Chem. Geol., 2017, 467, 89–99 CrossRef CAS.
- G. Siron, L. Baumgartner, A. S. Bouvier, B. Putlitz and T. Vennemann, Geostand. Geoanal. Res., 2017, 41(2), 243–253 CrossRef CAS.
- D. S. Xue, H. Y. Wang, Y. H. Liu, L. W. Xie and P. Shen, Geostand. Geoanal. Res., 2017, 41(3), 411–425 CrossRef CAS.
- S. N. H. Bokhari and T. C. Meisel, Geostand. Geoanal. Res., 2017, 41(2), 181–195 CrossRef CAS.
- R. Mathur, W. Powell, A. Mason, L. Godfrey, J. M. Yao and M. E. Baker, Geostand. Geoanal. Res., 2017, 41(4), 701–707 CrossRef CAS.
- M. J. O’Hara, C. M. Kellogg, C. M. Parker, S. S. Morrison, J. F. Corbey and J. W. Grate, Chem. Geol., 2017, 466, 341–351 CrossRef.
- J. W. Grate, J. J. Gonzalez, M. J. O’Hara, C. M. Kellogg, S. S. Morrison, D. W. Koppenaal, G. C. Y. Chan, X. L. Mao, V. Zorba and R. E. Russo, Analyst, 2017, 142(18), 3333–3340 RSC.
- M. Rospabe, M. Benoit and F. Candaudap, Geostand. Geoanal. Res., 2018, 42(1), 115–129 CrossRef CAS.
- A. Retzmann, T. Zimmermann, D. Profrock, T. Prohaska and J. Irrgeher, Anal. Bioanal. Chem., 2017, 409(23), 5463–5480 CrossRef CAS PubMed.
- H. L. Yuan, X. Liu, Z. A. Bao, K. Y. Chen and C. L. Zong, J. Earth Sci., 2018, 29(1), 223–229 CrossRef CAS.
- W. Zhang, Z. C. Hu, Y. S. Liu, W. W. Yang, H. H. Chen, S. H. Hu and H. Y. Xiao, Anal. Chim. Acta, 2017, 983, 149–159 CrossRef CAS PubMed.
- Z. A. Bao, L. Chen, C. L. Zong, H. L. Yuan, K. Y. Chen and M. N. Dai, Int. J. Mass Spectrom., 2017, 421, 255–262 CrossRef CAS.
- D. G. Reading, I. W. Croudace and P. E. Warwick, Anal. Chem., 2017, 89(11), 6006–6014 CrossRef CAS PubMed.
- M. Garcon, L. Sauzeat, R. W. Carlson, S. B. Shirey, M. Simon, V. Balter and M. Boyet, Geostand. Geoanal. Res., 2017, 41(3), 367–380 CrossRef CAS.
- K. L. Linge, L. P. Bedard, R. Bugoi, J. Enzweiler, K. P. Jochum, R. Kilian, J. G. Liu, J. Marin-Carbonne, S. Merchel, F. Munnik, L. F. G. Morales, C. Rollion-Bard, A. K. Souders, P. J. Sylvester and U. Weis, Geostand. Geoanal. Res., 2017, 41(4), 493–562 CrossRef.
- N. J. Cook, C. L. Ciobanu, K. Ehrig, A. Slattery, M. R. Verdugo-Ihl, L. Courtney-Davies and W. Y. Gao, Minerals, 2017, 7(12), 233 CrossRef.
- J. Aramendia, L. Gomez-Nubla, K. Castro, S. F. O. de Vallejuelo, G. Arana, M. Maguregui, V. G. Baonza, J. Medina, F. Rull and J. M. Madariaga, TrAC, Trends Anal. Chem., 2018, 98, 36–46 CrossRef CAS.
- E. Bolea-Fernandez, L. Balcaen, M. Resano and F. Vanhaecke, J. Anal. At. Spectrom., 2017, 32(9), 1660–1679 RSC.
- Y. M. Meng and R. Z. Hu, Anal. Lett., 2018, 51(5), 627–647 CrossRef CAS.
- S. K. Aggarwal and C. F. You, Mass Spectrom. Rev., 2017, 36(4), 499–519 CrossRef CAS PubMed.
- J. T. Sliwinski, M. Guillong, C. Liebske, I. Dunkl, A. von Quadt and O. Bachmann, Chem. Geol., 2017, 472, 8–21 CrossRef CAS.
- T. J. Ver Hoeve, J. S. Scoates, C. J. Wall, D. Weis and M. Amini, Chem. Geol., 2018, 483, 201–217 CrossRef CAS.
- J. M. Thompson, S. Meffre and L. Danyushevsky, J. Anal. At. Spectrom., 2018, 33(2), 221–230 RSC.
- D. Wiemer, C. M. Allen, D. T. Murphy and I. Kinaev, Chem. Geol., 2017, 466, 285–302 CrossRef CAS.
- M. Guitreau, N. Mora and J. L. Paquette, Geochem., Geophys., Geosyst., 2018, 19(1), 272–291 CrossRef CAS.
- P. Vermeesch, M. Rittner, E. Petrou, J. Omma, C. Mattinson and E. Garzanti, Geochem., Geophys., Geosyst., 2017, 18(11), 4096–4109 CrossRef CAS.
- L. W. Xie, J. H. Yang, Q. Z. Yin, Y. H. Yang, J. B. Liu and C. Huang, J. Anal. At. Spectrom., 2017, 32(5), 975–986 RSC.
- M. Burn, P. Lanari, T. Pettke and M. Engi, J. Anal. At. Spectrom., 2017, 32(7), 1359–1377 RSC.
- P. Hazarika, B. Mishra, M. K. Ozha and K. L. Pruseth, Chem. Erde-Geochem., 2017, 77(1), 69–79 CrossRef CAS.
- A. J. Cross and I. S. Williams, Chem. Geol., 2018, 484, 81–108 CrossRef CAS.
- L. A. Neymark, C. S. Holm-Denoma and R. J. Moscati, Chem. Geol., 2018, 483, 410–425 CrossRef CAS.
- T. Blackburn, N. McLean and S. A. Bowring, Geostand. Geoanal. Res., 2017, 41, 325–334 CrossRef CAS.
- B. S. Ellis, D. F. Mark, J. Troch, O. Bachmann, M. Guillong, A. J. R. Kent and A. von Quadt, Chem. Geol., 2017, 457, 15–23 CrossRef CAS.
- L. E. Morgan, M. Munk, B. Davidheiser-Kroll, N. H. Warner, S. Gupta, R. Slaybaugh, P. Harkness and D. F. Mark, Geostand. Geoanal. Res., 2017, 41(3), 381–396 CrossRef CAS.
- K. F. Oostingh, F. Jourdan, M. Danisik and N. J. Evans, Geochim. Cosmochim. Acta, 2017, 217, 193–218 CrossRef CAS.
- N. F. Pchelintseva and D. A. Bychkov, J. Anal. Chem., 2017, 72(6), 644–648 CrossRef CAS.
- Y. Makonnen, W. R. MacFarlane, M. L. Geageac and D. Beauchemin, J. Anal. At. Spectrom., 2017, 32(9), 1688–1696 RSC.
- K. H. Lepore, C. I. Fassett, E. A. Breves, S. Byrne, S. Giguere, T. Boucher, J. M. Rhodes, M. Vollinger, C. H. Anderson, R. W. Murray and M. D. Dyar, Appl. Spectrosc., 2017, 71(4), 600–626 CrossRef CAS PubMed.
- M. A. Speranca, M. S. Pomares-Alfonso and E. R. Pereira, Anal. Methods, 2018, 10(5), 533–540 RSC.
- K. Rifai, M. Laflamme, M. Constantin, F. Vidal, M. Sabsabi, A. Blouin, P. Bouchard, K. Fytas, M. Castello and B. N. Kamwa, Spectrochim. Acta, Part B, 2017, 134, 33–41 CrossRef CAS.
- D. Diaz, D. W. Hahn and A. Molina, Spectrochim. Acta, Part B, 2017, 136, 106–115 CrossRef CAS.
- J. O. Cáceres, F. Pelascini, V. Motto-Ros, S. Moncayo, F. Trichard, G. Panczer, A. Marin-Roldan, J. A. Cruz, I. Coronado and J. Martin-Chivelet, Sci. Rep., 2017, 7, 11, DOI:10.1038/s41598-017-05437-3.
- N. Hausmann, P. Siozos, A. Lemonis, A. C. Colonese, H. K. Robson and D. Anglos, J. Anal. At. Spectrom., 2017, 32(8), 1467–1472 RSC.
- S. Moncayo, L. Duponchel, N. Mousavipak, G. Panczer, F. Trichard, B. Bousquet, F. Pelascini and V. Motto-Ros, J. Anal. At. Spectrom., 2018, 33(2), 210–220 RSC.
- T. Yokoyama, Y. Nagai, Y. Hinohara and T. Mori, Geostand. Geoanal. Res., 2017, 41(2), 221–242 CrossRef CAS.
- D. Y. Tao, W. Guo, W. K. Xie, L. L. Jin, Q. H. Guo and S. H. Hu, Microchem. J., 2017, 135, 221–225 CrossRef CAS.
- F. Poitrasson and F. X. d’Abzac, J. Anal. At. Spectrom., 2017, 32(6), 1075–1091 RSC.
- D. Evans and W. Muller, Geostand. Geoanal. Res., 2018, 42(2), 159–188 CrossRef CAS.
- D. Monticelli, D. Civati, B. Giussani, C. Dossi, D. Spanu and S. Recchia, Talanta, 2018, 179, 100–106 CrossRef CAS PubMed.
- Y. Bussweiler, O. Borovinskaya and M. Tanner, Spectroscopy, 2017, 32(5), 14–20 Search PubMed.
- M. Fox, A. Tripathy-Lang and D. L. Shuster, Chem. Geol., 2017, 467, 30–41 CrossRef CAS.
- J. A. Petrus, D. M. Chew, M. I. Leybourne and B. S. Kamber, Chem. Geol., 2017, 463, 76–93 CrossRef CAS.
- T. Fusswinkel, C. Giehl, O. Beermann, J. R. Fredriksson, D. Garbe-Schonberg, L. Scholten and T. Wagner, J. Anal. At. Spectrom., 2018, 33(5), 768–783 RSC.
- K. Schloglova, M. Walle and C. A. Heinrich, J. Anal. At. Spectrom., 2017, 32(5), 1052–1063 RSC.
- W. Jian, M. Albrecht, B. Lehmann, J. W. Mao, I. Horn, Y. H. Li, H. S. Ye, Z. Y. Li, G. G. Fang and Y. S. Xue, Geofluids, 2018, 3692180 Search PubMed.
- C. V. Stead, E. L. Tomlinson, B. S. Kamber, M. G. Babechuk and C. A. McKenna, Geostand. Geoanal. Res., 2017, 41(2), 197–212 CrossRef CAS.
- R. Funderburg, R. Arevalo, M. Locmelis and T. Adachi, J. Am. Soc. Mass Spectrom., 2017, 28(11), 2344–2351 CrossRef CAS PubMed.
- M. Weber, J. A. Wassenburg, K. P. Jochum, S. F. M. Breitenbach, J. Oster and D. Scholz, Chem. Geol., 2017, 468, 63–74 CrossRef CAS.
- W. Zhang, Z. C. Hu, Y. S. Liu, T. Wu, X. D. Deng, J. L. Guo and H. Zhao, Chem. Geol., 2018, 479, 10–21 CrossRef CAS.
- L. Zhang, Z. Y. Ren, Y. D. Wu and N. Li, Rapid Commun. Mass Spectrom., 2018, 32(2), 105–112 CrossRef CAS PubMed.
- A. Van Ham-Meert, S. M. Chernonozhkin, S. J. M. Van Malderen, T. Van Acker, F. Vanhaecke and P. Degryse, Geostand. Geoanal. Res., 2018, 42(2), 223–238 CrossRef CAS.
- A. J. Pietruszka and L. A. Neymark, J. Anal. At. Spectrom., 2017, 32(6), 1135–1154 RSC.
- A. M. Bauer and M. S. A. Horstwood, Chem. Geol., 2018, 476, 85–99 CrossRef CAS.
- C. M. Fisher, C. Paton, D. G. Pearson, C. Sarkar, Y. Luo, D. B. Tersmette and T. Chacko, Geochem., Geophys., Geosyst., 2017, 18(12), 4604–4622 CrossRef CAS.
- W. Chen, J. Lu, S. Y. Jiang, K. D. Zhao and D. F. Duan, Anal. Chem., 2017, 89(24), 13415–13421 CrossRef CAS PubMed.
- M. Garcon, M. Boyet, R. W. Carlson, M. F. Horan, D. Auclair and T. D. Mock, Chem. Geol., 2018, 476, 493–514 CrossRef CAS.
- I. Gautam, J. S. Ray, R. Bhutani, S. Balakrishnan and J. K. Dash, Chem. Geol., 2017, 466, 479–490 CrossRef CAS.
- H. L. Zhu, F. Liu, X. Li, Y. J. An, G. Q. Wang and Z. F. Zhang, J. Anal. At. Spectrom., 2018, 33(4), 547–554 RSC.
- S. Mondal and R. Chakrabarti, J. Anal. At. Spectrom., 2018, 33(1), 141–150 RSC.
- S. Di Salvo, E. Braschi, M. Casalini, S. Marchionni, T. Adani, M. Ulivi, A. Orlando, S. Tommasini, R. Avanzinelli, P. P. A. Mazza, S. Conticelli and L. Francalanci, J. Anal. Methods Chem., 2018, 1292954 Search PubMed.
- S. N. Shilobreeva, J. Anal. Chem., 2017, 72(14), 1355–1368 CrossRef CAS.
- M. H. Ramsey and M. Wiedenbeck, Geostand. Geoanal. Res., 2018, 42(1), 5–24 CrossRef.
- J. Davies, R. A. Stern, L. M. Heaman, D. E. Moser, E. L. Walton and T. Vennemann, Chem. Geol., 2018, 479, 113–122 CrossRef CAS.
- K. Shimizu, T. Ushikubo, M. Hamada, S. Itoh, Y. Higashi, E. Takahashi and M. Ito, Geochem. J., 2017, 51(4), 299–313 CrossRef CAS.
- J. Isa, I. E. Kohl, M. C. Liu, J. T. Wasson, E. D. Young and K. D. McKeegan, Chem. Geol., 2017, 458, 14–21 CrossRef CAS.
- M. G. Sliwinski, K. Kitajima, M. J. Spicuzza, I. J. Orland, A. Ishida, J. H. Fournelle and J. W. Valley, Geostand. Geoanal. Res., 2018, 42(1), 49–76 CrossRef CAS.
- P. A. Carr, M. D. Norman and V. C. Bennett, Chem. Geol., 2017, 467, 122–133 CrossRef CAS.
- A. Ishida, K. Kitajima, K. H. Williford, M. L. Tuite, T. Kakegawa and J. W. Valley, Geostand. Geoanal. Res., 2018, 42(2), 189–203 CrossRef CAS.
- C. Welte, L. Wacker, B. Hattendorf, M. Christl, J. Koch, C. Yeman, S. F. M. Breitenbach, H. A. Synal and D. Gunther, J. Anal. At. Spectrom., 2017, 32(9), 1813–1819 RSC.
- B. Lemiere, J. Geochem. Explor., 2018, 188, 350–363 CrossRef CAS.
- B. S. Andrew and S. L. L. Barker, Geochem.: Explor., Environ., Anal., 2018, 18(1), 85–93 CrossRef.
- N. Alov and P. Sharanov, Anal. Lett., 2018, 51(11), 1789–1795 CrossRef CAS.
- W. Nikonow and D. Rammlmair, Geosci. Instrum., Methods Data Syst., 2017, 6(2), 429–437 CrossRef.
- C. G. Ryan, R. Kirkham, G. F. Moorhead, D. Parry, M. Jensen, A. Faulks, S. Hogan, P. A. Dunn, R. Dodanwela, L. A. Fisher, M. Pearce, D. P. Siddons, A. Kuczewski, U. Lundstrom, A. Trolliet and N. Gao, J. Instrum., 2018, 13, C03020 CrossRef.
- M. El Ouahabi, G. Chene, D. Strivay, J. V. Auwera and A. Hubert-Ferrari, J. Anal. At. Spectrom., 2018, 33(5), 883–892 RSC.
- J. A. Mering and S. L. L. Barker, Anal. Chem., 2018, 90(4), 2852–2859 CrossRef CAS PubMed.
- T. Tolessa, X. X. Zhou, M. Amde and J. F. Liu, Talanta, 2017, 169, 91–97 CrossRef CAS PubMed.
- M. R. Neyestani, F. Shemirani, S. Mozaffari and M. Alvand, Microchim. Acta, 2017, 184(8), 2871–2879 CrossRef CAS.
- L. Fischer, G. Smith, S. Hann and K. W. Bruland, Mar. Chem., 2018, 199, 44–52 CrossRef CAS.
- E. Lukojko, E. Talik, A. Gagor and R. Sitko, Anal. Chim. Acta, 2018, 1008, 57–65 CrossRef CAS PubMed.
- A. Baranik, R. Sitko, A. Gagor, I. Queralt, E. Margui and B. Zawisza, Anal. Chem., 2018, 90(6), 4150–4159 CrossRef CAS PubMed.
- A. Baranik, A. Gagor, I. Queralt, E. Margui, R. Sitko and B. Zawisza, Talanta, 2018, 185, 264–274 CrossRef CAS PubMed.
- X. Y. Jia, D. R. Gong, J. Y. Zhao, H. Y. Ren, J. N. Wang and X. Zhang, Microchim. Acta, 2018, 185(4), 228 CrossRef PubMed.
- E. Nourbala-Tafti, V. Romero, I. Lavilla, S. Dadfarnia and C. Bendicho, Spectrochim. Acta, Part B, 2017, 131, 40–47 CrossRef CAS.
- S. L. Jackson, J. Spence, D. J. Janssen, A. R. S. Ross and J. T. Cullen, J. Anal. At. Spectrom., 2018, 33(2), 304–313 RSC.
- I. Rapp, C. Schlosser, D. Rusiecka, M. Gledhill and E. P. Achterberg, Anal. Chim. Acta, 2017, 976, 1–13 CrossRef CAS PubMed.
- S. Tiwari, N. Sharma and R. Saxena, New J. Chem., 2017, 41(12), 5034–5039 RSC.
- C. E. Dogan, Fresenius Environ. Bull., 2018, 27(4), 1989–1998 CAS.
- P. Janik, B. Zawisza, E. Talik and R. Sitko, Microchim. Acta, 2018, 185(2), 117 CrossRef PubMed.
- A. I. C. Ricardo, A. Sanchez-Cachero, M. Jimenez-Moreno, F. J. G. Bernardo, R. C. R. Martin-Doimeadios and A. Rios, Talanta, 2018, 179, 442–447 CrossRef PubMed.
- L. Li, Z. H. Wang, S. X. Zhang and M. L. Wang, Microchem. J., 2017, 132, 299–307 CrossRef CAS.
- S. Q. Zhu, B. B. Chen, M. He, T. Huang and B. Hu, Talanta, 2017, 171, 213–219 CrossRef CAS PubMed.
- P. Yan, M. He, B. B. Chen and B. Hu, Spectrochim. Acta, Part B, 2017, 136, 73–80 CrossRef CAS.
- Z. Arslan, T. Oymak and J. White, Anal. Chim. Acta, 2018, 1008, 18–28 CrossRef CAS PubMed.
- K. Kolacinska, E. Chajduk, J. Dudek, Z. Samczynski, E. Lokas, A. Bojanowska-Czajka and M. Trojanowicz, Talanta, 2017, 169, 216–226 CrossRef CAS PubMed.
- N. Guerin, R. Riopel, S. Kramer-Tremblay, N. de Silva, J. Cornett and X. X. Dai, Anal. Chim. Acta, 2017, 988, 114–120 CrossRef CAS PubMed.
- R. Kashanaki, H. Ebrahimzadeh and M. Moradi, Anal. Methods, 2017, 9(21), 3121–3127 RSC.
- B. Herce-Sesa, J. A. Lopez-Lopez and C. Moreno, Chemosphere, 2018, 193, 306–312 CrossRef CAS PubMed.
- C. Karadas, Water Qual. Res. J. Can., 2017, 52(3), 178–186 CrossRef CAS.
- M. Ghorbani, S. Akbarzade, M. Aghamohammadhasan, O. Seyedin and N. A. Lahoori, Anal. Methods, 2018, 10(17), 2041–2047 RSC.
- A. Alikhani, M. Eftekhari, M. Chamsaz and M. Gheibi, J. Food Meas. Charact., 2018, 12(1), 573–580 CrossRef.
- Y. M. Liu, F. P. Zhang, B. Y. Jiao, J. Y. Rao and G. Leng, J. Chromatogr. A, 2017, 1493, 1–9 CrossRef CAS PubMed.
- F. K. Li, A. J. Gong, L. N. Qiu, W. W. Zhang, J. R. Li, Y. Liu, Y. N. Liu and H. T. Yuan, PLoS One, 2017, 12(9) DOI:10.1371/journal.pone.0185302.
- X. M. Li, N. Z. Song, W. Feng and Q. Jia, Anal. Methods, 2017, 9(36), 5333–5338 RSC.
- I. Lopez-Garcia, M. J. Munoz-Sandoval and M. Hernandez-Cordoba, Talanta, 2017, 172, 8–14 CrossRef CAS PubMed.
- M. Llaver, E. A. Coronado and R. G. Wuilloud, Spectrochim. Acta, Part B, 2017, 138, 23–30 CrossRef CAS.
- I. Lopez-Garcia, J. J. Marin-Hernandez and M. Hernandez-Cordoba, Spectrochim. Acta, Part B, 2018, 143, 42–47 CrossRef CAS.
- B. Horstkotte, K. Fikarova, D. J. Cocovi-Solberg, H. Sklenarova, P. Solich and M. Miro, Talanta, 2017, 173, 79–87 CrossRef CAS PubMed.
- E. Beikzadeh and A. H. M. Sarrafi, J. Food Meas. Charact., 2017, 11(3), 994–1002 CrossRef.
- N. Limchoowong, P. Sricharoen, S. Techawongstien and S. Chanthai, Food Chem., 2017, 230, 398–404 CrossRef CAS PubMed.
- E. Bagda, H. Altundag and M. Soylak, Biol. Trace Elem. Res., 2017, 179(2), 334–339 CrossRef CAS PubMed.
- F. Heydari and M. Ramezani, Sep. Sci. Technol., 2017, 52(10), 1643–1651 CrossRef CAS.
- X. J. Wang, P. Chen, L. Cao, G. L. Xu, S. Y. Yang, Y. Fang, G. Z. Wang and X. C. Hong, Food Anal. Methods, 2017, 10(6), 1653–1660 CrossRef.
- N. Jalilian, H. Ebrahimzadeh, A. A. Asgharinezhad and K. Molaei, Microchim. Acta, 2017, 184(7), 2191–2200 CrossRef CAS.
- N. Tavakkoli, S. Habibollahi and S. A. Tehrani, Arabian J. Chem., 2017, 10, S3682–S3686 CrossRef CAS.
- A. Cervantes, R. Rodriuez, L. Ferrer, V. Cerda and L. O. Leal, Microchem. J., 2017, 132, 107–111 CrossRef CAS.
- S. Sivrikaya and M. Imamoglu, Anal. Lett., 2018, 51(5), 773–791 CrossRef CAS.
- B. Feist and R. Sitko, Food Chem., 2018, 249, 38–44 CrossRef CAS PubMed.
- I. Yener, E. V. Oral, I. Dolak, S. Ozdemir and R. Ziyadanogullari, Ecol. Eng., 2017, 103, 43–49 CrossRef.
- N. Limchoowong, P. Sricharoen, Y. Areerob, P. Nuengmatcha, T. Sripakdee, S. Techawongtien and S. Chanthai, Food Chem., 2017, 230, 388–397 CrossRef CAS PubMed.
- M. T. F. Teodoro, F. D. Dias, D. G. da Silva, M. A. Bezerra, A. F. Dantas, L. S. G. Teixeira and A. L. C. Pereira, Microchem. J., 2017, 132, 351–357 CrossRef CAS.
- S. Z. Mohammadi, T. Rohani and L. Bahadori, Commun. Soil Sci. Plant Anal., 2017, 48(11), 1359–1368 CrossRef CAS.
- N. Baghban, E. Yilmaz and M. Soylak, Microchim. Acta, 2017, 184(10), 3969–3976 CrossRef CAS.
- M. Soylak and Z. Erbas, At. Spectrosc., 2017, 38(3), 57–61 CAS.
- A. M. Basaglia, M. Z. Corazza, M. G. Segatelli and C. R. T. Tarley, RSC Adv., 2017, 7(52), 33001–33011 RSC.
- C. R. T. Tarley, G. L. Scheel, E. S. Ribeiro, C. D. Zappielo and F. A. C. Suquila, J. Braz. Chem. Soc., 2018, 29(6), 1225–1236 CAS.
- M. Karve and B. Choudhary, Int. J. Environ. Sci. Technol., 2017, 14(5), 993–998 CrossRef CAS.
- M. Wang, L. Wu, Q. G. Hu and Y. L. Yang, Environ. Sci. Pollut. Res., 2018, 25(9), 8340–8349 CrossRef CAS PubMed.
- S. Z. Chen, J. T. Yan, J. F. Li and D. B. Lu, At. Spectrosc., 2017, 38(4), 86–91 CAS.
- S. Z. Chen, S. P. Zhu and D. B. Lu, Spectrochim. Acta, Part B, 2018, 139, 70–74 CrossRef CAS.
- A. H. Panhwar, M. Tuzen, B. Hazer and T. G. Kazi, Talanta, 2018, 184, 115–121 CrossRef CAS PubMed.
- Y. L. Jia, T. F. Xiao, J. L. Sun, F. Yang and P. C. Baveye, Sci. Total Environ., 2018, 630, 146–153 CrossRef CAS PubMed.
- M. R. Ceballos, R. Garcia-Tenorio, J. M. Estela, V. Cerda and L. Ferrer, Talanta, 2017, 175, 507–513 CrossRef CAS PubMed.
- Q. Guo, H. Z. Wei, S. Y. Jiang, S. Hohl, Y. B. Lin, Y. J. Wang and Y. C. Li, Anal. Chem., 2017, 89(24), 13634–13641 CrossRef CAS PubMed.
- Y. H. Liu, K. F. Huang and D. C. Lee, J. Anal. At. Spectrom., 2018, 33(5), 846–855 RSC.
- E. Yobregat, C. Fitoussi and B. Bourdon, J. Anal. At. Spectrom., 2017, 32(7), 1388–1399 RSC.
- M. Willig and A. Stracke, Chem. Geol., 2018, 476, 119–129 CrossRef CAS.
- J. M. Zhu, G. L. Wu, X. L. Wang, G. L. Han and L. X. Zhang, J. Anal. At. Spectrom., 2018, 33(5), 809–821 RSC.
- K. Y. Chen, H. L. Yuan, P. Liang, Z. A. Bao and L. Chen, Int. J. Mass Spectrom., 2017, 421, 196–203 CrossRef CAS.
- Z. H. Zhu, J. Meija, A. R. Zheng, Z. Mester and L. Yang, Anal. Chem., 2017, 89(17), 9375–9382 CrossRef CAS PubMed.
- L. E. Morgan, D. P. S. Ramos, B. Davidheiser-Kroll, J. Faithfull, N. S. Lloyd, R. M. Ellam and J. A. Higgins, J. Anal. At. Spectrom., 2018, 33(2), 175–186 RSC.
- M. S. Bohlin, S. Misra, N. Lloyd, H. Elderfield and M. J. Bickle, Rapid Commun. Mass Spectrom., 2018, 32(2), 93–104 CrossRef CAS PubMed.
- K. K. Larsen, D. Wielandt and M. Bizzarro, J. Anal. At. Spectrom., 2018, 33(4), 613–628 RSC.
- Y. Q. Wang, X. X. Huang, Y. L. Sun, S. Q. Zhao and Y. H. Yue, Anal. Methods, 2017, 9(23), 3531–3540 RSC.
- S. Wakaki and T. Ishikawa, Int. J. Mass Spectrom., 2018, 424, 40–48 CrossRef CAS.
- J. B. Creech, F. Moynier and M. Bizzarro, Geochim. Cosmochim. Acta, 2017, 216, 28–41 CrossRef CAS.
- A. C. Hunt, M. Ek and M. Schonbachler, Geostand. Geoanal. Res., 2017, 41(4), 633–647 CrossRef CAS PubMed.
- T. Kurzawa, S. Konig, J. Labidi, A. Yierpan and R. Schoenberg, Chem. Geol., 2017, 466, 219–228 CrossRef CAS.
- X. Y. Chen, T. J. Lapen and H. S. Chafetz, Geostand. Geoanal. Res., 2017, 41(3), 427–435 CrossRef CAS.
- X. Y. Wang, C. Fitoussi, B. Bourdon and Q. T. Amet, J. Anal. At. Spectrom., 2017, 32(5), 1009–1019 RSC.
- G. Bruegmann, D. Berger and E. Pernicka, Geostand. Geoanal. Res., 2017, 41(3), 437–448 CrossRef CAS.
- J. B. Creech, F. Moynier and N. Badullovich, Chem. Geol., 2017, 457, 61–67 CrossRef CAS.
- M. A. Fehr, S. J. Hammond and I. J. Parkinson, Geochim. Cosmochim. Acta, 2018, 222, 17–33 CrossRef CAS.
- F. Kurzweil, C. Munker, J. Tusch and R. Schoenberg, Chem. Geol., 2018, 476, 407–417 CrossRef CAS.
| This journal is © The Royal Society of Chemistry 2019 |