Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials

Contents

• Foreword

• 1. Metals
  • 1.1 Ferrous metals
  • 1.2 Non-ferrous metals
  • 1.3 Cultural heritage: metals

• 2 Organic chemicals and materials
  • 2.1 Organic chemicals
  • 2.2 Fuels and lubricants
  • 2.3 Explosives
  • 2.4 Pharmaceuticals
  • 2.5 Personal care products
  • 2.6 Inks and papers
  • 2.7 Polymers and composites
  • 2.8 Paints and coatings
  • 2.9 Cultural heritage: paintings

• 3. Inorganic chemicals and materials
  • 3.1 Inorganic chemicals
  • 3.2 Fertilisers
  • 3.3 Inorganic materials
  • 3.4 Catalysts
  • 3.5 Building materials
  • 3.6 Ceramics and refractories
  • 3.7 Glasses
  • 3.8 Nuclear materials
  • 3.9 Electronic materials and devices
  • 3.10 Nanostructures

• Conflicts of interest

• Abbreviations

• References

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Abstract

This ASU review focuses on developments in applications of atomic spectrometry to the characterisation of metals, chemicals and materials. It is difficult to identify research trends solely from an annual review of the literature, but a certain perspective can be obtained from examining the developments described in recent years in this ASU review series. It is, for example, evident that there has been a decline in truly novel applications for the analysis of chemicals, perhaps indicative of the fact that, for most sample types, there is now an abundance of methods available in the literature. Those papers that have appeared in the year under review have either focused on specific problems not yet fully addressed (e.g. determination of Si in gasoline) or on incremental development of well-established approaches to sample preparation or measurement. Nevertheless, there has been a very noticeable increase in activity in relation to publication of methods for the characterisation of pharmaceuticals. This is directly linked to changes in the US Pharmacopeia requirements for registration of pharmaceuticals for human use that require assessments to be made for trace element content. Laser-induced breakdown spectrometry is becoming widely employed for applications involving the characterisation of a wide variety of metals, materials and other solid sample types. Efforts are being made to overcome the perceived weaknesses of the technique, such as lack of sensitivity, elemental fractionation, accuracy and/or precision. Advances have been made, for example, using dual-pulse lasers to improve sensitivity, or by employing chemometrics methods with full spectrum data to improve the robustness of calibration. Many of these reported LIBS developments draw from, and have relevance for, applications involving LA-ICP-MS, which continues to be a popular means of generating highly sensitive lateral and depth profiling and bulk compositional information for a wide range of materials and solids. The use of field portable instrumentation for in situ analysis continues grow, with LIBS and XRF techniques amongst those most frequently cited. The development of such instrumentation has had a substantial impact in the examination of cultural heritage artefacts, especially in relation to paintings, wall murals and other objects of unique historical value. The latter area of application has also seen continued use of combinations of surface (SIMS, XPS, SEM-EDS, PIXE, GD and laser ablation methods) and bulk (ICP-OES, ICP-MS, AAS, XRF) analysis techniques to reveal details of objects that would not otherwise be identified (for example preparatory sketches hidden under original works of art or materials provenance). This trend towards using a multi-technique based approach has also been apparent in the characterisation of multi-layer or heterogeneous organic and inorganic materials and metals. Finally, methods for the analysis of nanoparticles and nanostructures have been reported, based primarily on single particle (SP)-ICP-MS and flow field flow fractionation (A4F). The investigation of methods of drift correction, the use of flow injection and isotope dilution methodologies in combination with SP-ICP-MS are indicative of the further development of this field.

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Foreword

This is the latest review covering the topic of advances in the analysis of metals, chemicals materials. It follows on from last year’s review in the series.¹ The review structure has been altered slightly this year to better align content to trends evident in the literature relating to the relevant application fields. The title of the review has been simplified to facilitate improved integration of the materials applications covered. Thus, Sections 2 and 3 have been refocused on applications where the sample matrix is predominantly either ‘Organic’ or ‘Inorganic’ and includes both chemicals and materials in the relevant subsections of each. Hybrid materials have been included in the relevant sub-sections that best describe the application field in which they are used. The significant increase in applications relating to cultural heritage (e.g. metals, ceramics, glasses and paintings) is also reflected in the revised structure. New tables have been also introduced to reflect these changes.

1. Metals

The application and development of analytical methods relating to metals and alloys continues to be of importance across a range of disciplines. Consequently, there is great diversity in publications appearing again this year. The use of different combinations of spectroscopic techniques remains of great importance in metals analysis. The use of a multiple spectroscopic approach to the analysis of complex samples is particularly evident in the development of new metallic products. Topics related to fundamental aspects of laser/sample interaction, the development of the calibration free calibration-free (CF) concept and the versatility of LIBS, again featured strongly. The unique capabilities of LIBS for stand-off analysis, particularly in harsh environments, are of great interest to industrial metals production and are highlighted in the following sections.

1.1 Ferrous metals

Many authors have utilised multiple spectroscopic methods to provide the detailed analytical characterisation that is often required in the development of new products. One popular area of application is in the development of coatings or modified steel surfaces, as new techniques emerge giving greater scope to surface engineering. Zhang et al.² investigated the properties of coatings containing Al, N, Si and Ti (identified as TiAlSiN), designed to improve the performance of high speed steels used in drill bits and cutting tools. In this work, cathode ion arc implantation was used to apply the TiAlSiN coating to a high-speed steel grade. The coating thickness, chemical composition and micro-structural morphology were investigated using EDXRF and SEM. Additional physical measurements were made to determine coating hardness and wear resistance, using a Rockwell hardness tester and a ball-on-disc tribometer, to assess cutting performance. The combined results were used by simulation software to generate a 3D orthogonal cutting model to study the influence of coating thickness on cutting force and temperature. Establishing a correlation between the analytical data and supplementary tests, measuring in-use properties, has become a valuable tool in product design and testing. Developments relating to corrosion resistance continue to be of commercial interest within the steel sector. Deposition of structured coatings onto steel surfaces using sol/gel processes has become a popular approach with many research groups. Bacic et al.³ investigated the molecular and nanostructure of the yttria-modified zirconia coating formed by the sol/gel dipping process onto the surface of stainless steel. The surface molecular structure was determined from the crystal structure, measured by XRD and coating depth was measured by GD-OES. It was concluded that increasing the concentration of yttria in the coating had little effect on the coating thickness. In addition, the number of coating layers and at the higher sintering temperature of 600 °C improved the corrosion resistance.

The identification of steel grades is of considerable importance in confirming steel for dispatch from a production facility or in the identification of scrap pieces for recycling. Although grade identification can be achieved using XRF and spark-OES instruments, LIBS has again attracted growing interest in rapid scrap sorting applications. Zhang et al.⁴ demonstrated the discrimination and identification of nine steel grades using a LIBS system integrated with classification software based on the concept of random forest data analysis (RFDA). This supervised learning algorithm was evaluated by direct application to nine steel grades and compared with alternative algorithms based on partial least squares data analysis (PLS-DA) and support vector machines (SVM). The classification of steel samples based, on the RFDA model was reported to show a better predictive performance than the PLS-DA and SVM. The authors concluded that the RFDA method offered a promising method for automatic real-time, fast, reliable, and robust measurements, suggesting suitability for ferrous scrap sorting.

Several papers have been published on the development of calibration-free laser-induced breakdown spectroscopy (CF-LIBS) as a means of dealing with matrix effects and the need for calibration curves. Wang et al.⁵ applied LIBS to the on-line measurement of iron ore acidity which is normally measured off-line by XRF from measured concentrations of silica and lime expressed as the CaO/SiO₂ ratio. This application exploited the inherent advantages of LIBS as a means of rapid analysis to monitor temporal changes in composition suitable for a dynamic situation. The Ca(I)/Si(I) molar ratio was obtained by the intercepts on the Boltzmann plots drawn by using corrected spectral lines without self-absorption and the Saha equation was used to obtain the Ca(I)Si(I) molar ratio. In addition, one standard sample matrix-matched with unknown samples along with binary search analysis were employed to obtain a more accurate plasma temperature. The acidity of the iron ores was calculated from known Ca/Si molar ratios obtained from accepted methods of analysis. The authors stated that a close agreement between the known acidity and that obtained by CF-LIBS analysis which had a mean relative error of 4.01%. This CF-LIBS method was proposed as a means of analysing the acidity of iron ores and as a new technology for enhanced control during smelting.

This review period has seen further progress in the development of laser-induced breakdown spectroscopy using chemometric techniques. Kong et al.⁶ demonstrated a method of selecting emission lines for Cr, Fe, Mn, Ni and Si from LIBS excitation of low alloy steel samples via a genetic algorithm that utilised the signal from matrix Fe as a reference. The optimum analytical line was identified for each of the five elements along with their corresponding Fe reference lines and reported in the format listed in the standard reference database (NIST). For Cr, the optimum analytical line was at 286.5100 nm, with the Fe matrix reference at 272.7539 nm; 403.3068 nm for Mn, with the corresponding Fe matrix reference line at 368.7457 nm; 288.1577 nm for Si, with the Fe matrix reference at 427.1761 nm and 352.4536 nm for Ni, with the corresponding Fe matrix reference at 358.6985 nm. From the quantitative application of these lines with internal referencing was reported to show high sensitivity. The use of genetic algorithms for automatically selecting the analytical and reference lines from complex emission spectra and the importance of internal referencing were highlighted by the authors.

Improvements to LIBS sensitivity remains a popular topic. Boron is one element that is problematic when detected directly in steel by OES methods due to its weak emission lines. Moreover, B is generally present at low concentrations in steels which are often highly alloyed with elements such as Cr, Ni and Mo forming a complex emission spectrum. Li et al.⁷ reported the use of laser ablation with laser-induced fluorescence (LA-LIF) to overcome sensitivity and interference difficulties of analysing B in superalloy steel containing high concentrations of Cr and Ni. A tunable laser was used to selectively enhance the B emission intensity at 208.96 nm in the plasma at two excitation wavelengths: 249.68 and 249.77 nm. The LA-LIF analysis was compared with the corresponding LIBS analysis for B in a superalloy steel grade. Sensitivity was increased by a factor of 3.0 by LIF enhancement at 249.68 nm yielding a LOD of 0.9 ppm. Higher sensitivity was reported using the 249.77 nm wavelength increasing sensitivity by a factor of 5.8 and a correspondingly lower LOD of 0.5 ppm.

Zhang et al.⁸ demonstrated the potential of LIBS as a tool for micro analysis by focusing the laser excitation beam to a spot size of the order of non-metallic inclusions. The sensitivity and speed associated with LIBS analysis was shown to differentiate between Al alloyed in the steel (soluble Al) and its presence in non-metallic inclusions (insoluble Al). The LIBS approach was used to set the threshold value to distinguish between soluble and insoluble Al in conjunction with the corresponding inclusion analysis from SEM. Parity between the LIBS and SEM was reported for the analysis of alumina inclusions classified as globular, analysed directly in a basic low-C steel and in an inclusion-critical bearing steel. The authors highlighted the importance of LIBS as a means of increasing the speed of inclusion assessment to achieve more effective production control.

An alternative approach to rapid micro-analysis has been presented by Janis et al.⁹ based on spark-OES analysis. As spark-OES is the only effective method of process control during steel-making, its application to micro-analysis is of interest. For bulk analysis, thousands of individual sparks are discharged into the sample surface for each multi-element analysis cycle. The authors used the data from individual spark discharges to identify non-metallic inclusions in thirty-seven production samples of duplex stainless steel. Spark discharges encountering inclusions showed a significant increase in emission intensity for the elements present, relative to the matrix metal. This aspect of spark-OES is called pulse discrimination analysis (PDA) and the authors highlighted its potential for process control as many high value steel grades are critically dependant on the levels of non-metallic inclusions (cleanness). Since spark-OES is the only technique fast enough to provide analytical process control, it was proposed that the PDA mode of operation could be used as a means of monitoring cleanness within the batch production cycle. Indeed, the authors reported initial results on the potential link between cleanness and downstream defects in the cast product. Although not presented as a conclusive study, the potential benefits of in-process cleanness to feedback control were highlighted.

Meermann et al.¹⁰ used an AF4 –SF-ICP-MS approach for the analysis of iron nanoparticles whilst also using on-line ID with a ⁵⁷Fe isotope enrichment. The method developed was a combined approach of stable isotope labelling and reverse post channel species un-specific on-line isotope dilution quantification. Iron oxide nanoparticles enriched in ⁵⁷Fe and coated with silica were spiked into filtered re-suspended river sediment – slurry. The result was an unambiguous tracing of the engineered nanoparticles among natural Fe-containing colloidal fractions followed by sensitive quantification, despite the naturally high Fe background levels. A LOD of 2.4 μg L⁻¹ and LOQ of 7.8 μg L⁻¹ Fe was reported. Good reproducibility of peak area (0.4–3%) and excellent precision of peak elution times (0.1–0.7%) were also reported.

1.2 Non-ferrous metals

There continues to be considerable interest the use of multiple spectroscopic methods often applied to cultural heritage material or as an integral part of new product development. Many of these applications follow the methodologies applied to ferrous metals, especially for initiatives to improve wear and corrosion resistance. The development of new alloys for orthopaedic implants features strongly each year in the non-ferrous section. The surface properties of a new quaternary alloy for implant composed of Ag, Ta, Ti and Zr (identified as, Ti₂₀Zr₅Ta₂Ag) were studied by Vasilescu et al.¹¹ using XRD, XPS and SEM in conjunction with Ringer’s solution in acidic, neutral and alkaline solutions to simulate body fluids. It was reported that XRD analysis showed the new alloy had a crystalline surface microstructure. XPS and SEM analyses show the thickening of the passive film and surface deposition of hydroxyapatite (Ca₁₀(PO₄)₆(OH)₂) in neutral and alkaline Ringer’s solution increased over time, which was confirmed by further electrochemical tests. Additional ICP-MS measurements showed that no Ta ions were detected and very low quantities of Ag, Ti, and Zr ions were released. Secondary ion mass spectrometry continues to play a valuable role in many multiple spectroscopic studies, especially with the increasing availability of high resolution detection. However, Li et al.¹² used high resolution SIMS exclusively to trace the distribution of the ¹⁸O isotope to study the oxidation mechanisms that occur when high purity terbium metal is exposed to a corroding environment. Isotopic tracing showed that the migration behaviour of oxygen correlated with the existence of defects in the metal, causing corrosion at localized nucleation sites. A set of ionization model was developed from this work to describe the oxidation process.

Laser induced breakdown spectroscopy continued to attract considerable interest for the characterisation of non-ferrous metals. Several papers have examined the fundamentals involved in the interaction of laser radiation and the sample matrix to develop the calibration-free laser induced breakdown spectroscopy concept (CF-LIBS). Smaldone et al.¹³ evaluated a variant of CF-LIBS which involved applying an inverse method (CF-IM). This new approach essentially applied the models used in CF-LIBS in reverse to obtain plasma temperature from a single certified standard. The authors demonstrated the CF-IM concept using a Nd : glass laser, operating at 527 nm and with a pulse duration of 250 fs. A close agreement between the CF-IM derived analysis and the certified composition was achieved for a series of brass and bronze certified standards. It was concluded that the CF-IM method could applied when fs laser pulses were used at 527 nm.

As indicated in the previous section, the development of methods for the improvement of sensitivity in laser induced breakdown spectroscopy been a popular theme again this year for non-ferrous applications. Notable work by Viljanen et al.¹⁴ demonstrated an improvement in LIBS sensitivity by use of 2.45 GHz microwaves pulsed at 1 ms and coupled with the LIBS generated plasma. The external microwave power was shown to significantly increase the signal lifetime from a few ms to hundreds of ms resulting in a great enhancement on LIBS signals by employment of a long integration time. The dependence of signal enhancement on laser energy and microwave power was experimentally assessed. The LOD for Cu in a copper/alumina solid was estimated at 8.1 ppm with sensitivity enhanced by a factor of almost one hundred compared to conventional single-pulse LIBS. Additionally, in this microwave assisted LIBS system, the effects of self-absorption were greatly reduced, offering potential benefits for measuring elements of high concentration. Similarly, a method of increasing sensitivity by prolonged plasma emission was also described by Wang et al.¹⁵ The confining effect of the crater, formed during LIBS ablation of copper samples, using a nanosecond pulsed Nd : YAG laser operating at 1064 nm was investigated. It was shown that increasing the diameter of the cavity increased persistence of the Cu(I) line. The authors postulated that shock waves, reflected from the crater during the ablation process, compressed the plasma and increased its density and temperature and that this causes further excitation of the atomic population. However, crater depth was shown to have no effect on the persistence of the Cu(I) line.

A variety of progressive LIBS applications have been seen this year, which exploit the unique properties of the technique for remote sensing in harsh environments. Guirado et al.¹⁶ have presented work which aims to expand the scope of LIBS to include submersed materials up to a depth of 50 m. In this work, the use of multi-pulse excitation was evaluated as an effective solution to mitigate the preferential ablation of the most volatile elements, such as Sn, Pb, and Zn in copper alloys. A novel remote LIBS prototype used in this development featured both single and multi-pulse excitation. Laser-induced breakdown spectroscopy analysis was performed at air pressure settings simulating the conditions during a real subsea analysis. A set of five certified bronze standards with variable concentration of As, Cu, Pb, Sn and Zn was used. Using single pulse operation, the emission signal was shown to be strongly affected by ambient pressure and fractionation was observed. Multi-pulse excitation was shown to be unaffected by pressure over the period of quantitative analysis, thus avoiding fractionation. The use of Cu as internal standard minimised matrix effects and discrepancies due to variation in ablated mass. In another example of measurement in a harsh environment, the remote analysis of molten metal during production by LIBS was investigated by Xin et al.¹⁷ A pilot study of the use of a coaxial double pulsed laser configuration for the direct analysis of molten magnesium was presented. It was observed that the optimum inter-pulse delay was dependant on the distance between the laser and the point of analysis and that the emission signals decreased with distance. Although excellent calibrations were reported for Pr, Y and Zr in solid reference samples, when applied to molten magnesium the quantitative performance was not as good. The authors conceded that the use of solid reference standards may not be a suitable means of pre-calibrating LIBS for direct quantitative analysis of molten magnesium and that further development work was required.

The analysis of elemental ratios in intermetallic nanoalloys and nanocomposites with calibration-free quantitation using LIBS was reported by Davari et al.¹⁸ The authors noted the advantages of LIBS compared with ICP-OES, i.e. in situ analysis, rapidity, no necessity for sample digestion etc., and then applied them to samples such as the binary materials PtNi, PdCo and PtCo. No external calibration was required. Instead an internal calibration was employed that used the matrix elements as internal standards. The techniques of SEM-EDX and TEM were also used to study the composition and morphology. Results obtained from the LIBS system agreed with those obtained using a dissolution followed by ICP-OES analysis.

Resano et al.¹⁹ utilised continuum source ETAAS to determine nanoparticle size. The atomisation profiles of ionic Au and nanoparticulate Au were very different. Careful optimisation of the heating rate during atomisation (150 °C s⁻¹) and of atomisation temperature (2200 °C) enabled distinction between the species to be made. Ionic Au produced a maximum absorbance signal first and then the maximal absorbance values for nanoparticles were increasingly retarded as the particle size increased. The procedure was tested using Au nanoparticles of 20 and 76 nm. A LOD of 5.5 pg (0.55 μg L⁻¹) was obtained and the linear range spanned to 10 ng. Method validation was obtained by the analysis of the reference materials NIST 8012 (nominal diameter 30 nm, experimental result 27.7 ± 8.8 nm) and by spiking a reference water sample (KEJIM 02) with ionic and nanoparticulate Au. The authors acknowledged that, in common with SP-ICP-MS, it is difficult to characterise small nanoparticles in the presence of large quantities of ionic Au. Characterisation when ionic and nanoparticulate Au are both present is difficult because the absorbance peaks at least partially overlap.

1.3 Cultural heritage: metals

The increasing availability of high resolution mass spectrometers has become a valuable tool in establishing provenance for cultural heritage materials. Milot et al.²⁰ demonstrated the use of stable Fe isotopes as an alternative approach to establishing provenance for ferrous artefacts. By reconstructing a bloomery furnace, the authors demonstrated that this ancient means of smelting did not cause fractionation of Fe isotopes. Sample slag and metal were collected from the experimental bloomery furnace and the Fe isotope composition determined by high resolution ICP-MS with a multi-collector detection. It was reported that the isotopic composition for Fe in the metal and slag was the same as the ore in 8 of the 9 sets of samples. It was concluded that Fe isotopes could be used to link ferrous artefacts to their original iron ore reserves since no isotopic fractionation was observed in the smelting process which would affect provenance.

Although non-destructive methods are generally preferred in the analysis of heritage materials, the removal of material for isotopic analysis is often required to establish provenance. Torrisi et al.²¹ used laser ablation sampling to minimise the material removed for a set of bronze coins dating back the 8th century A.D. With the laser coupled to a high-resolution quadrupole mass spectrometer was used to establish the ²⁰⁸P : ²⁰⁶Pb and ²⁰⁷Pb : ²⁰⁶Pb ratios for the coins. A comparison between the measured isotope ratios and database values of mineral reserves indicated a link to possible geological sites of the extracted mineral. Clemenza et al.²² combined thermal ionization mass spectrometry and neutron activation analysis to determine provenance for six lead ingots from the Roman period. Isotopic ‘fingerprinting’ combined with a comprehensive element profile for each ingot. Markings on each ingot identified the foundry where it was produced, with the collection known to come from three different foundries. The authors reported that the analytical data had confirmed provenance and provided insight into the manufacturing processes. This approach was highlighted as a method incurring negligible damage and recommended for wider application to cultural heritage materials.

2 Organic chemicals and materials

As indicated in the foreword, the review has been restructured to reflect the growing diversity of the literature relating to the characterisation of chemicals and materials. Consequently, this section has now been expanded from previous years to include all organic products, whether chemical, formulation or material-based.

2.1 Organic chemicals

There has been a noticeable reduction in novel developments in the literature in the year under review. A few publications of note are described in this section and other applications for the analysis of organic chemical products are summarised in Table 1.

Table 1 Applications of atomic spectrometry to the analysis of organic chemical products

Element	Matrix	Technique	Sample treatment/comments	Reference
Al	Explosives	OES;LIBS;s	Determination of Al in 1,3,5-trinitro-1,3,5-triazine (RDX)-based aluminized explosives and use of LIBS atomic (Al, C, H and O) and molecular spectra (AlO and CN bands) for investigation of detonation performance	74
B	Pharmaceutical active agents	MS;ICP;l	Active pharmaceutical components (boronic acids and boronate esters) were determined stoichiometrically following detection of elemental B. An LOQ of 0.8 ppm was reported for the method	75
B	Aliphatic and aromatic trifluoroborates	OES;MIP;l	Samples were decomposed using oxygen flask combustion. The mass fraction of B was determined in a range of compounds with an error less than ±0.3 abs%	76
Cd	Pharmaceutical formulations	AA;ETA;l	Method based on use of a palladium–magnesium chemical modifier. The LOD and LOQ reported for Cd were 4 and 13 ng g^−1 respectively with an RSD of 5.0%	77
Cr	Chromium-complex acid dyes	OES;ICP;l	Commercial dyes (8) were dissolved in methanol followed by solid phase separation on a C-18 column. Free Cr was subsequently determined by ICP-OES. Cr^VI was determined separately by IC and total Cr was determined after acid digestion by ICP-OES	78
Cr	Sunscreen products	AA;ETA;s	Direct analysis of sunscreen samples in a graphite furnace CS-AAS instrument. An LOD of 1.0 μg kg^−1was reported using a sample mass of 4.5 mg. Results obtained agreed with those using a conventional digestion method	79
Pb	Pharmaceutical formulations	AA;ETA;l	Method based on use of a palladium–magnesium chemical modifier. The LOD and LOQ reported for Pb were 49 and 165 ng g^−1 respectively with an RSD of 6.2%	77
Pb	Sunscreen products	AA;ETA;s	As for Cr except for use of a using a chemical modifier (0.05% Pd + 0.03% Mg + 0.05% Triton X-100). A LOD of 3.0 μg kg^−1 was reported using a sample mass of 4.5 mg	79
Sn	Boat paints and anti-fouling paints	XRF and ICP-MS	Comparison of various acid digestion sample preparation methods using ICP-MS detection and a preferred direct analysis method based on field-portable XRF for total Sn. Organo-Sn species were extracted from samples and determined by GC	80
Various	Active pharmaceutical ingredients	—;—;—	Microwave acid digestion method for the determination of ‘heavy metals’ and ‘ignition residue on sulfated ash’ measures for 15 different pharmaceutical ingredients to meet United States Pharmacopoeia (USP) and European Pharmacopoeia requirements	81
Various (6)	Acyclovir ointment and raw materials	MS;ICP;l	Sample were subjected to microwave digestion and analysed using an instrument in standard and kinetic energy discrimination modes for As, Cu, Cr, Ni, Pb, and V. The LODs obtained were sufficient to satisfy the requirements of the US. Pharmacopeia convention	82
Various (3)	Colour additives	Polarised EDXRF and WDXRF	Comparison of techniques for the direct determination of As, Hg, and Pb in US FDA certifiable colour additives. The high energy polarised EDXRF system provided better signal to noise ratios that WDXRF and improved LODs. Precision achieved was comparable to that of ICP-MS	83
Various (3)	Cosmetic products	AA;F;l	Formation of ternary complexes of Cd^II, Pb^II, and Sn^II in the presence of cetyl pyridinium chloride at pH 8.5 followed by ultrasonic-assisted cloud point extraction using Tween 60 prior to analysis by FAAS. The LODs for Cd^II, Pb^II, and Sn^II, respectively, were 3.70, 1.35, and 0.45 μg L^−1, with a reported method preconcentration factor of 50	84
Various (5)	E-cigarette cartomiser liquids	MS;ICP;l	Method described for the determination of trace elements. Levels found in all commercial brands analysed were in the ranges 53.9–2110 μg L^−1 (for Cr); 28.7–6910.2 μg L^−1 (for Mn). 58.7–22 600 μg L^−1 (for Ni); and 4.89–1970 μg L^−1 (for Pb)	85
Various (9)	E-cigarette (E-CIG) liquids and aerosols	MS;ICP;l	Use of mixed cellulose ester membranes to collect trace metals (Al, As, Cd, Cu, Fe, Mn, Ni, Pb, and Zn) from E-CIG devices before and after aerosol generation cf. conventional smoking	38
Various	Graphitic carbons	OES;ICP;l	Investigation of wet digestion sample preparation methods for the elemental analysis of graphitic carbons and nanotubes	24
Various (23)	Petroleum jelly personal care products	MS;ICP;l	Evaluation of several sample preparation methods for the ICP-MS determination of Ag, As, Ba, Cd, V, Cr, Co, Cs, Ga, Ge, Mo, Nb, Ni, Pb, Rb, Re, Se, Sr, Tl, U, W and Zr, using internal standard calibration. The method was applied to the analysis of commercial hair-relaxing kits	86
Various (16)	Pharmaceutical products	WDXRF	Investigation of WDXRF for the determination of 16 trace elements to meet ICH guidelines (see Pharmaceutical section 2.4 in text). Five elements (As, Cd, Hg, Pd, and V) were subsequently excluded from the analysis. The LODs and LOQs for the other elements were reported in the range 0.6–5.4 μg g^−1 and 1.7–16.4 μg g^−1 respectively	87
Various	Printing inks	SEM-EDS and LA-ICP-MS	Printing inks (319 samples of toner, inkjet, offset, and intaglio) were examined directly on the paper substrate. The LA-ICP-MS system provided almost compete discrimination between different ink samples and sources	88
Various	Pyrazoles	OES;LIBS;s	Use of chemometrics techniques with spectral fingerprint to identify structural isomers	89
Various	Red inks	TOF-SIMS	Depth profiling and image mapping used to examine different pen inks, red sealing inks, and printed patterns on paper to identify the sequence of recording	90
Various	Revenue stamps ink	EDXRF	Direct elemental analysis of ink and paper substrate followed by PCA for discrimination between authentic and counterfeit revenue stamps	91

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The determination of trace metals in organic chemicals can be problematic because of spectral interferences arising from matrix constituents. For example, line source AAS instruments have difficulty in compensating for structured background effects. However, a high-resolution continuum-source AAS instrument has been employed to investigate such molecular interferences in the determination of Si in a xylene solution containing sulfur compounds.²³ It was found that CS molecules formed in the N₂O/C₂H₂ flame absorbed radiation at the five most sensitive wavelengths used for the determination of Si. It was found that the intensity of the structured background generated was dependent not only on the level of S present in the sample solution but also on its chemical form and relative volatility. It was reported that the use of a least squares procedure in combination with an iterative background correction approach compensated for molecular interferences arising from solutions containing up to 5% S w/w.

The creation of novel molecular structures based on carbon (e.g. graphene) has been the subject of much research interest in recent years. However, the trace metal characterisation of these substances has been challenging because of a lack of appropriate methods of sample preparation and suitable certified reference materials. A two-step microwave assisted digestion approach has been proposed for the analysis of both certified and commercial carbons.²⁴ It was reported that ICP-OES could be used to determine successfully up to 18 elements (apart from Sm) in two carbon RM’s using the method. However, it was noted that while it was possible to apply the procedure to a carbon nanotube sample, other materials were incompletely digested, suggesting that further work needs to be done to achieve wider application.

The detection of chemical warfare agents (CWA) has been investigated using a portable LIBS system.²⁵ The instrument was used to detect contamination by four such agents (sarin, lewisite, mustard gas and VX) on a wide variety of substrates including wood, concrete, military green paint, gloves, and ceramic in a laboratory environment. Direct detection was achieved by acquisition of LIBS spectra for As, Cl, F, P and S which were correlated with the presence of the CWAs. The LOQ for the method was reported at a surface concentration of 15 μg cm⁻².

2.2 Fuels and lubricants

Overall, the volume of papers in this application area continues to decline. However, there were more contributions this year related to crude oils possibly reflecting the upturn in the oil price. The numbers of papers on coal from China has dramatically reduced perhaps indicating the change in emphasis in China from fossil fuels to renewable energy sources. Authors continue to re-invent old standard lab methods presenting them as ‘something new’. Journal referees are urged to make themselves familiar with published standard laboratory methods (IP, ASTM etc.) particularly in the field of Hg analysis to prevent these papers slipping through the peer review net. It is noteworthy that ICP-MS-MS is increasingly being used. However, authors do not appear to be making the most of the capabilities of these instruments for ‘difficult to analyse’ elements and there is a lot more progress yet to be made in this field. Analytical research in fuels and lubricants can be challenging. However, industrial laboratory practice (often unpublished because of commercial confidentiality or intellectual property considerations) may well be more advanced than work currently appearing in academic journals. Consequently, it is recommended that academic authors should consult with colleagues in the relevant industrial fields to identify areas of practical concern rather than relying solely on reviewing the open literature.

2.2.1 Petroleum products – gasoline, diesel, gasohol and exhaust particulates. Only three papers appearing in the year under review were worth highlighting.

The measurement of Si in gasoline has been investigated using ICP-OES by Zhao et al.²⁶ The profile of Si within the industry was massively raised in the UK and elsewhere when there was a costly Si contamination in petrol issue in 2007. Silicon usually enters fuels via contamination from organosilicon compounds used in the petrochemicals industry. These compounds can typically have low boiling points (in the case of the 2007 issue hexamethyl di-siloxane with a boiling point around 100 °C). As these compounds behave in a volatile way in a conventional spray chamber, enhancement is seen and an over recovery of Si measured. Consequently, Zhao et al.²⁶ tried to address this problem using a temperature-controlled cyclonic spray chamber and a micro nebuliser for sample introduction. This sample introduction system was found to improve the issue but not to eliminate it. Instead, a direct injection high efficiency nebuliser (DIHEN) was employed. Using the DIHEN, good linearity of response was achieved and recoveries for Si ranged from 92.8–108%. The method appears to be simple, rapid, sensitive and accurate. However, these types of nebuliser are notoriously difficult to set up for routine analysis.

There have been two reports in the year under review of the use of laser-induced breakdown spectrometry of on-line analysis. Trichard et al.²⁷ investigated the determination of sulfur in petroleum products using LIBS. A study of buffer gases was presented to improve the sulfur IR line detection and an optimal helium flow rate of 1.4 L min⁻¹ was established. Detection limits were achieved in the 0.2% w/w range. Calibration was made under the process conditions of 70 °C and at room temperature. Normalization of signal response with a helium emission line was used to compensate for liquid level variations and for temperature variations. The analysis of a sample over the course of 2 days under the conditions of the industrial process was tested and gave good repeatability.

In a second paper, Trichard et al.²⁸ investigated the on-line determination of nickel and vanadium in oil samples. These are the most abundant metallic elements in crude oils and affect the cracking conversion process, cause catalyst deactivation and increased coke formation thereby reducing the effectiveness of the whole process. A Nd : YAG laser that could operate at 266 nm and 532 nm was evaluated for on-line use. An original optimization strategy employing a Doelhert design and full quadratic model was carried out. It was reported that the visible wavelength gave the better results. Measurements were compared under stationary and dynamic conditions (i.e. the sample being moved at a cc h⁻¹ flow rate as it is under the process conditions), and no significant difference in terms of sensitivity and detection limits was observed due to the viscous nature of oil samples. Detection limits achieved using the system were 31 mg kg⁻¹ for Ni and 7 mg kg⁻¹ for V.

2.2.2 Coal, peat and other solid fuels. Corazza et al.²⁹ outlined a method for S analysis using microwave induced combustion to improve digestion of coal prior to analysis by ICP-OES. The new method was compared to analysis by UV fluorescence and microwave digestion followed by ICP-OES analysis using coal reference materials. Parameters such as concentration of absorbing solution, the necessity of a reflux step and cooling time were optimised. Complete digestion using the new method was achieved in 6 minutes. This method was claimed to be faster, more efficient and ‘greener’ than previous methods used.

Hou et al.³⁰ described a hybrid quantification model for coal analysis using laser-induced breakdown spectroscopy. The aim of this work was to improve the sample to sample reproducibility which is currently seen as a large problem in the development of LIBS coal methods. The proposed method included three steps. Firstly, the intensities of all spectral lines from every single pulse were converted to a standard value to reduce measurement uncertainties, then the standardized spectra were compared with a large spectral database to check whether the current sample was a new sample or is already present. If the sample was found to be a new sample, a dominant factor based partial least square (PLS) model was applied and the new information and results inserted into the spectrum database making the database self-adaptable for future measurements. Using this approach, the average measurement errors for carbon, hydrogen, volatiles, ash and heat values are 0.42%, 0.05%, 0.07%, 0.17% and 0.07 mJ kg⁻¹, respectively. It was reported that the method met the requirements of the national standard for coal analyses. Considering the scale of the market for coal analysis, this work may open the door to large scale commercialization of LIBS in this field.

Artemyeva et al.³¹ explored the use of graphite furnace AAS to investigate trace element sulfide evaporation and atomisation behaviour during coal combustion. The graphite furnace was used to simulate the evaporation and atomisation behaviour that mineral inclusions and exclusions experience during the onset of coal combustion. Thus, sulfides (of As, Sb and Se) were dissolved in water using potassium polysulfide. The sulfides in inclusions were simulated by adding the test mixture to a graphite tube in a reducing environment. Excluded sulfides were studied by coating the graphite tube with ZrO₂ or WO₃. Arsenic sulfide (As₂S₃), exhibited a lower Arrhenius activation energy of atomization, (E_a) than that of the corresponding oxide in both mineral inclusions and exclusions.

A review paper by Mketo et al.³² containing 95 references, provided an overview on analytical methods for multi-element determination in coal samples. Sample preparation strategies and analytical techniques were considered in detail, including wet digestions and combustion reactions. Pros and cons of these sample processing methods were discussed with emphasis given to microwave approaches. The mobility behaviour of some elements was discussed in the context of speciation in the coal. Lastly, thermal stability of various elements in combustible material was also reviewed.

2.2.3 Oils – crude oil lubricants. Druzian et al.³³ described a method for the simultaneous determination of Ba, Co, Fe, Mg, Mn, Ni, V and S in crude oil distillation residues. Samples were wrapped in polyethylene films and combusted with oxygen prior to dissolution in nitric acid with reflux and ICP-OES analysis. This method was compared to microwave acid wet digestion and no statistical difference was observed. The combustion method used less acid and produced a solution containing less C than the wet digestion technique helping reduce the potential for spectral interferences.

Walkner et al.³⁴ described the use of ICP-MS-MS for the analysis of crude oils after pressure digestion. Extensive use was made of the mass shifting capability of the ICP-MS-MS with O₂ and NH₃ to measure elements ‘off mass’, thus removing previously problematical interferences especially those on S and P. The simultaneous determination of 25 elements was achieved using the method. Analysis of reference materials NIST 1643c and NIST 1084a was undertaken and showed good agreement with the reference values. The high sensitivity of the instrumentation also allows the detection of ultra-trace elements such as Re and U without a preconcentration evaporation step which could introduce analyte losses. A similar approach was taken by Casey et al.³⁵ for complete fingerprinting of crude oils using one digestion followed by ICP-OES and ICP-MS-MS. The proposed method minimised organic matrix residues and removed spectral interference effects. Good elemental recoveries were reported for 57 elements.

2.2.4 Alternative fuels. Three papers concerning the characterisation of alternative fuels are worthy of note. Ling et al.³⁶ described a method for the multielement analysis of biodiesel using an emulsion breaking preparation technique. The analysis was carried out using ICP-MS equipped with a dynamic reaction cell used to reduce spectral interference effects. The limits of detection achieved for, Ca, Cr, Cu, Fe, Mg, Mn, Ni, Pb and Zn, were from 0.0078 to 0.19 μg L⁻¹ and LOQs were from 0.026 to 0.63 μg L⁻¹.

A method for the analysis of bioethanol samples by ICP-MS using a high temperature Torch Integrated Sample Introduction System (hTISIS) has been described by Sanchez et al.³⁷ Bioethanol is considered a promising alternative to fossil fuels and in its anhydrous form it is often blended into products such as gasoline. However, some problems can be found when bioethanol containing samples are introduced into ICP-MS systems resulting in analysis bias and interferences. The basic principle of the hTISIS consists of complete aerosol evaporation before it enters the plasma. In this manner analyte transport efficiency is virtually 100% regardless the sample matrix. The aim of this work was to test the combination of hTISIS with ICP-MS as a rapid and direct way of performing multielement determination in bioethanol samples. The studies were undertaken following two different flow regimes, continuous sample aspiration at a 25 μL min⁻¹ flow rate and air-segmented injection mode of 5 μL aliquots. Thus 28 bioethanol samples were analysed by the hTISIS system under both continuous and discrete sample introduction modes. At high temperatures, the analyte transport-related interferences caused by ethanol were removed making it possible to use external calibration. The use of the hTISIS was shown to provide sensitive and accurate results for direct elemental determinations in bioethanol. The hTISIS also provided higher sensitivities and lower LODs than a conventional sample introduction system which is important for the determination of the very low concentration of elements present in bioethanol.

Lastly, Sommersacher et al.³⁸ described a method for simultaneous on-line determination of elements from a single particle during biomass combustion. During thermal biomass conversion, volatile and semi-volatile ash-forming elements are partly released from the fuel in to the gas phase. Reactions of these released ash forming elements lead to fine particle and deposit formation causing operational problems reducing the efficiency of the combustion system. A new reactor was designed with integrated ICP-MS coupling for the study of Cl, K, Na, Pb, S, and Zn release from single biomass particles. The core element of the reactor was a tube composed of 99.7% Al₂O₃ with a diameter of 50 mm, which was placed in an electrically heated oven. The tube was equipped with four lateral access boards, two for optical access and two for sample handling. Through one of the latter a sample holder was introduced which was connected to a balance. A single biomass pellet or chip was placed on the sample holder and introduced into a protective tube mounted inside the preheated reactor tube. Target temperatures up to 1000 °C were possible. The system was then sealed and reaction agent N₂/air mixtures were injected from the bottom. The protective tube was then removed and the sample was exposed to the atmosphere resulting in decomposition. The evolving gases were diluted with argon in a porous tube placed at the reactor exit. Side streams were extracted from the diluted gas and forwarded to online gas analysers, a separate side stream was extracted, further diluted with argon and introduced to the ICP-MS. Dilution steps were applied to avoid condensation of ash forming vapours in the sample lines and to keep the gas composition within the instrument detection range. Time resolved analysis was performed to track the release of the elements during the analysis. The combination of the ICP-MS data with that from the gas analysers and thermal gravimetric analysis gives insight into the whole combustion process.

2.3 Explosives

The characterisation of explosives continues to be an area of active research. There have been several investigations of the use of LIBS in these applications. Thus, Rao et al.³⁹ have continued their LIBS studies of nitroimidazoles using fs and ns pulsed lasers. The molecular and atomic spectral signatures of seven novel explosives molecules were investigated in both air and argon atmospheres. It was found that atomic emission was most prominent when ns laser pulse excitation was used. By contrast, molecular emissions were found to be strongest using fs laser pulses but were also dependent on the ablation atmosphere used. The LIBS data were also collected in a time-resolved mode to permit the study of the evolution of molecular and atomic species and to correlate these in relation to structure of the explosives molecules.

The classification of explosives is an important topic, particularly where rapid analysis is required. While supervised learning methods have previously been exploited for the purposes of explosives recognition, but may be biased if the training set database does not include relevant samples or substrates. Consequently, LIBS was used in combination with an unsupervised learning method to address this problem.⁴⁰ The intensities of seven emission lines and five intensity ratios were used in a hierarchical clustering analysis for this application. It was reported that the proposed method achieved improved results for the classification of explosives. The classification of plastics explosives has also been investigated.⁴¹ Unique TOF-SIMS positive and negative ion spectral signatures were collected from sampling 18 composition C-4 explosives from military and commercial sources. It was found that the negative ion spectra were more consistent with the explosive content in the samples. Partial least squares discriminant analysis of the spectra provided a means of classification of the C-4 samples. However, using Bayesian integration of the TOF-SIMS data with elemental spectra from LA-ICP-MS, classification with a much higher degree of certainty was obtained.

A laboratory based, proof of concept study combining a microwave plasma and ion trap mass spectrometer as a potential field deployable instrument was reported.⁴² Coupled together the techniques provided direct elemental, molecular and isotopic information of solid materials. A variety of substrates, including non-conductive materials, were successfully analysed by simply placing the material in the plasma without sample preparation and at ambient pressure. Although still very much in the development phase, there could be significant potential applications for such an onsite instrument in many fields of work including explosives and gunshot residues for forensics and homeland security, and on-site monitoring during remediation.

2.4 Pharmaceuticals

Changes to the requirements of the US Pharmacopeia now require that elemental impurities need to be checked to ensure pharmaceutical product compliance. The International Council on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use has also issue guidelines concerned with achieving the with the same objective (ICH guidelines). Consequently, an increasing number of papers have been published in the year under review that focus on the relatively routine application of atomic spectrometric techniques to the analysis of pharmaceuticals. Given the regulatory changes mentioned above, this trend will no doubt accelerate in the future. Applications of this type are summarised in Table 1. However, there is already a sizeable literature available, that properly used, should obviate the need to reinvent the wheel. In this context, Lewen⁴³ has published a tutorial overview of sample preparation for trace metal analysis which is recommended reading for anyone new to this field. Similarly, Jin⁴⁴ has drawn attention to the specific requirement in trace element analysis to avoid contamination from reagents, laboratory apparatus and the laboratory environment. It is to be hoped that the industry takes note of the importance of achieving the general standards of practice routinely followed elsewhere if unfortunate consequences are to be avoided.

It is relatively rare that atomic spectrometric techniques are used to measure the active component in pharmaceuticals. However, a direct method, using an AAS instrument has been proposed for the determination of propranolol.⁴⁵ Samples and standards were introduced to a 1 cm quartz cuvette was positioned in the burner head. The Mg 285.2 nm wavelength was used to obtain an analyte absorbance measurement. It was reported that the LOD for the method was 0.56 mg L⁻¹ for propranolol. Results obtained in the analysis of pharmaceutical formulations were compared to those achieved using UV-vis spectrometry and HPLC.

Finally, Jimenez et al.⁴⁶ reported an electrophoretic methods of nanoparticle size segregation in antiseptic material. Separation of different sized Ag nanoparticles was achieved using agarose gel. Once the particles had been separated, the gels were analysed using LA-ICP-MS for quantification of the Ag. The effects of different stabilising agents (polyethylene glycol, sodium dodecylbenzene sulfonate and sodium dodecyl sulfate (SDS)), buffer solutions (Tris Glycine, Tris borate EDTA buffer) and functionalising agents (mercaptosuccinic acid and proteins) were all tested. The optimised conditions were: 1% SDS, 0.1% mercaptosuccinic acid and Tris Glycine in gel buffer system. This combination enabled Ag nanoparticles of different size to be separated since there is a linear relationship between apparent mobility and mean diameter. Although the protocol worked with standards, the sample contained casein which, when treated with SDS and mercaptosuccinic acid, altered the behaviour of the nanoparticles. It was therefore not possible to characterise them in terms of size. It was noted that the combination of agarose gel electrophoresis and LA-ICP-MS confirmed the formation of a protein corona around the particles in the antiseptic sample.

2.5 Personal care products

The emphasis of analytical research related to personal care products, such as cosmetics, toiletries and sunscreens, is increasingly concerned with ensuring consumer safety, much like that of the pharmaceutical industry. One current area of concern relates to the levels of heavy metals (such as Cd and Pb) present in cosmetic products. A review has been published that summarised recent developments in sample preparation for cosmetic analysis across the analytical technique spectrum.⁴⁷ Subjects relevant to atomic spectrometry included solid- and liquid-phase extraction techniques and microwave digestion.

The application of laser-induced breakdown spectrometry to the characterisation of cosmetic products has been reported by Augusto et al.⁴⁸ The aim of the study was to identify metal contaminants (Cd, Co, Cr and Ni) in eye shadow and lipstick solid samples. The emission spectra were collected via direct laser sampling and the data was subjected analysis using a variety of chemometrics approaches (PCA, SIMCA, KNN and PLS-A). Reference concentrations for the analytes derived from conventional analysis by ICP-OES using acid digestion were used in the models. Samples were classified according to metal content below and above legislative tolerance levels (an average of 5 mg kg⁻¹). The results obtained for 46 samples demonstrated that the LIBS KNN model correctly predicted the sample classification at a level of 98%.

The use of direct solid sampling in graphite furnace AAS has never become a mainstream activity due to the difficulties of coping with the extremely high levels of spectral background generated. However, the introduction of commercial continuum source based AAS instruments has given new impetus to this area of research. Thus, Barros et al.⁴⁹ have reported a method for the direct determination of Sb in facial cosmetics (blush, eye shadow and compact powder). The main source of spectral background in this analysis was identified to be caused by SiO molecules. Zeolite and mica materials were used as precursors to generate reference spectra at the Sb 217.581 nm and 231.147 nm lines to allow correction of this specific background component. A full factorial design approach was used to optimise pyrolysis (1500 °C) and atomisation (2100 °C) temperatures, using a Pd(NO₃)₂/Mg(NO₃)₂ chemical modifier. Calibration was achieved using aqueous standards containing 0.5 to 2.25 ng Sb. Using a solid sample mass of 0.15 to 0.25 mg, Sb content was detected at 271.581 nm achieving an LOQ of 0.9 mg kg⁻¹ and RSD of 0.3 to 7.1%. The reported recoveries for Sb added to the samples were in the range 82–108%. Although these figures demonstrate the applicability of the method, they do also suggest an inherent variability that is most likely caused by sampling such small amounts of solid material.

The determination of trace level sulfur in solid samples is problematic for most atomic spectrometric techniques, because of limitations due to sensitivity, spectral interferences or sample preparation. Toluene sulfonamide formaldehyde resin can cause contract dermatitis in individuals with an allergy to the material which may be present in certain cosmetics. A method for the direct determination of sulfur in nail polish, is therefore of interest in this context.⁵⁰ Samples were analysed without any sample preparation using a portable EDXRF instrument using a PLS-DA approach to identify S in validation samples. The LOD for the method was reported as 239 mg kg⁻¹ for S and in the range 1–10 mg kg⁻¹ for six other elements (Bi, Ca, Cu, Fe, Ti and Zn).

2.6 Inks and papers

There has been increasing interest in recent years in the application of surface analysis, depth-profiling and data imaging techniques to the forensic examination of inks deposited on paper. Researchers in this field will therefore welcome the development of a searchable database intended to improve the gathering of printing ink evidence.⁵¹ The database was reported to contain 319 samples of toner, inkjet, offset and intaglio inks in use around the world. The data for the inks were generated using a combination of FTIR, SEM-EDS, LA-ICP-MS, DART-MS, and pyrolysis-GC-MS. A PLS-DA based search algorithm was developed to generate a score for the association between similar samples. It was reported that LA-ICP-MS provided the best performance of any of the individual techniques in association of ink types. Use of a combination of data from LA-ICP-MS and DART-MS further improved classification accuracy for similar inks.

However, while work continues to be reported related to the identification of inks, more recently the focus of activity has moved toward investigation of the sequence of deposition of multiple, overlapping ink sources (for example, printer ink, ballpoint ink or sealant inks) on the same document. Thus, Goacher et al.⁵² have investigated the use of TOF-SIMS in determining the correct deposition order of different intersecting black inks. However, importantly, it was found that signals from certain inks were dominant over others giving rise to possible errors in interpretation. The SIMS technique offers advantages for this sort of investigation including limited sample damage, good sensitivity for small samples and the ability to generate chemical images. Three different primary ion beams were used to in both positive and negative secondary ion detection modes to investigate this problem. In addition, chemometrics techniques (multivariate analysis techniques, principal component-analysis and multivariate curve resolution) were applied to secondary ion images and spectra from the relevant regions of interest. However, it was reported that despite the utilisation of all these approaches, the issues concerning the apparent correct order of deposition were incompletely unresolved. It was noted that evidence of ink ageing was also identified. While this is a negative finding, it is nevertheless an important contribution to the identification of technical challenges that remain outstanding in this field.

2.7 Polymers and composites

This year has seen a different spread of applications concerning the analysis of polymers and composites. As well as the sample preparation methods, different methods of analysis and the analysis of extracts into food simulants subsections, a further section has been added to cover the analysis of environmentally weathered polymers. This new section comes on the back of increased research into the presence of micron-sized plastic particulates in the environment and how they act as a sink or preconcentration hub for both inorganic and organic pollutants. Other polymers may weather in the environment and so research has been undertaken into the behaviour of these materials.

A review by Chan and Weng⁵³ discussed (with the aid of 61 references) the use of XPS and TOF-SIMS for the surface characterization of polymer blends. The review drew attention to the fact that the surface characteristics are frequently different to the bulk material because of surface segregation of the low surface energy component. The principles of the techniques were discussed and then several applications described. The advantage of XPS is that it may be used for non-conducting materials and the data output is easy to interpret. However, it is incapable of providing all the necessary information. The introduction of TOF-SIMS as a complementary technique has enabled high resolution mass spectra to be obtained (typically with a resolution of 10 000) with great sensitivity. A comparison of the techniques in terms of detection limits, resolution, sampling depths, the analytical capabilities and the amount of damage inflicted on the sample was presented in tabular form which should be helpful to a novice in the field.

The migration of metallic species from polymer packaging into foodstuffs or simulants is still a topic producing research papers. Whitt et al.⁵⁴ used the standard ASTM E1613-04 method to determine Cd, Cr, Pb, Ni and Sb in water and 5% citric acid extracts from 22 re-cycled polyethylene terephthalate (PET) materials. The effects of storage time (1, 7 and 14 days at both 7.2 and 22.2 °C) and heating using a microwave oven were tested. Unfortunately, the ASTM test requires use of ICP-OES for the analysis, which is clearly going to struggle for sensitivity. Consequently, most sample contents were below the LOD for the method. Despite this, the authors managed to conclude that neither storage time nor microwave heating increased metal leaching. However, the presence of 5% citric acid led to enhanced leaching compared with water.

The other papers of interest in this field of application concerned the leaching of nanoparticulate materials from food packaging materials. Ramos et al.⁵⁵ tested Ag migration as a function of time (up to 10 days), temperature (20–70 °C), and leaching agent (water, 3% acetic acid, 10% ethanol and 90% ethanol) from a food box and a baby bottle. Although the baby bottle contained double the Ag content, the leach rate from it was two or three orders of magnitude lower. The highest release was observed at 70 °C in acetic acid. Further investigation using single particle (SP)-ICP-MS enabled differentiation between ionic and nanoparticulate Ag. The sample preparation was optimised to preserve the integrity of the speciation. Similarly, operating conditions were also optimised. Analysis of water and acid extracts demonstrated the presence of both ionic and particulate Ag, with the particulates being in the 18–30 nm range. Leach rates were generally low, corresponding to 0.1–8.6% of total Ag; with the exception being the food box, which released up to 34% and 69% of total Ag at 40 and 70 °C, respectively.

Silver was also the main analyte tested for by Ozaki et al.,⁵⁶ who additionally determined As, Cd, Pb and Zn in leachate from plastic wares purchased in Japan. Total concentrations of the analytes in the materials were determined using ICP-MS after a nitric acid microwave digestion protocol. Levels of Ag and Zn were very high, being 21–200 and 8.4–140 mg kg⁻¹, respectively; whereas the other analytes were <LOD. Leachates (4% acetic acid, water and 20% ethanol) were tested for total leached metals but also for speciation between ionic and nanoparticulate Ag. The latter was achieved using ultracentrifugation using a unit that had a pore size of 1–2 nm followed by ICP-MS detection. The highest leach rates were observed in the acetic acid matrix, although both water and ethanol also produced some leaching. Some of the wares were known to contain nanoparticulate material whereas others were known to contain Ag, but not the form in which it was present. The ultracentrifugation experiments demonstrated that leaching of the wares known to contain nanoparticulate materials in 4% acetic acid led to dissolution of Ag ions. Conversely, leaching into water or 20% ethanol resulted in nanoparticulates being extracted.

Liu et al.⁵⁷ investigated leaching of Cu from nanocopper/low density polyethylene (LDPE) composite films in into acetic acid, 10% ethanol and real food matrices (rice vinegar, bottled water and Chinese liquor). Again, the effects of leach time and temperature (20–70 °C) on the amount leached were tested. In accordance with other studies, under the same conditions, leaching into acetic acid was far greater than into ethanol. Leaching into bottled water and Chinese liquor was negligible, whereas the rice vinegar did leach some Cu. An ICP-OES instrument was used for the analysis of the 3% acetic acid and real food matrices, whereas a GFAAS instrument was used to analyse the 10% ethanol samples. Techniques such as field emission scanning electron microscopy (FE-SEM), FTIR and atomic force microscopy (AFM) were also used to characterise the nanoparticulates present in the films. Ntim et al.⁵⁸ used SP-ICP-MS, AF4-ICP-MS, ultrafiltration and SEM measurements to determine the dissolution behaviour of Ag nanoparticles in the food simulants water, 3% acetic acid and 10% ethanol. There was clear evidence of oxidative dissolution in the acetic acid, but none in the other simulants.

The interaction of plastics with the environment is a newer area of research. Turner^59,60 has produced two papers of note in this subject area. In the first, a portable XRF instrument was used to determine metals and metalloids (As, Ba, Br, Cd, Cr, Cu, Hg, Ni, Pb, Sb, Se, Sn and Zn) as well as Cl in plastics, foams and ropes collected from beaches in South West England. The author ascribed the presence of Br and Cl in the samples to the presence of brominated and chlorinated flame retardant materials. All sample types contained Pb, sometimes at exceptional concentrations, e.g. 17 500 mg kg⁻¹. This was attributed to Pb being present historically as stabilisers, colorants and catalysts. The presence of significant amounts of Cd (sometimes exceeding 1000 mg kg⁻¹) was restricted to plastics. Individual polymer types were identified using FTIR. The second paper⁶⁰ reported the use of portable XRF for the determination of analytes including Br, Cl, Fe, Pb and Zn in 70 samples of foamed plastics collected from a beach. Fourier transform IR identified most samples as being polyurethane or polystyrene. Validation of the methodology was achieved through analysis of two polyethylene disks impregnated with analytes. These were reference materials supplied by the instrument manufacturer. Agreement to within 10% was obtained for most analytes, with the others agreeing to within 15% of nominal values.

Wang et al.⁶¹ described the characterisation of microplastics in the surface sediments from the Beijing river littoral zone. Sieving of the sediments followed by flotation in a sodium chloride solution enables separation of plastics from the sediments. The microplastics were present at 178 ± 69 to 544 ± 107 items per kg of sediment. Both FTIR and SEM imaging identified that the plastics had been weathered. A sulfuric acid/hydrogen peroxide digestion was undertaken to determine the “total” metal concentrations of the plastics. Ultrasonic cleaning followed by ICP-MS analysis of the cleaning fluids enabled the authors to determine whether metals (Cd, Cu, Ni, Pb, Ti and Zn) were adsorbed to the materials or not. It was concluded that the inherent load of these metals in the plastics was greater than the amount adsorbed from the environment, although it was noted that different plastic types had different elements on the surface. This indicated that some elements were not inherent but had been adsorbed from the environment. Imhof et al.⁶² described the flotation separation of microplastics from limnetic ecosystems using a zinc chloride solution. This enabled particles with a density of less than 1.6 kg L⁻¹ to be separated from the bulk sample. Plastic identification was accomplished through Raman microspectroscopy whereas metal content was determined using ICP-MS. A microwave digestion protocol involving hydrogen peroxide, nitric and sulfuric acids was used to determine total metal concentrations. Again, high levels of Cd, Cu and Pb were noted in both the weathered and pristine samples. These authors also concluded that most of metal present is inherent to the particles rather than adsorbed from the environment.

Sample preparation methodology using microwave assistance is another area that still garners significant research because polymers tend not to be easily dissolved. However, most atomic spectrometric techniques require some sort of dissolution prior to analysis. A method for microwave digestion of aluminium-plastic packaging materials was reported by Mi et al.⁶³ The determination of As, Cd, Cr and Pb in the digests was carried out using ICP-MS. Different mixtures of reagents and different proportions were tested to investigate which would give the most complete digestion. A mixture of sulfuric and nitric acids (1 : 7) proved optimal. Interestingly, the recovery results of additions were somewhat variable, lying in the region 83.8–111.6%. Precision for the method was also variable, lying between 0.5 and 7.4% RSD. Krzyzaniak et al.⁶⁴ described a microwave induced combustion (MIC) procedure to prepare high purity polyimide samples prior to the determination of halogens and S using ion chromatography (for Cl, F and S) and/or ICP-MS (for Br and I). This method used 600 mg of sample in oxygen and a microwave power of 1400 W for 5 minutes. The absorbing solution was 6 mL of 50 mmol L⁻¹ ammonium hydroxide. After cooling for 20 min, the solution was diluted to 25 mL prior to analysis. The results from the method developed were compared with those obtained using a microwave-assisted digestion using concentrated nitric acid. The CRM (ERM EC680k low density polyethylene) was used to validate the method. Results of a student t-test on the data obtained from ion chromatography demonstrated that there was no significant difference between the experimental values obtained for the CRM and the certified values.

Hallerstig et al. described the hot-block digestion of soft silicone wound dressings using decylbenzene sulfonic acid in propanol.⁶⁵ Approximately 50 mg of silicone was digested at 150 °C for one hour in the presence of 2 mL of the acid (70%) in propanol and 250 μL of 10 000 mg L⁻¹ In solution. After cooling and extraction in 18 mL of hydrochloric acid, the black organic layer was removed and the acid volume reduced to only 5 mL using a pipette. This was then diluted to 50 mL using 10% hydrochloric acid. After filtration, the digest was analysed using ICP-OES using In, added as an internal standard. Matrix matched standards were required for best accuracy. Method validation was achieved through the analysis of three different samples of known composition (a low, medium and high concentration) and with results being obtained by three independent workers. Accuracy and precision were both excellent (99–104%) and 0.3–4.4% RSD, respectively.

The direct analysis of plastics and polymers using laser-induced breakdown spectroscopy continues to receive attention, mainly to avoid the need for lengthy or potentially hazardous digestions. Trautner et al.⁶⁶ employed a LIBS system with an F-2 laser operating at 157 nm and a Nd : YAG one at 532 nm. The LIBS systems operated in both nitrogen and argon background gases as well as air for the analysis of polyethylene and a rubber material. An Echelle spectrometer with an intensified charge coupled device (CCD) was used for the measurement for atomic, ionic and molecular signals, including C₂ Swan and CN bands. Wang et al.⁶⁷ reported the application of LIBS for the determination of Al, C, Fe, O, Si and Zn in room-temperature vulcanised silicone rubber samples. Results were in broad agreement with those obtained using SEM-EDS. These authors also studied the ablation properties and, using SEM, found that craters of only 200–400 μm were created and that there was no difference in the hydrophobicity between the ablated and non-ablated areas. This indicated that no ample modification had occurred and hence, that LIBS was virtually non-destructive. The depth of the crater increased proportionately with laser shot number and increased with laser energy. A concomitant increase in electron number density and the intensity of analyte emission was also observed. The authors concluded that the methodology would be of most use when used on-line during the preparation process.

Kayar et al. used laser ablation inductively coupled mass spectrometry for the determination of heavy metals in plastics.⁶⁸ Operating conditions such as plasma gas flow rate, laser energy, spot size and scan rate, were optimised to yield maximum intensities and the best precision. Method validation was achieved through the analysis of the CRM ERM EC681k low density polyethylene, and the materials sample #950 polypropylene and sample #11051 PVC as well as comparison of data with those obtained using EDXRF. Limits of detection for the LA-ICP-MS method were reported as 1, 2, 1 and 3 mg kg⁻¹ for Cr, Cd, Hg and Pb, respectively. Precision and accuracy were less impressive, with values of over 10% RSD being commonplace and accuracy deviating between 0 and 40% from assigned values. The use of LA-ICP-MS as a validation method for Br results obtained from the analysis of WEEE polymers using a portable XRF instrument (p-XRF) had also been reported.⁶⁹ Customized standard materials of brominated fire retardants ranging between 0.08 and 12% Br in styrene were used as external calibrants for the p-XRF instrument. Detection limits were 0.0004% for LA-ICP-MS and 0.0011% for p-XRF. Results from the two techniques were in accordance, demonstrating the utility of p-XRF.

Finally, secondary ion mass spectrometry techniques have also been applied the characterisation of polymers. Cluster ions are known to enhance the sputter yield whilst suppressing damage accumulation and are of use for soft samples, e.g. polymers. Chu et al.⁷⁰ discussed the depth profile analysis of PET using an Ar₂₅₀₀⁺ cluster beam along with a low energy (200 V) O₂⁺ co-sputtering beam. The mixed beam retained secondary ion intensity (>95%) at depth compared with the used pristine surface. In addition, the cluster beam masked the damage inflicted by the O₂⁺ beam. A helium ion microscope has beam interfaced with a SIMS system for the study of polymeric materials.⁷¹ This combination promises improved lateral resolution, but optimal results can only be obtained when the interaction of the helium or neon primary ions with the sample is fully controlled. The effect of primary ion damage (roughness) on the polymer surface was studied and results using the novel system compared with those using the traditional SIMS Ar⁺ primary ion. Simulations and modelling were also used to clarify the results. Preferential sputtering of C was observed for some of the primary ions and was also dependent on the impact energy. For helium primary ions, low impact energies caused the problem of preferential sputtering whereas for the heavier primary ions, preferential sputtering was sample dependent. Pelster et al.⁷² discussed the imaging of heterogeneous topographically complex polymer systems using TOF-SIMS and laser post-ionization secondary neutral mass spectrometry (laser-SNMS). The laser-SNMS had between one and three orders of magnitude higher sensitivity depending on the polymer, had better sub-μm resolution, had a higher dynamic range and showed superior performance on topographically complex systems when compared with the TOF-SIMS.

2.8 Paints and coatings

Papers continue to be published concerning the analysis of industrial paints, coatings and related component materials, and a summary of these is presented in Table 1. While much of the literature reflected well-established practice, it appears that the current focus of research in this application area is in the study of the environmental impact of paints. For example, XPS has been used to quantify the Cr^VI content of thin (2 μm) protective coatings on electrical steel.⁷³ An ultra-low angle microtome was used to generate extended wedge-shaped tapers throughout the coating to facilitate bulk analysis by XPS. It was reported that both elemental and Cr^VI specific content and its distribution in the coating could be quantified using this approach. The method was found to be advantageous in comparison with an existing photometric method, based on alkaline extraction, because it did not require information about coating thickness or density.

2.9 Cultural heritage: paintings

The application of atomic spectrometric techniques to the characterisation of paints and pigments used in paintings of historical or cultural value is growing year on year. One trend worth noting has been an increase in applications of atomic spectrometry techniques to the study of wall paintings and murals. However, the primary focus of much of the literature is on reporting new insights relating to the origin of materials used or in the provenance or genesis of the specific artworks rather than on analytical developments per se. A summary of these applications is provided in Table 2.

Table 2 Applications of atomic spectrometry to the analysis of paintings

Element	Matrix	Technique	Sample treatment/comments	Reference
Various (8)	Artistic paints (France, 19^th century)	XRF and SEM-EDS, HPLC-MS	Improved HF-based extraction method for the multi-technique identification of 35 organic dyes and other components present in yellow and red paint samples. Eight elements (Al, Ca, Cu, K S, Si, Sn and Zn) were detected confirming the presence of aluminium hydrate and tin salts as carriers, and chalk	98
Various	Church icon paint from El Mini, Egypt	SEM-EDS, ICP-OES, and PS	Description of the use of multiple analytical techniques for the investigation of coloured components in four painted icons	99
Various	Medieval paintings from Gotland, Sweden	SEM-EDS and MC-ICP-MS	Pigment samples (116) from medieval paintings were examined using SEM-EDS. Isotope ratios for 6 Pb-containing pigments were measured using MC-ICP-MS	100
Various (5)	Mural church painting from Magurele, Romania	XRF	Use of portable instrument to identify Cr, Hg, Fe, Pb and Zn containing pigments used in an oil based mural painting	101
Various (10)	Painted 19^th century icon	XRF, FTIR and optical microscopy	Study of the paint layer on a wooden icon using a multi-technique approach. The presence of Al, Ca, Cu, Zn, Ti, Si, Sr, As, Hg and Pb in pigments was identified by XRF thereby providing an indirect means of estimating the age of the icon	102
Various	Painting by Rembrandt (ca. 1652)	Light microscopy, SEM-EDS and XRF	Multi-technique approach investigation of authenticity and painting construction for conservation purposes. A cobalt containing blue pigment smalt characteristic of Rembrandt was identified and XRF was also used for non-destructive element mapping of painting areas	103
Various (8)	Painting by Courbet, Paris, France	XRF and SEM-EDS	Description of the use of macro-XRF for 2D elemental mapping of (As, Ba, Ca, Cr, Cu, Fe, Pb and Zn) in the painting and confocal XRF for depth profiling of paint layers. Paint cross sections were also studied using SEM-EDS. The order of 3 successive compositions of the painting were established	104
Various	Pigments and paint layers in Mudejar polychrome wood ceilings from Seville, Spain	XRF and SEM-EDS	Use of portable XRF to study the elemental content of pigments and re-painting of ceilings and doors. A SEM-EDS system was used to examine small fragments and layer cross sections	105
Various	Pigments under wall mosaics from Ravenna, Italy	EDXRF, FTIR, Raman and fibre optic reflectance spectrometry	Multi-technique investigation of pigments in drawings and painted layers under 5^th to 7^th centuries wall mosaics	106
Various	Pigments in a Qing dynasty meticulous painting	XRF, Raman microscopy and 3D video microscopy	Multi-technique non-destructive analysis approach to identification of pigments	107
Various	Pigments in Korean Buddhist Paintings	TOF-SIMS and p-XRF	Identification of pigments used in temple paintings from the using a combination of TOF-SIMS in positive and negative ion modes and p-XRF for elemental analysis	108
Various	Renaissance painting	XRF	Application of confocal XRF to the non-destructive depth profiling of a painting from the Louvre. The technique was reported to be able to identify pigments used and the order of application of paint by the artist	109
Various	Roman wall paintings from northern Anatolia, Turkey	XRD, XRF, Raman and SEM-EDS	Multi-technique approach to the investigation of pigments and arriccio plasters used in 2^nd to 4^th century AD wall paintings, confirming the presence of calcite, yellow and red ochre, goethite, cinnabar, dolomite, green earth and carbon black	110
Various (5)	Roman mural paintings from Wossingen and Mulheim-Karlich, Germany	SR-XRF	Use of Sr macro-XRF for non-destructive measurement Ca, Cu, Fe, K and Pb in coloured green pigments present in 2^nd and 3^rd century painting fragments. It was reported the creation of 2D elemental maps assisted in discrimination between the pigments, plaster, different layers and impurities in wall paintings	111
Various	Roman wall paintings from Seville, Spain	XRF, XRD and UV-vis fibre optic reflectance spectrometry	Study of 1^st to 2^nd century wall paintings using multi-technique approach including portable XRF/XRD for the identification of coloured materials	112
Various	Wall painting in Necker Children’s hospital, Paris France	XRF, XRD and SEM	Multi-technique approach including FTIR and Raman to the characterisation of fragments of mural painting	113
Various	Wall painting in Ethiopian Church	XRF, SEM-EDS, micro-Raman, ATR-FTIR, XRD and pyrolysis GC-MS	Multi-technique characterisation of wall painting stratigraphy, paint layers, support material and pigment composition. Portable XRF was used for in situ measurement of elemental content	114
Various	Wall paintings (10^th to 16^th centuries) from the Seville Alcazar, Spain	XRF, XRD and EM-EDS	Paintings were studied using a multi-technique approach. Measurements to identify pigments using portable XRF and combined XRF/XRD were carried out in situ and grazing angle incidence XRD and micro-Raman spectroscopy were used to examine sample cross sections	115
Various	Wall paintings from Buddhist cave on the northern Silk Road in the Turfan collection, Berlin	SEM-EDS, EDXRF, micro-XRF, XRD and Raman	Multi-technique investigation of cave painting materials. Layers of material comprising, earthen render, gypsum and paint containing inorganic pigments were identified	116

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The development of non-invasive and non-destructive X-ray methods for the study of historical artistic works has been the subject of a recent review by Janssens et al.⁹² Methods for the characterisation of entire paintings, paint samples and pigments using XRF, XRD and XAS were considered. Specific attention was paid to the μ-XRF technique for imaging of elemental distributions within paint multilayers or within microsamples. It was noted that a combination of these techniques and FTIR and Raman spectrometry is often required to fully elucidate chemical changes that have taken place within paintings. The prospects of using XRF scanning and full-field hyperspectral imaging for examining entire artefacts are also favourably considered.

It is evident from the current literature that the attractions of the use of portable instrumentation to study artworks in situ rather than by physical sampling or by examination in the laboratory are becoming clear. Consequently, a review of experiences in using the MOLAB integrated suite of portable instruments (including XRF, mid and near FTIR, UV-vis, Raman and XRD) is timely.⁹³ The effect these techniques have had in advancing understanding of painting materials and execution methods was explored. The article also covered both point analysis and hyperspectral imaging approaches to characterisation.

The characterisation of artworks is heavily reliant on the identification of paints and pigments used. Consequently, the development of a spectral image reference database for 240 reference pigments will be of interest to other workers in this field.⁹⁴ Digital microscopy with visible and infra-red illumination and XRF were used in combination with image processing to generate reference spectra and images for the pigments, facilitating identification. The application of this approach to the characterisation of pigments in fresco paintings was assessed. The features of a pilot XRF imaging system for mapping pigments in historical paintings have been described by Mindur et al.⁹⁵ A pinhole camera, a gas electron multiplier detector and a dedicated data acquisition system were used to investigate hidden layers including ‘phantom stripes’. It was reported that the system could resolve energy differences in signals for pigments containing elements differing only by Z = 1, which was critical in identifying hidden layers.

An X-ray fluorescence spectrometer has been used to detect a hidden drawing inside a painting.⁹⁶ The laboratory constructed confocal 3D-XRF instrument contained two poly-capillary X-ray lenses to generate 3D elemental maps. The system was used to image the distribution of Prussian blue (as Fe). By using 3D imaging of XRF intensities from elements in the paint layers, a drawing of a hidden cat in a work by Vincent van Gogh was revealed! In a similar vein, a high-definition XRF-mapping technique was used to find a hidden portrait by Edward Degas.⁹⁷ A synchrotron radiation source was used with a 31.6 mega-pixel XRF scanning system to derive elemental maps and image processing was then used to generate a false colour representation of hidden layers. It was claimed that the approach yielded a technical understanding of a painting that could not be resolved using conventional techniques. However, given that other methods have been published for the detection of hidden layers within paintings, this subject is likely to be of continuing interest in heritage science going forward.

3. Inorganic chemicals and materials

Over the last few years, the use of atomic spectrometry in applications involving inorganic chemicals and materials has changed both in nature and volume. Consequently, the structure of this year’s review has been adjusted to reflect these changes. A lack of relevant papers in the field of forensics has seen this subject heading removed. The section combines both inorganic chemicals and materials and now includes applications involving nuclear materials, glass and electronic materials previously in a separate functional materials section.

3.1 Inorganic chemicals

There was an increase in the volume of publications concerning the analysis of salts in the current review period. The direct analysis capability of LIBS was exploited in several such investigations. For example, a calibration free LIBS approach was used to determine Ca, Cl, Fe, K, Li, Mg, Na, Si and Sr in black salt.¹¹⁷ Sampling was achieved directly using the second harmonic (532 nm) of a Q-switched Nd : YAG laser operated at ambient pressure. Results achieved using the method were compared with those obtained by ICP-OES and good agreement was reported. Principle component analysis of chemical fingerprints obtained by simultaneous LIBS and LA-ICP-MS measurements was used to classify salt samples by Lee et al.¹¹⁸ The tandem instrument used performed rapid spectroscopic analysis of the laser ablation plasma, prior to transportation of ablated material to the ICP-MS. Minor metallic elements (Ca, K and Mg) detected by LIBS and the non-metallic (I) and trace heavy metal (Ba, Pb and W) elements detected by LA-ICP-MS provided complementary chemical information to distinguish salt samples.

Zheng et al.¹¹⁹ produced a comparative study of the effect of sample preparation on the determination of Cl in different salt mixtures by LIBS. Samples of three types of mixed salt powders (NaCl + KBr; NaCl + MgSO₄; and NaCl + Na₂CO₃) were prepared as pressed pellets and as a dried solution layer on an aluminium target with varying chloride concentrations. Chlorine calibration curves were prepared for each preparation method and comparison of the slopes of each mixture type provided an assessment of the matrix effect related to the different mineral salt matrix. A similar chlorine response for calibrations in each of the mixture types prepared by dried solution method showed an absence in matrix effects. This result was further confirmed by the consistence of the measured temperatures and the electron densities of the produced plasmas. In contrast, the slopes of the chlorine calibration curves exhibited significant variation for different pellet samples corresponding to different powder mixtures. Both ICP-OES and ICP-MS were used for the determination of 27 trace elements in sea, rock and roasted bamboo salts from seven countries.¹²⁰ Detection limits were reported from 7.032 (Mg) to 13.915 (S) for ICP-OES and from 0.023 (Sr) to 0.228 (Ba) ng g⁻¹ for ICP-MS, respectively. Spike recoveries achieved for fortified samples were from 91.3 to 107.6%.

An interesting paper was published on the use of sonoluminescence spectroscopy for the determination of 15 elements in a concentrated solution of table salt.¹²¹ Sonoluminescence, a phenomenon known since the 1930’s, is where ultrasound is used to collapse small bubbles of inert gas passing through a solution, the energy of which results in the emission of light from nearby atoms. The authors developed an instrument based on an atomic absorption spectrometer and a sonoluminescence source cell operated with variable ultrasonic frequency from 18 to 47 kHz and intensity regulated from 0.05 to 25 W cm⁻². It was shown that the proposed analytical technique gave results at high concentrations with better metrological characteristics than AAS because the samples were not diluted.

The determination of trace metals impurities in pure substances is critical in many industries and is the staple of atomic spectrometry. A quick and simple method for the analysis of high purity cadmium and cadmium dioxide by ETAAS was described.¹²² Samples were digested in nitric acid prior to analysis. Full analytical details were presented and the developed method allowed the determination of Ag, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Sn with detection limits reported in the range 2–60 μg kg⁻¹. Khomichenko et al.¹²³ studied the matrix interferences caused by the bulk elements during the determination of 42 trace elements in silicon and germanium oxides. The optimised method permitted accurate spike recoveries for all analytes with LODs reported in the μg kg⁻¹ range. Wu et al.¹²⁴ detailed a reaction cell procedure for the determination of Eu, among other analytes, in ultra-pure barium carbonate by ICP-MS. The on-mass determination of Eu in a barium matrix was impeded by the isobaric interferences of oxides (¹³⁵Ba¹⁶O⁺ on ¹⁵¹Eu⁺ and ¹³⁷Ba¹⁶O⁺ on ¹⁵³Eu⁺). Additionally, the simple O₂ reaction products were similarly interfered (¹³⁵Ba¹⁶O₂⁺ on ¹⁵¹Eu¹⁶O₂⁺ and ¹³⁷Ba¹⁶O₂⁺ on ¹⁵¹Eu¹⁶O₂⁺). Two methods to overcome this were reported: measuring off mass for EuO₂⁺; or a more sensitive NH₃ reaction gas mode. The latter allowed the measurement of Eu on mass as NH₃ did not react with Eu⁺ but with BaO⁺ to form a neutral product, BaO. The proposed method enabled the detection of Eu as low as 2 ng L⁻¹. A detailed procedure for the determination of REEs in yttrium, gadolinium and neodymium oxides by direct arc atomic emission spectroscopy was given by Koshel et al.¹²⁵ The parameters investigated included sample weight, exposure duration, current intensity, electrode type, and inter-electrode distance. The optimised method allowed the determination of analytes over the range 0.001–0.1 wt%.

The method of standard dilution analysis (SDA) was applied for the analysis of high purity acids by triple quadrupole ICP-MS.¹²⁶ The SDA method is based on the preparation of only two analytical solutions containing the same amount of sample and internal standard but differing amounts of analytes. Using continuous monitoring, a spiked sample solution was initially measured forming a plateau in signal. Then a second, unspiked solution, was added to the same tube resulting in a signal drop as the two solutions slowly mixed. As dilution slowly took place, the internal standard concentration remained constant but the analyte concentrations reduced and many calibration points were generated over time from the signal drop. Since the concentration of the spiked solution and the internal standard were known, the concentration of the analytes in the sample could be calculated. The combination of this calibration approach with a triple quadrupole ICP-MS system overcame the matrix, transport and spectral interferences formerly observed during the analysis of concentrated acids. The method allowed the determination of As, Cr and Ni with LODs of 6, 10 and 30 ng L⁻¹ and achieved spike recoveries in the range 90–114%.

3.2 Fertilisers

Fertilisers represent a major source of trace metal contaminants in agricultural soils and thus the quantification of such is a popular field of research. Azzi et al. studied the potential impact of 44 commercial phosphate fertilisers used across the eastern Mediterranean region, in terms of metals inputs.¹²⁷ Direct analysis by XRF was performed for the determination of Ag, Bi, Mn, Mo, Nb, Pd, Sb and Zr, whilst Ca, Cd, Cu, Fe, Pb and Zn were determined by AAS following digestion in aqua regia. Mineral phases were also confirmed by XRD and it was shown that sulfates were the main Cd-bearing phases. The concentrations of Pb and Zn were also found to be strongly correlated, as were Ag, Mo, Nb, Pd, Sb and P₂O₅. It was calculated, based on concentration and fertiliser usage, that the annual average inputs of Cd, Cu, Pb and Zn were 6, 124, 26 and 922 g ha⁻¹. This was below the allowed limits, but could become a concern due to accumulation.

The benefits of measuring off-mass, following reaction with oxygen in an octopole reaction cell, for the determination of As in fertilisers with high REE contents were highlighted.¹²⁸ Direct measurement of ⁷⁵As⁺ resulted in recoveries from 59 to 151% for a range of standard materials, due to several isobaric interferences, including ¹⁵⁰Nd²⁺ and ¹⁵⁰Sm²⁺. However, reaction with oxygen and measurement of ⁷⁵As¹⁶O⁺ removed said interferences and yielded much improved recoveries from 81 to 105%. Full method details were included in the paper. An optimised method for the determination of thorium and uranium in mineral fertiliser, with LODs of 0.6 and 0.8 μg g⁻¹ respectively, was also presented.¹²⁹ Also, the method development and uncertainty estimation for B, S and P determination in mineral fertilisers by ICP-OES was detailed.¹³⁰ The extraction of analytes was conducted in open flasks and decomposed in a closed microwave vessel. However, it was shown that when S is present in the elemental form, the sample must be digested in a closed vessel for accurate recoveries. The uncertainties of the method were estimated according to the bottom-up approach, which were 7.1, 4.3, and 7.8% for B, S and P, respectively.

Contaminants (Cd, Cr, and Pb) as well as minor (B, Cu, Mn, Na, and Zn) and major (Ca and Mg) elements were directly determined in solid fertiliser samples prepared as pressed pellets using LIBS.¹³¹ Optimal results were obtained using a laser energy of 75 mJ, a spot size of 50 μm, and 2.0 μs delay. Good correlation was obtained between the proposed LIBS method and the reference values obtained with ICP-OES and LODs of from 2 mg kg⁻¹ (Cd) to 1% (Zn) were achieved.

3.3 Inorganic materials

The direct determination of elements in solid samples without the need for prior decomposition has obvious benefits with regards to speed and ease of analysis. The optimisation of a solid sampling HR-CS-GFAAS method for the determination of Li and Na in yttrium oxyorthosilicate scintillator materials was described by Laczai and colleagues.¹³² The effect of sample mass, analytical lines and pyrolysis/atomization heating programs were all investigated. Using a calibration method of standard addition, detection limits of 20 μg g⁻¹ and 80 μg g⁻¹ for Li and Na respectively were achieved. The same group also reported the optimisation of the same set up for the determination of Mg in lithium niobate crystals over the range 0.74–7.25 mg g⁻¹.¹³³ Using the secondary Mg I 215.4353 nm spectral line, 1500 °C pyrolysis and 2400 °C atomization temperatures, precision of <6.3% was attained. The accuracy of both methods was checked by FAAS using solutions of digested samples.

The classification of quartz was achieved by discriminant functional analysis following characterisation by LIBS.¹³⁴ Plasmas were produced at ambient pressure using Nd : YAG laser at fundamental harmonic mode (1064 nm) and the system was optimised to get well resolved emission spectrum at the early stage of plasma generation. The strongest predictor in discriminating of quartz was the intensities ratios of Si I (252.41)/Li I (670.79), Si I (252.41)/Na I (588.99 + 589.59), and Si I (252.85)/Li I (670.79) spectral lines. With the conjunction of DFA and LIBS, the group membership of five unknown quartz types was predicted with 99% precision. Often the averaging multiple laser pulses are required to achieve appropriate signal to noise ratios. However, with each pulse the depth and shape of the laser crater changes, thus changing the plasma characteristics. It is therefore difficult to relate the ion signal to the sample concentration of the analyte. Belyaev et al.¹³⁵ reported the use of laser-induced evaporation with subsequent TOF-MS as a means of overcoming this for determining trace impurities in corundum. A laser power density in the order of 10⁵ W cm⁻², sufficient for the intensive evaporation of the matrix oxide without plasma formation, was used. The neutral vapour particles formed entered the mass spectrometer and were ionised using electron impact. Thus, the evaporation and ionisation processes were independent. The analysis obtained with this method was in good agreement with results from ICP-MS.

The varying forms of field flow fractionation have been used less in this review period than in previous ones. Asymmetric flow field flow fractionation (AF4) coupled with ICP-MS was used by several workers to characterise nanoparticles. Helsper et al.¹³⁶ used the combination to characterise seven commercial titanium dioxide pigments and two other well-defined materials. The operating conditions were optimised for linearity, LOD, recovery, repeatability and reproducibility. Multi-elemental scans demonstrated that Zr was present in many of the samples and this co-eluted with the Ti. The other two materials were found to contain Al. The particle sizing was corroborated using several different techniques including AF4 coupled with MALS, SP-ICP-MS, SEM and particle tracking analysis. The last of these techniques was inappropriate, but the size distributions obtained using the other techniques were similar. Number-based size distribution techniques such as SEM and SP-ICP-MS yielded smaller modal diameters than the mass-based technique of AF4-ICP-MS.

Sanchez-Garcia et al.¹³⁷ described the characterisation of cerium oxide nanoparticles using AF4-ICP-MS and hollow fibre flow field flow fractionation (H5F)-ICP-MS. Full experimental details as well as the advantages and limitations of each were discussed. Optimization of the operating conditions including carrier composition, pH, ionic strength, cross flow and carrier flow were studied to optimise the separation, recovery and repeatability. Unfortunately, no size standards of cerium oxide nanoparticles exist and so the authors attempted to use Ag, Au and SiO₂ nanoparticles of known size. Both the Ag and Au nanoparticles did not show comparable behaviour to the CeO₂, but the SiO₂ yielded size distributions in agreement with those predicted by theory. They were therefore used to estimate the sizes of the CeO₂ nanoparticles. When compared with results obtained using TEM, dynamic light scattering and XRD, the data were reported as being “in adequate accordance”. In general, the AF4 variant was favourable to the H5F because it was more versatile. However, the H5F system had less carrier consumption and a marginally higher recovery. A LOD of 0.9 μg L⁻¹ CeO₂ was obtained for an injection volume of 100 μL, corresponding to a number concentration of 1.8 × 10¹² L⁻¹ for nanoparticles of 5 nm diameter.

Sedimentation field flow fractionation is another variant of FFF. This variant uses a centrifugal system to separate different size particles. Soto-Alvaredo et al.¹³⁸ used the system coupled on-line with multiangle light scattering and ICP-MS-MS detection to determine the size and agglomeration state of TiO₂ nanoparticles. As well as the challenge of separating the different sized particles, the detection of Ti using ICP-MS also has problems. There are significant isobaric and polyatomic interferences which need to be overcome for reliable data to be obtained. The instrument used in this study employed ammonia and helium as the reaction gas. These react selectively with the analyte rather than the interferences and the product ions are then selected by the second quadrupole as target ions. The result was that a LOD of 10 ng L⁻¹ was obtained. The operating parameters for the sedimentation FFF system were also optimised using two commercially available model TiO₂ nanoparticle standards of nominal diameter 21 and 50 nm. Particle characterisation in the lake water samples was achieved using the multiangle light scattering system, the FFF system coupled with the ICP instrument and independently with TEM measurements.

Continuum source electrothermal atomic absorption spectrometry (CS-ETAAS) has been used to characterise inorganic nanoparticles.¹³⁹ In the first example, magnetic nanoparticles were solid sampled and the Fe content as well as the particle size determined. The novelty comes in the particle size measurement. The slope of the rising edge of the absorbance signal was correlated with the particle diameter. For instance, slopes of 0.009, 0.015 and 0.017 were obtained for particles of 5, 17 and 30 nm, respectively. The area and upslope of the absorbance signals obtained using the 352.614 nm line were used for the measurements. The furnace parameters of atomisation heating rate, atomisation temperature, pyrolysis heating rate, pyrolysis temperature and pyrolysis hold time were all optimised using two multiple response surface designs (central composite designs). A mass of 0.1 mg of nanoparticles was used for the optimisation experiments. All areas were normalised to the exact sample mass used to overcome difficulties in weighing the same mass. An aqueous series of standards were used for calibration for quantification of the Fe content. Method validation for particle size and Fe content was achieved through TEM and SEM, respectively.

3.4 Catalysts

This review period has seen a surge in the documented use of the hot filtration test for assessing the heterogeneity and stability of catalysts during reaction conditions. The simple procedure typically involves filtering the solid catalyst from the reaction liquor midway through the reaction cycle and analyses of traces of the active metal remaining in the solution by AAS, ICP-OES and/or ICP-MS. Catalyst materials for Suzuki and Heck C–C coupling reactions such as; boehmite nanoparticle supported Pd(0),^140,141 Ni–Pd bimetallic nanoparticles¹⁴² and silica supported Pd¹⁴³ were all assessed by the method, with the latter showing excessive leaching of the active metal. The leaching of Pd from graphene anchored Pd transfer hydrogenation catalysts,¹⁴⁴ Ag from supported Ag nanoparticles,¹⁴⁵ and W from W-containing apatite¹⁴⁶ and porous clay supported WO₃ catalysts¹⁴⁷ was also documented.

A method for the determination of Ni, Ce and Zr supported on carbon nanotubes by slurry nebulisation ICP-OES was detailed by Bok-Badura and colleagues.¹⁴⁸ Fine powder samples were dispersed in a 1% Triton X-100 solution using an ultrasonic bath prior to measurement by ICP-OES. The instrument was calibrated using stock standard solution prepared in a 1% Triton X-100 solution and analysis performed with alteration to typical operating parameters. The optimum slurry concentration was determined to be 40–400 mg L⁻¹, where concentration greater than this caused blockages of the peristaltic pump tubing and nebuliser.

Probing the surface atomic and structural composition is key to understanding the catalytic activity. Although most surface specific techniques require the measurement be performed under high vacuum conditions due to low tolerance of the detector towards gases and moisture, and a reduction in the limited free path due to atmospheric atoms. However, the development of NAP XPS, where measurements can be made in the presence of low pressure reactive species, is becoming a powerful in the field of catalysis. A novel experimental set up based on separation of the vacuum environment from the pressure environment using a graphene coated silicon nitride grid containing an array of micrometre sized holes was presented.¹⁴⁹ Successful operation was demonstrated with three different examples: the oxidation/reduction reaction of iridium, copper nanoparticles and the hydrogenation of propyne on Pd black catalyst powders. Feng et al.¹⁵⁰ used a quasi-in situ SIMS set up and in situ NAP XPS to study the changes of the surface composition of ORR electrocatalysts under cathodic/anodic polarization with oxygen at elevated temperature. Showing cation segregation and oxidation at the surface, this is proposed to be a deactivation route for these materials. The origin of catalytic activity of the metal hydride ZrCoH_x towards CO₂ reduction was investigated by Kato et al.¹⁵¹ Surface mapping of a catalyst prepared with substituted deuterium using TOF-SIMS showed an oxide layer with heterogeneous distribution of Zr and Co. Negative ion mapping revealed D⁻ to be mainly associated with regions rich in Zr, whilst gas phased H was absorbed to areas rich in Co. Experiments with NAP-XPS also showed that the surface CoO layers were rapidly reduced in hydrogen at elevated temperature, whilst the Zr 3d spectra revealed the formation of hydroxyl groups from the ZrO layer. It was believed that this apparent transfer of hydrogen aids the formation of methane and water following CO₂ adsorption on the active surface.

Time of flight secondary ion mass spectrometry is becoming increasingly used for the analysis of catalytically active materials. It was used by Gerstl et al.¹⁵² to understand sulfur poisoning of gadolinia doped ceria electrocatalysts in hydrogen sulfide containing environments. Data showed the incorporation of sulfur in to the bulk phase resulting in possible bulk phase changes. Additionally, unexpectedly high concentrations of segregated surface silicon were found, explaining observations of the change of surface polarisation resistance. Barreca et al.¹⁵³ used TOF-SIMS imaging of titanium supported Fe₂O₃/WO₃ composites prepared by chemical vapour deposition and RF-sputtering to understand and control the W/Fe ratio during preparation. The study showed uniform Fe₂O₃ globular particles decorated with tine WO₃ aggregates, whose content could be controlled by modulation of the sputtering time. Other applications include the imaging of gold nanoparticle doped organic–inorganic poly(titanium oxide) nanocomposites used a photocatalysts,¹⁵⁴ measuring the uniformity of thin film cobalt oxides deposits on glass substrates,¹⁵⁵ and the depth profiling of photoactive tungsten doped titania thin films.¹⁵⁶

Although not strictly covered by the remit of this review, an unusual adaption of a triple quadrupole ICP-MS instrument was reportedly used for the screening of the reactivity of unstable monovalent metal ions towards azide–alkyne cycloaddition.¹⁵⁷ Metal ions were produced by in the plasma in the same way a standard ICP-MS the target mass ion was then selected in the first quadrupole. Reactant gases, phenylacetylene and benzylazide, were then introduced into the collision reaction cell and the subsequent measurement of the product and intermediate masses following separation in the second quadrupole.

3.5 Building materials

Anthropogenic activities, such as high-altitude flights and living in buildings, have enhanced the public exposure to natural radiation. Mossini et al.¹⁵⁸ provided a clear and exhaustive review of natural radionuclide measurement procedures for building materials. The direct determination of radioisotopes by ICP-MS was highlighted as a promising alternative to standard national normatives (Dutch NEN 5697, Italian UNI 10797, Polish ITB 455), based on gamma spectrometry.

Unsurprisingly, given it is one of the common causes of failure, the determination of chloride ingress in concrete continues to be an area of interest. Paul et al.¹⁵⁹ used XRF to confirm that mechanically induced cracks in concrete specimens lead to higher chloride migration than in un-cracked specimens following cyclic exposure to chloride solutions. A more in-depth investigation was performed using LA-ICP-MS by Bonta et al.¹⁶⁰ Drilled core samples were analysed directly, simplifying the sample preparation compared to the standard Volhard method. Additionally, the multi-element capability of the technique allowed the differentiation between cement and binder phases permitting the selective determination of chloride in each phase. Improvement in the detection limits for Cl determination in concrete by LIBS was reported.¹⁶¹ The analysis is carried out in the cooling phase of the plasma, by the observation of newly formed radicals and diatomic molecules whose strong molecular emission bands can often be found in the visible spectral region and offer a higher detection sensitivity over elemental emission lines.

A detailed method for the analysis of cement pore solutions by ICP-OES was reported by Caruso et al.¹⁶² The paper highlights the criticality of matrix matching calibration standards to the specimen solutions, where high concentrations of elements not to be determined are present. Pore solutions were separated from concrete pastes by filtration through a 0.45 μm Nylon membrane followed by acidification with HNO₃ prior to analysis. The authors claimed a 34-fold reduction in LODs for the analytes Al, Fe, Mg and Si, compared to other reported work. The monitoring of solubilised silicon concentrations in pore solutions by ICP-OES to examine the promotion of the alkali–silica reaction (ASR) due to de-icing salt ingress was also discussed.¹⁶³ Hydrated cement pastes with increasing salt content were prepared and cast into bars, then subsequently stored in a 20% salt solution for up to 1 year. Pore solutions were mechanically expressed from the bars and the solubilised Si determined.

A laser-induced breakdown spectrometry-based cement QC system was developed by Guo and colleagues.¹⁶⁴ A description of the overall structure, optics and internal standard method of analysis was provided. Using the method, the determination of Al₂O₃, CaO, SiO₂ and Fe₂O₃ content in cement materials was achieved with 0.13%, 0.46%, 0.25%, 0.05% absolute error compared to XRF analysis. A spectrum standardisation method for the determination of Al, Ca, Si and Fe in cement raw materials by LIBS was also detailed by a team at the same institute.¹⁶⁵ The basic idea behind the spectrum standardization method is that there exists a standard state for samples with a similar matrix, qualified by plasma temperature, electron number density and total number density of the elements of interest. By compensating for deviations of these parameters from the standard state, fluctuations in the characteristic line intensity can be eliminated to some extent. The group compared results with traditional univalent and partial least squares calibration models, showing significant improvements in accuracy and stability of measurements.

3.6 Ceramics and refractories

The literature in this field continues to be dominated by applications related to the analysis of archaeological or historical ceramics. In common with previous years, most of these applications used a direct analysis technique to preserve the integrity of the sample, i.e. cause as little damage as possible. This approach has the added attraction of not having to use hazardous or time-consuming acid dissolution procedures.

3.6.1 Industrial ceramics. The use of chemometric tools to maximise the information obtained from the analytical data or to optimise the analytical methodology has been the subject of a report by Hernandez-Garcia et al.¹⁶⁶ Laser-induced breakdown spectroscopy was used in combination with multivariate calibration to determine Pb, Sr, Ti and Zr in samples of lead–zirconate–titanate ceramics. Spectral data was analysed using partial least squares which, when the results were compared with those obtained using PIXE, provided better results than both multiple linear regression and from external calibration. The Pb data was within 98–102% of reference values whereas the other elements were within 90–110%. Numerous other examples of the use of chemometrics are presented in Table 3, in relation to the analysis of historical ceramics.

Table 3 Analysis of atomic spectrometry to the analysis of cultural heritage ceramics

Analyte	Sample matrix	Technique; atomization; presentation	Comments	Ref.
Various (29)	Amphorae (53) from Catalonia, Spain	XRF; —; s, XRD, optical microscopy	Cluster analysis using centroid agglomerative method and squared Euclidean distance used to identify six distinct groups of samples. Data also analysed using PCA. Imported samples identified as coming mainly from Tunisia, although others from the Eastern Mediterranean were also found	168
Various (>30)	Amphorae from Sidi Zahruni, Tunisia	XRF; —; s, XRD	Amphorae samples (43) analysed. Data treated with hierarchical cluster analysis with average linkage method, square distance and PCA. Sample crushed and powdered and then fused with Li2B4O7. Several CRMs analysed. Results from samples compared with samples analysed previously; indicating they were from the same source	169
Various (26)	Ceramic garden statues	XRF; —; s	Portable XRF used to distinguish between statues from three manufacturers (Coade, Blashfield and Doulton). Cluster analysis, PCA and one-way ANOVA used for data analysis. On-board pre-calibration used on p-XRF instrument. Despite sample heterogeneity, p-XRF was capable of making the distinction. Some samples showed anomalous elemental profiles	170
Various (11)	Ceramic sherds (German)	XRF; —; s, LIF; FTIR; μ-Raman	Ceramic body and glaze of samples analysed using a multi-spectroscopic approach. Mineralogy and microstructure was elucidated. Firing temperature and production techniques investigated. Binary plots used to discriminate between sample types	171
Various (9)	Copper-red porcelain from Changsha kiln from the Tang Dynasty, China	XRF; —; s, XANES, optical microscopy, SEM; μ-XRD	Technique of EDXRF used to quantify analytes. Content of Cu determined whether pigment was red (<0.5% CuO) or green (>1% CuO)	172
Various (25)	Hellenistic style tableware from Uzbekistan	XRF; —; s, XRD and SEM-EDS	Cluster analysis used to treat data. Most samples were identified as being of local origin. Information on preparation of the wares and firing treatment also obtained	173
Various	Medieval ceramics	AES; LIBS; s	Glaze, paint and clay of medieval Islamic and Byzantine ceramics analysed using LIBS. Analytical data analysed using PCA and partial least squares discriminant analysis (PLS-DA). Islamic ceramic glazes contain Sr	174
Various	Ming dynasty wares	EDXRF; —; s, INAA	Elemental composition of body and glaze from imperial and civilian porcelain from early Ming dynasty analysed. Chemometric (PCA) analysis of data demonstrated that the raw materials were similar although the degree of elutriation could be different	175
Various (9)	Piedmontese porcelain (18^th century)	p-XRF; —; s	Analytical data from clays validated through analysis of the CRM SARM 69 (from MINTEK) and the SRM 98b (from NIST). Glaze data validated through analysis of six other CRM. Analytical data treated with PCA and hierarchical cluster analysis	176
Various (21)	Pre-Islamic ceramics from Iran	p-XRF; —; s	Portable XRF used to analyse 26 shards of ceramic dating from between the neolithic and the late bronze age. Scatter plots and principal component analysis (PCA) used on analytical data. Five distinct groups of ceramics identified. Analysis was rapid and non-destructive	177
Various (5)	Porcelain from Mietsu Naval Facility, Japan	XRF; —; s	Synchrotron radiation XRF used to determine Fe, Rb, Sr, Y and Zr in porcelain from the naval facility as well as from a castle and Amakusa or Izumiyama porcelain stones	178
Various	Pre-historic painted pottery from China	XRF; —; s	Three dimensional XRF used to obtain depth profiles of Ca, Fe and Mn. The Ca was homogeneous in both painted and unpainted regions. However, the Mn appeared only in the painted regions. The Fe content differed between the two regions	179
Various (13)	Qinghua décor (Ming dynasty, China)	μ-XRF; —; s, XRF; —; s, μ-XRD, XANES	Synchrotron-based multi-analytical approach for the analysis of the composition and microstructure of underglaze colour. The underglaze contained Co, Fe and Mn. The Fe was distributed evenly throughout the white and blue parts	180
Various	Terra Sigillata (6^th and 7^th century)	XPS; —; s	Non-destructive analysis of samples using XPS enabled the provenance to be elucidated. The surface and the bulk material could be analysed. Data were obtained through the method of atomic ratios employing relative sensitivity factors. Method validation was achieved through the analysis of the SRM NBS 679 brick clay	181
Various (15)	Yuan dynasty porcelain from Chinese city of Jingdezhen	XRF; —; s, optical microscopy	Micro-XRF used to determine trace element concentrations in the body, glaze and coloration of samples. Scatter plots of elemental ratios used to distinguish between sample types. Differences in materials used and the manufacturing technology were identified	182

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The technique of LIBS is regarded as being largely non-destructive and has been used for the analysis of ceramics. Schiavo et al.¹⁶⁷ described a new instrument specifically designed for high resolution 3D-compositional analysis and mapping of materials. The instrument was capable of both single and double pulse modes. The double pulse version mode was used with a 260 ns acquisition delay after the second pulse yielding significantly better sensitivity. A compositional map was constructed using LIBS intensity measurement at the sample surface. The lateral resolution obtained was governed by the crater size, with 10 000 spectra being generated from a 5 × 5 mm area. Depth profiling was achieved by repeated ablation of the same spot. According to the authors, the instrument had several advantages over LA-ICP-MS; e.g. the lack of doubly charged interferences, the possibility of quantitative analysis without the necessity of calibration and the ability to determine analytes of low atomic mass. The LIBS analysis was micro-destructive, with the top 5 μm of the surface being ablated.

3.6.2 Cultural heritage: ceramics. Most of reports in the year under review concentrate on the historical perspective in relation to the artefact rather than the analytical chemistry details. A summary of these investigations is presented in Table 3. However, it is worth noting that many of the studies have used one or more chemometric techniques to attempt to elucidate provenance or to obtain information on the method of preparation of cultural heritage.

3.7 Glasses

Reports concerning the analysis of industrial glasses are considered in Section 3.7.1 of the review. The characterisation of glasses associated with cultural heritage is summarised in Section 3.7.2. A summary of reports pertaining to the analysis of glasses is presented in Table 4.

Table 4 Applications of atomic spectrometry to the analysis of glasses

Element	Matrix	Technique	Sample treatment/comments	Reference
Cu	Borate glass	LIBS and electron paramagnetic resonance spectroscopy (EPRS)	Combined double pulse ns lasers (266 nm and 1064 nm) in a collinear configuration were used to increase LIBS signal response from lithium–lead–borate glass samples containing variable amounts of Cu (as oxide). Quantitative results obtained using the double pulse LIBS system agreed with those from an EPRS method	190
N	Silicate glasses (natural and synthetic)	SIMS	First reported determination of N in melt inclusions. Calibration for N was achieved using implanted glasses. Ion yields and background intensity were significantly affected by water content	191
Various	Ancient glass	SEM and ICP-OES	Iridescent glass samples from the ancient Sasanian historic site (Tomb-e Pargan) in the Bushehr Province of Iran were examined. The cause of iridescence was attributed to the depletion of alkaline elements on the surface of the glass	192
Various	Antibacterial borate glasses	AA;F;l	Glass disc samples (doped with Sr) were exposed to de-ionised water for 1, 7 and 30 days and ion release profiles for Ca^2+, Na^+, and Sr^2+ generated. Results obtained using the method were consistent with those from weight loss analysis	193
Various	Automotive glass (Korean)	MS;ICP;LA	Samples of glass from car side windows (35) and wing mirrors (120) were directly analysed (for major components, REEs and Pb isotopes) using a combination of LA-ICP-MS and linear discriminant analysis. The approach could distinguish between different side mirror glasses for forensic identification purposes but not mirror samples	194
Various	Borosilicate bioactive glasses and glass-ceramic derivatives	XRF, XRD and FT-IR	Multi-technique investigation of composition and structure of glasses and glass-ceramics formed following heat treatment	195
Various	Glass beads	MS;ICP;LA	Direct laser sampling of 15^th to 17^th century glass beads (74) from burial sites in Cardamom Mountains, Cambodia. New high-alumina glass type identified	196
Various	Glass beads	OES;ICP;LA	Analysis of glass beads from Shirenzigou and Xigou sites in Balikun County in the Hami region of eastern Xinjiang (China). Compositions found indicated glasses were of Asian rather than Egyptian or Mesopotamian origin	197
Various	Glass beads	XRF and Raman	Glass beads (9) excavated in the harbour area of Rio de Janeiro, Brazil were analysed by both techniques and the combined results used to classify the samples into European and Asian groups associated with manufacturing process	198
Various	Glass bracelets	PIXE and PIGE	Fragments (78) of glass bracelets found in the Byzantine fortified settlement of Isaccea, Romania (10^th to 13^th centuries AD) were studied. Analysis was used to identify the raw materials and manufacturing techniques used, including glass chromophores and pigments used for external decoration	199
Various	Glass vessels	MS;ICP;LA	Glass vessels (133) originating from 7^th to 12^th century Palestine and selected from excavations in modern day Israel were analysed for major minor and trace elements. Results indicated chronology of changes to glass production in the region	200
Various	Glass vessels	MS;ICP;LA, SEM-EDS and EMPA	Use of multiple techniques for the study of glass bowls and cups (21) excavated from the Palatine and Esquiline hills in Rome estimated to be from the 1^st to 3^rd century AD. Samples were classified as Roman and Late Antique colourless glass containing both antimony and manganese	201
Various	Late antique glass vessels	MS;ICP;l and LA, SEM-EDS and EPMA	Study of 24 glass vessels, found at Herdonia (Foggia, Italy) from the 3^rd to 7^th century AD. The vessels were assigned to compositional groups of Adriatic, Levantine and Egyptian origin	202
Various	Late antique and Early Medieval glass vessels	MS;ICP;LA, SEM-EDS and EPMA	Study of composition of 32 glass objects, found at Faragola (Italy) from the 3^rd to 9^th century AD, resulting in identification of vitrification, fluxing and colouring agents and source of origin	203
Various	Lithium borate glass	XRF	Examination of effect of instrumental conditions and glass composition on results of XRF analysis	204
Various	Medieval and post-Medieval glass fragments	PIXE and PIGE	Archaeological glass from Dubrovnik (Croatia) from 10^th to 18^th century was analysed and found to have composition related to natron glass, plant-ash glass and potash glass groups	205
Various	Plant ash glasses	MS;ICP;LA and EPMA	Use of major, minor and trace element fingerprinting of 8^th to 15^th century glasses originating from a 2000-mile area between Egypt and northern Iran to investigate source of production	206
Various	Roman glass	PIXE and PIGE	Non-destructive analysis in a helium atmosphere applied to the study of a yellow and blue millefiori glass fragment decorated with coloured glass pieces found underwater in a 1^st century BC Roman merchant shipwreck	207
Various	Roman glass	MS;ICP;LA, SEM-EDS and Raman	Multi-technique characterisation of 18 coloured and decorated glass samples form Pompeii preserved at the National Archaeological Museum of Naples. The inorganic chemicals giving rise to the different colorations were identified	208
Various	Stained glass	μ-EDXRF and microparticle induced X-ray emission spectrometry	Characterisation of 130 samples of stained glass ‘Fensterbierscheiben’ rural panels from the Pena National Palace (Sintra, Portugal). Results obtained indicated that majority of the panes were of high-lime low-alkali glass composition indicating they were manufactured using raw materials from the same region	209
Various	Stained glass	PIXE and PIGE	Direct analysis of early Medieval stained window glass obtained from the abbey of Baume-les-Messieurs (Jura, France). Results indicated that the fragments were made from wood-ash glass	210
Various	Stained glass grisailles	SR-XRD, SEM-EDX and LA-ICP-MS	Analysis of brown-black paint layer applied to stained glass samples from Spain (Avila, Burgos, Alcala de Henares, and Segovia) dating from early 16^th to 20^th centuries	211

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3.7.1 Industrial glasses. Most primary papers published in the year under review concern the application of techniques for the direct analysis of solid glass materials. Perhaps the most significant trend is in the increase in the use of laser-based sampling methods.

One of the perennial challenges posed by the direct solids sampling of glasses is in ensuring a valid approach to calibration of response to achieve accurate quantification. In this context, a study of the use of silicon calibration solutions in the LA-ICP-MS analysis of silicate glass samples has been published by Zhu.¹⁸³ A tandem quadrupole ICP-MS instrument fitted with an octopole reaction cell was used. Silicon was incorporated in calibration solutions and used also as an internal standard. The laser ablation system was used to generate sample aerosols that were introduced through the make-up gas port into a dual-pass spray chamber. A microflow nebuliser was used to introduce calibration solutions to the ICP via the same spray chamber assembly. The system was used to quantify the levels of 12 elements in NIST SRM 612 (silicate glass) and results agreed with reference valued. Detection limits were reported in the range 0.02 μg g⁻¹ for Ag to 1.1 μg g⁻¹ for As. A chemometrics-based approach to the accurate determination of Mn in glasses using LIBS has been investigated.¹⁸⁴ A Q-switched Nd : YAG laser operating at 355 nm was employed to sample glasses and the resultant emission observed using an Echelle spectrometer fitted with a CCD detector. Calibration models based on PCR and PLSR were evaluated for the quantification of Mn in glass doped in the range 0.77–11.61%. The reported regression coefficients (R-2) varied from 0.85 to 0.98 for the univariate LIBS method and between 0.98 and 0.99 for multivariate calibration curves for Mn in three different spectral regions. An improved LOD of 0.02 wt% Mn was reported when PCR and PLS was used. While this multivariate calibration approach has improved the performance of LIBS in this application, the figures of merit reported do not suggest that more conventional alternatives, such as XRF, will become outmoded any time soon.

The LIBS technique has also been combined with laser-induced fluorescence measurement for the detection of trace elements (Al, P and Yb) in luminescent glasses.¹⁸⁵ A wavelength-tunable laser was used for excitation of fluorescence from analyte atoms and ions generated in the ablation plasma. It was reported that the spectral intensities for Al and P and Yb were enhanced by 50, 8 and 23 times respectively using laser-induced fluorescence in comparison with conventional LIBS measurement, thereby significantly improving the sensitivity for the analysis of active luminescent glasses. The effect of sample temperature on the observed LIBS emission intensity from a glass has been investigated.¹⁸⁶ A fs laser was used to sample the glass which was heated from 20 to 200 °C. Variations were also made to both laser energy (0.3 to 1.8 mJ) and delay time (0.6 to 3.0 ms) and optimal conditions were established below the maximum values assessed. It was found that emission intensities increased with higher sample temperatures resulting in improved detection limits, presumably due to the higher mass of material ablated. This work suggests that improvements could be made to analytical performance if sample temperature was controlled in other LIBS applications. However, the influence of sample temperature on the LA-ICP-MS analysis of glass has also been studied by Jarosova et al.¹⁸⁷ Two ns Nd : YAG laser wavelengths (213 and 266 nm) were used for sampling standard glass (NIST SRM 610) at room temperature (ca. 21 1C) and at the temperature of dry ice (ca. −77 °C). The ratio of U : Th signals was used to calculate fractionation in the sampling process. It was found that fractionation decreased from 1.29 to 1.03 if the sample was kept at the low temperature during the entire time of analysis. Considering the outcomes of these LA-ICP-MS and LIBS studies together, although the measurement techniques and laser sampling conditions are not the same, it is possible that increasing the sample temperature to maximise LIBS signal intensity¹⁸⁶ might not be optimal in terms of achieving analytical accuracy due to bias arising from fractionation effects.

One of the advantages of LA-ICP-MS over LIBS lies in the ability to provide information on isotopic content of glasses. To this end, a multiple Faraday collector ICP-MS instrument equipped with 10 (13) Omega resistor high-gain amplifiers and an ultraviolet fs laser ablation sampling system was applied to the analysis of Pb isotopes in glasses.¹⁸⁸ A strong linear correlation was observed between the rates of signal intensity change and the measured isotope ratios for the same time intervals. However, it was found that individual amplifiers responded differently requiring a correction to be made to the time-resolved data. The NIST SRM 612 (synthetic glass) was used an external standard for the analysis. The resultant method was applied to the determination of Pb isotope rations in ²⁰⁸Pb : ²⁰⁶Pb and ²⁰⁷Pb : ²⁰⁶Pb isotope ratios in and BHVO-2G (1.7 ppm Pb) BCR-2G (11 ppm Pb) basalt glass samples. A laboratory bias of ±0.05–0.15 parts per thousand RD and intermediate precisions of ±3–7 parts per thousand for BHVO-2G and 0.6–3.7 parts per thousand BCR-2G were reported which 2–3 times better those obtained using alternative ICP-MS instrument configurations.

The determination of low levels of C is problematic for most atomic spectrometry techniques. However there have been two novel reports of the application of SIMS to the analysis of carbon in glass. Thus, Bronsky and Shilobreeva¹⁸⁹ have described a method for the quantitative estimation of C in silicate glass. The method was based on estimating the carbon ionization coefficient using its dependence on the structure of the sample matrix. The ratio of the number of nonbridging oxygen atoms to the number of tetrahedrally coordinated silicon and aluminium ions allowed calibration graphs to be constructed to allow determination of the ionisation coefficient for C, allowing an subsequent estimation of the C content of the sample, under prevailing SIMS instrument experimental conditions.

3.7.2 Cultural heritage: glasses. The bulk of applications of atomic spectrometry to the analysis of glasses in the year under review concerned the study of cultural heritage glassware. The use of non-destructive (e.g. X-ray microbeam) or minimally destructive (e.g. laser sampling) techniques is attracting much attention, as is the employment of portable instrumentation for in situ analysis. However, the focus of many of these papers is often explicitly on matters of historical interest, such as provenance, using the analytical method as a probe to provide insight. A summary of such applications appearing in the current review period are presented in Table 4.

3.8 Nuclear materials

Key developments in the analysis of nuclear materials in the year under review concerned the assessment of the performance of materials used in fusion reactor design, and quantification of impurities that can impact material performance. Accurate isotope ratio measurements for forensic applications remains a popular area, whilst nuclear chemistry developments included the use of novel separation materials and online separation techniques prior to measurement. The literature continues to be dominated by applications involving LIBS and ICP-MS, although there were several studies that utilised a combination of techniques for complete material characterisation. Finally, the use of rapid measurement techniques with minimal or no sample preparation is still growing. The main benefits cited included reducing the laboratory footprint and worker exposure, and increasing sample throughput and ease of measurement.

3.8.1 Nuclear fusion applications. Characterisation of the behaviour of materials in fusion reactors remains a topic of considerable interest. Assessments were made of materials designed for routine operation, and the behaviour of impurities in materials that affect reactor performance. Some reports concerned the use of multiple techniques for fully assessing materials, whilst in others in situ techniques for rapid measurement were described.

Laser-induced breakdown spectroscopy (LIBS) was one of the most commonly used techniques for the analysis of fusion materials. For example, LIBS was used as a diagnostic tool for the study of plasma-facing components at the ENEA research centre, Italy, and IPPLM, Poland.²¹² The technique was used to characterise samples eroded, re-deposited or contaminated after or during reactor operations. The technique was also used in depth profiling and spatial analysis of dust impurities on contaminated curved surfaces of a graphite limiter.²¹³ Seven elements were detected (C, Cr, Cu, Fe, Mn, Mo, Ni), with concentrations determined from a calibration-free LIBS method. A similar approach was applied to assess C and W content in W-based samples with a protective carbon layer.²¹⁴ In this study, a vacuum-UV LIBS technique analysed samples under a range of pressures (atmospheric and 5–10⁵ Pa), with the spectra recorded by three spectrometers with different delay times and atmospheric pressures. From the measured spectra, the best precision was obtained with the W^III spectral lines.

A modified laser-induced breakdown spectroscopy system was used in a proof of concept investigation of impurity transport in plasma fusion devices and plasma-wall interaction measurement.²¹⁵ Following a laser pulse on the reactor wall, a surface layer of deposited impurities was evaporated and collected into two mass spectrometers: one to assess impurities, and one to quantify hydrogen isotopic composition surface. The potential of laser-induced desorption spectroscopy (LIDS) for retention of hydrogen isotopes in the vacuum vessel wall was presented in a separate paper.²¹⁶ The in-vessel gas produced was measured by optical emission spectroscopy. Several types of plasma sources that could be used for remote, in situ measurements were assessed. Accurate measurement of H isotopes is important, as ³H retention in the vacuum vessel can adversely affect the performance of fusion reactors. A final example of LIBS application was in a comparative study of single and double pulse systems for detection of U in special nuclear material.²¹⁷ The study highlighted the crowded emission spectra and challenges in separating emission lines for some nuclear material when using single pulse LIBS. By comparison, double pulse LIBS was found to improve the signal intensity with minimal increase in sample damage compared to single pulse LIBS.

The properties of divertor tiles during reactor operations have been the subject of several investigations. Bergsaker et al.²¹⁸ used a multi-technique approach to the assessment of deposition of Be and deuterium retention in tile surfaces exposed in Joint European Torus (JET) operations from 2010–2012. A combination of SEM, SIMS, ICP-AES and micro-beam nuclear reactor analysis (μ-NRA) was employed. The analysis demonstrated that deposition and retention was microscopically heterogeneous. The overlaying of μ-NRA and SEM allowed separation of surface roughness effects from depth profiles at flat surface regions, without pits. The JET Tokamak was also the focus of a study into Be migration in limiter tiles during plasma operation.²¹⁹ The sample was a ¹⁰Be-enriched limiter tile installed prior to operations in 2011–2012. Quantification of the ICP-AES total Be in cm²-sized samples was achieved using ICP-OES, and the ¹⁰Be : ⁹Be ratio was assessed by AMS. The study was the first to measure marker concentrations in divertor deposits, with values ranging from 0.4–1.2% of source concentration. Divertor tile deposits from JET were also measured in situ for the first time using LIBS.²²⁰ The spectral lines of elements and impurities were assessed following repeated laser shots at the same divertor location, with increasing shots showing changes in spectral intensities of deuterium, carbon, and impurities from tungsten substrate. The results demonstrated the possibility of direct and remote analysis of reactor components.

3.8.2 Material impurities. Identification and quantification of impurities is an important area with regards to the impact on material quality and performance. In one study,²²¹ transition metal oxides (V and Co) were proven to have a significant impact on the physical properties and dissolution behaviour of a simulant nuclear waste glass (International Simple Glass, ISG). The ISG doped with V₂O₅ and Co₂O₃ was left for 112 days, with ICP-MS measurement of the dissolved ion concentration showing that the extent of glass dissolution was reduced after doping. Phase and microstructure changes were assessed by XRD, Raman spectrometry and SEM, whilst DSC proved that only vanadium oxide reduced the glass transition temperature. Somayajulu et al.²²² characterised (Pu, Th)O₂ pellets for chemical quality control by assessing trace metal and non-metal content. Multiple spectrometric methods were applied to trace metal content, with non-metal impurities determined using chromatographic, spectrophotometric, conductometric and manometric methods, with concentrations reported at the <1 ppm up to >100 ppm concentration range.

Methods for certifying the isotopic composition of nuclear grade gadolinium oxide and rare earth element impurities have been published.²²³ In compact reactors, Gd₂O₃ is added to nuclear fuels as a neutron poison. However, impurities can lead to additional neutron poisons or formation of undesired radionuclides. Conventional analysis and total evaporation were combined with TIMS for isotopic analysis and produced comparable results, although the measurement uncertainties were lower using conventional analysis. The REE impurity concentration was assessed by ICP-MS, with concentrations found in agreement with nuclear specifications. Saha et al.²²⁴ investigated trace metal impurities in binary and/or ternary metallic alloys of U and Pu with transition metals (Mo, Ti and Zr), which are promising fuels for fast breeder reactors. A range of trace impurities were assessed by ICP-MS following solvent extraction using TBP to remove matrix constituents, with any remaining effects accounted for using the common analyte internal standard technique. The analyte recovery was >92%, with RSD’s of 5–8% in real U alloy samples, and a method detection limit of 3–15 ng mL⁻¹. The method was validated through comparison with IDMS and gamma spectrometry recoveries.

Zhang et al.²²⁵ demonstrated the advantages of a modified inductively coupled plasma sample introduction system for impurity measurements using ICP-MS. Impurities in uranium using CRMs and real samples were assessed using ICP-MS coupled with parallel two-layer laminar flow microfluidics (PTL-ICP-MS). A reduced amount of sample required, and higher sample throughput was achieved. Compared to offline separation, PTL microfluidics reduced the amount of radioactive sample, waste and risk of exposure. Montoya et al.²²⁶ combined ICP-AES with a novel FAST sample introduction system to determine trace level impurities in plutonium metal. The modifications reduced the sample volume required, and improved measurement sensitivity and precision compared to an unmodified system. An offline plutonium oxalate precipitation technique has been applied to separate trace impurities from plutonium materials.²²⁷ Following centrifugation, measurement was performed using ICP-OES. The separation technique was successfully applied to the minimisation of spectral interferences from the sample matrix. Offline separation was also combined with ICP-AES measurement for determining trace level constituents in a Th matrix.²²⁸ A comparison of the separation capability of three ligands (TBP, TOPO and DHOA) was made. It was reported that TOPO showed the highest uptake of Th, which was successfully stripped using oxalic acid. Using the method, multiple analytes were determined at concentrations of 0.1, 0.5 and 1 mg L⁻¹ using DHOA, TBP and TOPO, respectively. Adya et al.²²⁹ developed a method for determining trace metal constituents and Np simultaneously in a high-purity Np matrix. An interference-free Np line was identified for ICP-AES measurement, and the correction factor and tolerance level for Np were evaluated using synthetic samples. In a separate study,²³⁰ the same lead author developed a method for direct measurement of P and S in U, Th and Zr matrices, with promising results using synthetic samples for direct, trace level determination.

Inductively coupled plasma mass spectrometry has been used in the measurement of radionuclides in reactor-grade graphite. Carter et al.²³¹ presented a novel method for measuring diffusion coefficients of fission products and quantification of Cs released from new grades of graphite used in high temperature gas-cooled reactors Caesium metal was loaded onto graphite spheres, and the Cs released was transported into an ICP-MS for direct measurement. Neutron activation analysis was used to determine Cs calibration factors for quantification. Plukiene et al.²³² also applied ICP-MS to measurement of irradiated graphite, focusing on Pu, Am and Cm in a RBMK-1500 reactor. It was reported that there was good agreement for isotope ratio measurements obtained using ICP-MS, alpha spectrometry and simulated ratios. The results were important with regards to dose from irradiated graphite, and for accurate radionuclide quantification with regards to decommissioning of graphite-moderated reactors.

3.8.3 Nuclear forensics and isotope ratio applications. Nuclear forensics remains a popular topic for identifying the source of contamination and age-modelling applications. Developments of note include expanding the range of measurable radionuclides that are applicable to this field, and improved accuracy and precision of more frequently measured isotopic ratios such as U and Pu. The range of techniques and instrumentation for rapid measurement of nuclear materials was the subject of a review.²³³ Attention was focused on the requirements for screening of materials with regards to nuclear forensics and radiological emergency response. The review also included an assessment of the role of mobile and fixed laboratories and equipment, and improvements in measurement capabilities that has expanded the range of radionuclides measurable for forensic applications. Nagy et al.²³⁴ detected ¹³⁵Cs (classified as a ‘difficult-to-measure’ radionuclide by the nuclear industry) by ICP-MS following a 2-stage chemical separation. The ¹³⁵Cs : ¹³⁷Cs ratio was used to calculate the thermal neutron flux of coolant samples of defective fuel elements, and combined with decay-counting measurement of ¹³⁴Cs : ¹³⁷Cs to calculate sample burn-up. The application of handheld XRF for forensic measurements were explored in a separate study.²³⁵ The impact of an external radiation field using ⁶⁰Co and ¹⁹²Ir for high and low-energy gamma emissions, respectively, was assessed. The results obtained demonstrated the impact of increasing dose rates on dead-time and instrument background.

The age modelling of radionuclides was addressed in several papers. The results from an inter-comparison exercise for ²³⁰Th–²³⁴U age modelling using a uranium CRM (NBL U050) were reviewed.²³⁶ Results were obtained by IDMS, with the ages reported in agreement or slightly higher than the known production age. A study by Kayzar and Williams²³⁷ also focused on nuclides in the uranium decay chain, concentrating on ²²⁶Ra and ²²⁷Ac parent/daughter isotope ratios. Model ages were calculated for a uranium CRM (CRM-U100) and two enriched U metal samples from a round robin exercise. Accurate measurement of ²²⁶Ra : ²³⁸U and ²²⁷Ac : ²³⁵U ratios was achieved providing information on migration during uranium processing. Age-dating was also applied to Pu materials (SRM 946 and 947) using ICP-MS.²³⁸ Good agreement was obtained between measured values and the archive purification dates. One method offered rapid and direct measurement of ²³⁵U : ²³⁹Pu and ²³⁶U : ²⁴⁰Pu, ratios while the use of a single stage separation preceding measurement allowed detection of additional U : Pu isotopic ratios.

A novel TOF-ICP-MS system was developed for detection of fission products commonly found in nuclear debris following detonation events.²³⁹ A chemically induced, ligand enhanced volatilisation of REE’s was used prior to separation by GC and detection by TOF-ICP-MS. The method achieved good reproducibility, suggesting that it could be applicable to rapid post-detonation forensic applications. Wang et al.²⁴⁰ also applied solid samples for isotopic analysis of Pb in U particles using LA-ICP-MS. External standardisation was used to correct for mass fractionation resulting from laser sampling. A reference material (CRM 124-4) was used to validate the experimental approach. The relative uncertainties for Pb isotopes were reported in the range 0.40–0.68%. The procedure was also tested on uranium particles, demonstrating that the origin of U particles could be determined from the Pb isotopic signature.

The measurement of uranium isotope ratios continues to be explored. Tarolli et al.²⁴¹ used a combination of high precision SIMS imaging and EDS X-ray elemental maps for isotopically unique U-bearing particles. Image registration, fusion and particle micromanipulation were performed for the first time in this field on samples containing a mixture of CRMs and one SRM to simulate swipe particles used for routine inspection of uranium enrichment facilities by the IAEA. The measured isotopic values obtained were within several percent of NIST values for the range of CRMs tested. Uranium isotopic ratios were also measured in U₃O₈ particles using SIMS by coupling an instrument to a single stage AMS (SSAMS).²⁴² The setup was tested on pg to ng quantities of three uranium CRMs, with positive ions from SIMS injected into the SSAMS. The low UH⁺ : U⁺ ratio of ∼1.4 × 10⁻⁸ obtained was advantageous for ²³⁹Pu measurement. The almost complete removal of molecular interferences makes this approach attractive for isotopic measurements of solid nuclear materials. A combination of TOF-SIMS and SF-SIMS was used for measurement of synthetic samples containing UO₂ and Gd, Mo or Zr oxide by Park et al.²⁴³ The techniques complemented each other, with TOF-SIMS used for qualitative elemental analysis, and removal of potential U isobars achieved using SF-SIMS by measuring UO₂ rather than UO.

Uranium isotopic abundance (²³⁵U : ²³⁸U) was also determined using RIMS.²⁴⁴ The focus of the study was on improved understanding of laser-induced bias to improve measurement accuracy and precision, and highlighting considerations for correctly interpreting experimental data. This is of interest given that bias correction largely focuses on the use of isotopic standards, without consideration of first-principles of laser-induced bias. Multi-collector ICP-MS detection of U isotopic ratios was demonstrated by Boulyga et al.²⁴⁵ Multiple U isotopes were measured, with detection limits of 1 × 10⁻⁷ (²³³U : ²³⁸U) and 6 × 10⁻⁸ (²³⁶U : ²³⁸U) for 0.4–0.6 g U, with relative expanded uncertainties of <0.2% (k = 2). The instrument modifications used to improve sensitivity were described, as were the methods for overcoming multiple interferences (including screening of samples for potential polyatomic overlaps). Interference removal was assessed in detail by Pollington et al.,²⁴⁶ focusing on the impact of polyatomics on accurate measurement of minor U isotopes ²³⁴U and ²³⁶U. The impact of polyatomic interferences as well as methods to mitigate the effects was evaluated. Finally, the performance of total evaporation (TE), modified total evaporation (MTE) and conventional-use was compared for TIMS measurement of major and minor U isotope ratios in CRMs.²⁴⁷ Precise, accurate measurement was achieved for major isotopes using TE and MTE, and for minor isotopes by MTE and conventional use.

The measurement of plutonium isotope ratios for forensic applications has been the subject of further investigation. A combination of alpha spectrometry and ICP-MS was applied to analysis of mixed oxide particles contained in environmental samples, following dissolution and chemical separation.²⁴⁸ By combining the techniques, the limitations associated with individual measurement methods could be overcome, for example using alpha spectrometry for ²³⁸Pu to overcome the ²³⁸U overlap of ²³⁸Pu for mass spectrometric techniques. The use of RIMS for rapid, interference-free technique for Pu isotope ratio measurements without the need for chemical separation has been demonstrated.²⁴⁹ Plutonium solutions deposited on metal surfaces were repeatedly measured using RIMS over two months, achieving absolute accuracies for the measurement of ²⁴⁰Pu : ²³⁹Pu in two solutions of 0.70% and 0.58%, and quantification of ²³⁸Pu in the presence of high ²³⁸U concentrations. Finally, Hanson et al.²⁵⁰ described a method for determining the yield of the Trinity weapons test by measuring the amount of Pu, and non-natural ⁹⁵Mo : ⁹⁶Mo and ⁹⁷Mo : ⁹⁶Mo ratios in glassy debris samples. The non-natural ratios were ascribed to decay of short-lived ⁹⁵Zr and ⁹⁷Zr, and together with the total Mo, new efficiency and yield estimates of the Trinity test were calculated.

3.8.4 Nuclear chemistry. The importance of chemical separation prior to measurement remains a key topic in this application area. Pre-separation can improve the accuracy of measured values, lower the detection limits achievable, and overcome the limitations of measurement techniques. Advances in this field have been achieved using novel separation materials, high volumes or masses of materials, and coupling the separation online with the measurement technique to improve the separation efficiency.

The importance of accurate quantification of long-lived, difficult-to-measure radionuclides relevant to decommissioning and longer-term waste monitoring has been the subject of investigation. A separation procedure for Palladium (¹⁰⁷Pd) using extraction chromatography resin (Ni-resin) achieved >70% recovery when measured by AAS, although the concentrations present in real samples fell below the minimum detectable activity.²⁵¹ The combination of laser-induced phytoreduction precipitation separation with ICP-MS measurement for the determination of ¹⁰⁷Pd in a spent fuel sample has been described.²⁵² A rapid one-step recovery was achieved by phytoreduction induced by pulsed laser irradiation at 355 nm. Using natural Pd as a carrier, chemical yields of ∼90% were achieved, with virtually no impurities. The increasing number of nuclear sites approaching decommissioning over the coming years necessitates the need to develop of robust, reproducible procedures. As well as direct measurement, the need for more accurate underlying metrology data for accurately quantifying difficult-to-measure radionuclides was highlighted in one study of MC-ICP-MS measurement of ¹²⁹I, a long-lived radionuclide present in spent nuclear fuel.²⁵³ The uncertainty and reproducibility of ¹²⁹I concentrations was assessed by isotope dilution using a stable ¹²⁷I spike, achieving an expanded relative uncertainty of <0.7% (k = 1). In combination with decay counting techniques, the uncertainty in the final half-life value was reduced to 0.76% (k = 1).

The lack of reference solutions and materials and the resulting impact on method development and final measurement remains a concern. For example, in one study the absence of certified ¹²⁷I : ¹²⁹I standards was identified as an issue for mass bias correction, with Te chosen as an alternative.²⁵³ A review of the mass spectrometric analysis of Th emphasised the importance of developing certified isotopic ²³⁰Th : ²³²Th reference materials for applications including the increased use of Th in future energy production.²⁵⁴ A separate study²⁵⁵ focused on the need for Pu and U isotopic standards with lower uncertainties for validating procedures related to fields including environmental monitoring, radiation protection, fuel fabrication and nuclear forensics. It was found that the high uncertainties of current U and Pu isotopic standards meant that the improved accuracy and precision of MC-ICP-MS over the last few decades could not be fully utilised.

The implications of high isotopic uncertainties for tracers was highlighted by Penkin et al.,²⁵⁶ focusing on ²⁴⁴Pu, commonly used as a spike for mass spectrometric Pu measurement. To address the isotopic purity of available ²⁴⁴Pu, a PuO₂ sample initially containing ∼17.5% ²⁴⁴Pu was run through a 2-stage electromagnetic separation procedure, improving the purity to 98.9% and >99.98% with each separation stage. The future aim is to certify the material as a spike for isotope dilution mass spectrometry, which will address the needs with regards to international nuclear safeguards. A second study also focused on purification of ²⁴⁴Pu using a benchtop approach, achieving a purity of >99.996% for a small quantity, compared to 97.87% for the standard available.²⁵⁷ The reduction of other Pu isotopes allowed for higher spike levels, which is significant for environmental analysis.

Improved separation of actinides was demonstrated in several separate studies. By coupling calixarene-based chromatography online with ICP-MS, an improvement in sensitivity was achieved for ²³⁸U, ²³⁹Pu and ²⁴¹Am compared to ‘dilute and shoot’ analysis.²⁵⁸ The optimised procedure achieved detection limits from 16 pg to 0.6 ng, and was validated using CRMs containing each radionuclide. Locklair et al.²⁵⁹ focused on pre-concentration of Pu isotopes through development of a novel anion-exchange-based thin film (around 150 nm) coated onto silicon substrates. Rapid Pu uptake in batch trials were assessed by ICP-MS, LSC and alpha spectrometry, with an equilibrium constant of 9060 L kg⁻¹. The films were deemed to be particularly suitable as a substrate for TIMS and alpha spectrometry. In another study, a micro-volume anion exchange porous polymer disk-packed cartridge was developed for Am/Np separation prior to ²³⁷Np quantification by ICP-MS.²⁶⁰ Of the amine-based groups investigated, triethylenediamine (TEDA) performed best and was applied to a spent nuclear fuel sample, with ²³⁹Np and ²⁴³Am as yield tracers. The Np yield was 90.4%, with effective separation from both Am and ²³⁸U. Compared to conventional anion exchange chromatography separation, the disk-packed cartridge method reduced the separation time by 75%. Campbell et al.²⁶¹ evaluated LA-ICP-MS for direct measurement of ²³⁹Pu and ²³⁷Np in U oxides without an internal standard. The total procedural time of <1 hour for each sample set was considered rapid compared to existing techniques for nuclear safeguard applications. Detection limits of 0.026 and 0.111 wt, were reported for ²³⁹Pu and ²³⁷Np respectively. However, the issue of sample heterogeneity must be considered for laser ablation compared to solution-based sample introduction.

Some studies focused on separation and pre-concentration of trace level elements. Krajko et al.²⁶² developed a method using pre-concentration, group separation and ICP-MS measurement to analyse REE’s in a high purity U matrix. Chemical recoveries obtained were >90%, with LODs in the low pg g⁻¹ range. The methodology offered the potential to track REE behaviour through the nuclear fuel cycle. In another study, the different evaporation rates of Sm and Nd was used as a rapid and simple approach to determine burn-up of spent nuclear fuel using TIMS.²⁶³ The higher evaporation rate of Sm meant the isotopic composition of the fission product monitor Nd could be assessed without isobaric overlap from Sm. The effectiveness of the technique was determined by comparing the burn-up value for a pressurised heavy water reactor fuel dissolver solution (0.84 at%) with values using a conventional method (0.82 at%).

3.9 Electronic materials and devices

The electronics industry continues to develop novel materials and devices in which analysis plays an enabling role in assessing functional performance. However, most reports published in the year under review were focused on the outcomes of such studies, rather than in development of analytical technique. A summary of applications appearing in the year under review is presented in Table 5. Key advances of relevance to the atomic spectrometry literature are described in the following text sections.

Table 5 Applications of atomic spectrometry to the analysis of electronic materials

Element	Matrix	Technique	Sample treatment/comments	Reference
Al	Ti–N barrier layer in silicon solar cell	SEM, SIMS, XPS and XRD	Multi-technique approach to study of Al diffusion in TiN in films. Quantitative analysis using SIMS	277
As	Si wafer (back side)	TXRF and SIMS	Investigation of As contamination by migration to the reverse side of a silicon wafer during GaAs layer deposition. The As was quantified at levels in the range 1.5 × 10^16 to 1 × 10^20 atoms cm^−3 using SIMS and 1.0 × 10^10 to 10^15 atoms cm^−2 by TXRF	278
Au	Electronic hybrid organic/inorganic materials	SIMS	Use of Cs^+ 500 eV low energy ion beam for depth profiling of metal (Au and Cr)/organic (tyrosine) model multilayer materials with comparable erosion rates. Method offers potential for application to organic LEDs and organic photovoltaic hybrid devices	279
B	Si1−xGex material	SIMS	Sample depth profiling using a normal incidence O2^+ ion probe with energies of 0.2–1.0 keV (optimum at 0.3 keV). Quantification of B achieved for each profile by determining the individual sensitivity factor	280
B	Metal oxide semiconductor transistor capping bilayer	SIMS	Investigation of B diffusion from Si silicon junction to capping silicon oxide/silicon nitride bilayer material	281
B	Hydrogenated amorphous silicon films	SIMS	Method for the detection of shallow dopants (B and P) 10–15 nm from the surface of a-Si : H thin films	282
Cd	Doped zinc and indium oxide nanocomposite materials	AA;ETA;slurry	Dopants (Cd and Se) were determined using a powder suspension sample pre-treatment prior to direct analysis by CSAAS. Results were validated using a conventional decomposition procedure followed by ICP-MS detection	283
Cl	Organic–inorganic perovskite solar cells	XRF	Use of SR nanobeam XRF for determination of Cl in CH3NH3PbI3 films synthesised with and without Cl containing precursors. Spatial inhomogeneity of Cl was demonstrated using the technique and the standard deviation of the Cl : I ratio across the film was reported as up to 30% of the mean value	284
Cr	Electronic hybrid organic/inorganic materials	SIMS	See Au^279	279
F	AlGaN/GaN metal-insulator-semiconductor heterostructures	SIMS	Method for the quantification of dopant in field effect transistor structures at F at a level of 5.5 × 10^19 atoms cm^−3	285
H	Hydrogenated graphene film on silicon carbide substrate	SIMS	Method for the reproducible measurement of a depth profile in a graphene layer (0.9 nm) thick. Depth resolution of 0.2 nm per decade was achieved	286
Li	Li battery anodes	OES;GD;s	Use of depth profiling to study Li deposition and surface thin film formation and homogeneity in aged electrodes from commercially available cells	287
N	GaNAs semiconductor alloys layers	HR-XRD, SIMS and photoluminescence	Comparison of techniques for the accurate determination of N. The N content measured was found to be affected by the concentration of interstitial N present	288
P	Hydrogenated amorphous silicon films	SIMS	See B^282	282
Se	Zinc and indium oxide nanocomposite materials	MS;ICP;l and AA;ETA;slurry	See Cd^283	283
Various	Cu2ZnSnS4 (CZTS) absorber layers	XRF, GD-OES XRD and Raman	Multi-technique characterisation of thin films in terms of composition and spatial inhomogeneities	289
Various (4)	Electrical and electronic equipment	OES;ICP;l	Precious metal analytes (Ag, Au, Pd, Pt) were separated from the matrix by co-precipitation with Ni and Te substrates and ion exchange with strongly basic anion exchange resins. For Pd, precipitation with dimethylglyoxime was the only viable method	290
Various	GaAs/AlAs superlattice multilayer structures	SIMS and AES	Auger electron spectroscopy and SIMS used to generate depth profiles that were then fitted using a mixing-roughness-information model to quantitatively determine material structure	291
Various	Li ion battery anodes	SIMS and XPS	Surface analysis study of effect of SiO2 anodes on composition of solid phase electrolyte solutions	292
Various (4)	Lithium ion battery layered electrodes	TXRF	Use of TXRF for determination of transition metal degradation products dissolved from layered Li/Ni/Co/Mn/O2 cathodic materials over 100 operational cycles	293
Various	Photovoltaic panels	MS;ICP;l	Samples were dismantled, shredded and pulverised prior to microwave-assisted digestion or toxicity characteristic leaching procedure (TCLP) sample preparation methods for the analysis of waste silicon and CIGS solar panels	294
Various	Silicon oxide/silicon nitride thin films	SIMS, RBS, AES and EDXRF	Multi-technique approach to the characterisation of chemical composition and stoichiometry of thin films grown on multi-crystalline silicon	295
Various (alkali metals)	Thin film solar cells and absorber materials	SIMS, XPS and photoluminescence	Multi-technique study of K and Na surface and bulk content in Cu(In,Ga)Se-2 (CIGS) materials	296
Various	Thin film solar cells	SIMS, XPS, ICP-AES and EPMA	Multi-technique study of composition (matrix and impurity trace elements) of prepared Cu(In,Ga)Se-2 thin film. The average contents found using depth profiling XPS and SIMS were compared with those obtained using quantitative ICP-OES and EPMA methods	297

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3.9.1 Semiconductor materials and thin films. While secondary ion mass spectrometry is widely used in the characterisation of semiconductor materials, reliable quantification still poses a significant challenge in many applications. Consequently, many of the papers published in the year under review focused on improving analytical accuracy of the technique in application to semiconductors. In this context, Drozdov et al.²⁶⁴ have described a new approach to the SIMS analysis of Ge_xSi_1−x heterostructures that could be of importance. A non-linear calibration curve was obtained for the ratio of cluster and elementary secondary ions of Ge without secondary ions of Si, for the first time. It was reported that the method developed allowed the quantification of Ge in heterostructures of this type and it was used for quantitative lateral mapping. An algorithm was also developed and used for the investigation of lateral heterogeneity of Ge_xSi_1−x materials. In another study, Drozdov and co-workers²⁶⁵ employed TOF-SIMS and SNMS for the quantitative depth profiling of Si_1−xGe_x structures. A linear correlation of the intensity ratios of secondary ions and post-ionised sputtered neutrals with Ge concentration was obtained. The calibration data were used for quantitative depth profiling of thin film structures on Si and good agreement was achieved with values obtained using high resolution XRD.

Hashiguchi et al.²⁶⁶ investigated the effect of ion polarity in secondary ion mass spectrometry for the quantitative determination of N and O in GaN thin films. Primary ion bombardment using Cs⁺ was used to generate positive and negative secondary ions for quantification of O at levels in samples in the range 3.2 × 10¹⁹ to 7.0 × 10²¹ atoms cm³. It was reported that the secondary ion intensity measured for the negative ions was approximately two orders of magnitude greater than for positive ions. However, the O : N intensity ratio obtained using positive ions (CsM⁺) was similar to the calculated atomic density ratio suggesting that this mode was preferred over negative ions for the characterisation of oxygen-implanted GaN materials. Depth profiles for O were generated using a Gaussian-type beam and abroad spot beam and similar depth profile resolution was achieved in each case.

The quantitative use of SIMS has also been described in application to the depth profiling of a multi-phased semiconductor material by Gong and Marjo.²⁶⁷ A TOF-SIMS instrument using a Cs⁺ sputter beam and Bi cluster ion source for analysis was used in the characterisation of an AlGaAs/GaAs multilayer stack. It was found that under these experimental conditions, secondary ion yield was significantly enhanced particularly for cluster ions. Sputtered neutrals, representative of all matrix elements, were included in the cluster ions. The intensities of secondary cluster ions of the type Al_iGa_jAs_k (where i, j, and k are integers) were measured allowing the integration of atomic counts for each major element constituent. The atomic counts for major elements were summed and normalised, allowing quantification. This development is particularly important because it was achieved without the use of relative sensitivity factors used in conventional SIMS analysis and offers a potential route to minimising matrix effects. The new method was reported to achieve accurate depth profiling of a AlGaAs/GaAs (CRM BAM-L2000) sample. A new ultra-low energy SIMS method for the measurement of interface profiles in Si_1−xGe_x/Si materials has been reported by Morris et al.²⁶⁸ An incident O₂⁺ beam was operated in the energy range 0.25–2.5 keV and energy sequencing utilised to probe the material interface. Simultaneous fitting of the SIMS profiles measured at different energies was employed to generate an intrinsic sample profile with reported precision at sub nm levels. The results obtained using the SIMS method agreed with those from direct imaging of the interface using high angle annual dark field scanning TEM.

It is evident from the literature cited elsewhere in this review that there is a general increase in the popularity of laser-induced breakdown spectrometry for the direct analysis of solids. However, it has also been recognised that the technique may be subject to bias arising from the effects of laser sample heating, in certain applications. Thus Liu et al.²⁶⁹ have investigated this problem in application to the analysis of GeSi semiconductor material. It was found that emission intensities were dependent on sample temperature. This was attributed to increased laser energy coupling to the surface of the sample facilitated by a reduction in reflectivity as the sample is heated and a resultant increase in laser ablation mass. It was reported that Ge intensities at 422.6 and 589.33 nm were enhanced by factors of 1.5- and 3.0-fold respectively when sample temperature was increased from 50 to 300 °C. While it was noted that similar emission intensity could be achieved using a more powerful laser, it was concluded that sample preheating could improve LIBS intensity using less energetic laser pulse conditions. However, given other investigation of sample heating in LIBS in the year under review. It is important to recognise that sample fractionation is also an important consideration affecting accuracy in the laser sampling of complex materials.

3.9.2 Solar cells and photovoltaic materials. There has been less activity in the characterisation of solar cells and related photovoltaic materials in the year under review. Specifically, the dramatic increase in recent years in reports concerning use of atomic spectrometric techniques for the analysis of Cu(In,Ga)Se₂ (or CIGS) solar cells and materials appears to have abated significantly. This may because a variety of methods are now available from the literature. Nevertheless, Choi et al.²⁷⁰ have proposed a rapid method of depth profiling of CIGS solar cell films using LIBS. The effect of laser wavelength on ablation characteristics was assessed using ns Nd : YAG lasers operating at 266 and 532 nm. It was found that although the sampling depth per pulse was smaller using the UV laser, the visible laser produced smoother more uniform craters. It was concluded, not entirely surprisingly, that depth resolution in this application depended on the mechanisms of ablation as much as the wavelength of laser used.

Polycrystalline CdTe photovoltaic thin films used in solar cells are commonly annealed in atmospheres containing chlorine and oxygen and the presence of these elements at gain boundaries can affect the performance of such devices. A study of the use of TOF-SIMS to the analysis of impurities in cadmium telluride thin films has been published by Mao et al.²⁷¹ The two-dimensional distribution of Cl, Cu, O and S was imaged with a lateral resolution of ca. 150 nm. The degree of chemical segregation of these impurities at the material grin boundaries was assessed by subjecting the TOF-SIM data to a two-sample Kolmogorov–Smirnov statistical test. The results indicated that substantially greater segregation was detected for Cl and O using this method.

The use of GD techniques in the analysis of photovoltaics devices and materials is now well established. Fernandez et al.²⁷² have reported the application of GD-TOF-MS to depth profiling of silicon thin film tandem solar cells. An rf pulsed GD source, employing ms and sub-ms regimes, was used to sample tandem junction solar cells on a 2 mm thick glass substrate with a 1 μm thick ZnO : Al coating. The instrument was operated under ‘low mass mode’ conditions. The distribution of B, Ge and P dopant elements was investigated using two sample preparation treatments involving flat or rough cell substrates. It was reported that the use of a rough substrate resulted in poorer depth resolution for impurities in the silicon, implying diffusion in the sample coating.

3.9.3 Lithium ion batteries. Efforts continue to be devoted to finding new ways to shed light on the properties of lithium ion batteries used as power sources for a wide range of portable electronic devices and increasingly electric cars. Not surprisingly therefore, many of the reports appearing have been focused on battery ageing or in understanding the mechanisms involved in electrode degradation.

Much of the literature in recent years has concerned the assessment of one or more components of a lithium ion battery under experimental conditions designed to assess operational mechanisms or degradation. However Yamazaki et al.²⁷³ have reported on what is claimed to be the direct observation of lithium ion concentration in composite electrodes in situ. Li ion battery model cells were constructed for use with PIXE and PIGE techniques. Proton microbeam scanning was used to generate 2D images of Li concentration in LiFePO₄ composite electrodes in various charged states. This publication is very important because it creates the opportunity to study changes in a wide range of lithium battery configurations in real time. The identification of the chemical environment of Li ions contained within Li compounds is important for the understanding of battery component function and overall performance. Consequently, the publication of a XANES-generated library of lithium compounds represents an important contribution to the literature.²⁷⁴ The K-edge X-ray absorption near edge structures for Li compounds found in every possible battery component including lithium metals (anode), Li-containing cathodes, electrolytes and solid electrolyte interphase were reported. It was noted that sensitive Li species might be damaged by X-ray radiation and the authors suggest that this possibility deserves greater attention in the characterisation of these type of material.

X-ray photoelectron spectroscopy is one of many surface analysis techniques that have been applied to the study of lithium ion batteries. The technique offers the possibility of determining chemical information from peak shift phenomena arising from electrical charging. Oswald, Hoffmann and Zier²⁷⁵ have reported on XPS peak shifts observed in the sputter depth profiling of surface layers in graphite anodes. However, these peak shifts were found to result from the state of charge, or lithiation, of the anode material rather than to classical static electric charging. The cause of the peak shifts was attributed to the presence of Li in its elemental state. It was concluded that the binding energy scale had to be corrected to achieve accurate results and that this could be achieved by implanting Ar to identify the relevant peak positions. Clearly this is an important contribution that should help in the interpretation of XPS spectra in relation important applications such as the investigation of battery ageing and failure.

The dissolution of metals from positive battery electrodes has a direct impact on the durability of such batteries. Thus Shilina et al.²⁷⁶ have investigated the rate of dissolution and oxidation states of Mn ions in electrodes using the unusual combination of EPR with AAS and ICP-OES. The dissolution of Mn cations from active electrode material (LiMn₂O₄ and LiNi_0.5 Mn_1.5O₄) and Mn²⁺standards into the same electrolyte solution was monitored. The correlation between EPR and AAS/ICP-OES results for and samples allowed speciation of the Mn ions. Using this approach, it was established that the predominance of Mn ion species was more dependent on the nature of the electrode material than on the rate of dissolution. However, it is worth noting in this context that pre-separation/preconcentration methods used in combination with AAS or ICP-OES might have allowed a simpler means of speciation Mn in this application.

3.10 Nanostructures

This section is devoted to providing an overview of progress made in addressing the unique challenges posed by the developing nanomaterials and nanostructures. Applications are also considered in the metals and inorganic materials sections of this review where appropriate. However, because methods arising from this rapidly-evolving field of materials research may have wider applications, a summary of the main developments is presented here.

Approaches to the analysis of nanostructures have evolved over the last few review periods. The main technique employed for nanoparticle analysis is still single particle ICP-MS (SP-ICP-MS). However, two years ago, SP-ICP-MS was still in its infancy and many researchers using it were still attempting to identify optimal operating conditions (dwell times, concentrations etc.). The last couple of years have seen a steady increase in the number of applications of SP-ICP-MS. It is already established that SP-ICP-MS is capable of yielding data both in terms of particle size number distribution and in concentration. Instrumental dwell times must be sufficiently small to ensure that only one particle is detected at any instant (this also requires the sample to be very dilute), but sufficiently large to enable the signal from a larger particle to be captured completely. Nebulisation/sample transport efficiency should also be optimised. Some approaches still have trouble differentiating between extremely small particles and dissolved ions. Differentiating between natural and engineered nanoparticles is also problematic. Several papers in this review period have discussed these remaining problems.

3.10.1 Reviews. Several reviews of nanoparticle analysis have been published. Some of these reviews have a focus on sample preparation methods, whereas others were concerned with a specific area of interest, e.g. SP-ICP-MS. Costa-Fernandez et al.²⁹⁸ reviewed (with 82 references) the use of MS for the characterization and quantification of engineered inorganic nanoparticles. Numerous MS techniques were considered including direct methods of analysis (e.g. slurry atomisation, ETV introduction etc.), SP-ICP-MS and varying forms of chromatography coupled with ICP-MS (e.g. FFF, liquid chromatography, ion mobility analysis and electrophoretic methods). Also included were molecular mass spectrometry techniques such as matrix assisted laser desorption/ionization and electrospray ionization. The authors provided insights into likely future trends in this field. A review of SP-ICP-MS, containing 146 references, was published by Montano et al.²⁹⁹ The advantages of the technique (rapidity, yields data of size, size distribution, particle number concentration and major elemental composition) were discussed. In addition, advances in data processing were highlighted in the review. The transformation of SP-ICP-MS from a niche technique into a widely used application tool for the analysis of complex environmental and biological samples was discussed. The final review relevant to this section was prepared by De la Calle et al.,³⁰⁰ who reviewed the current trends in sample preparation for metallic nanoparticle analysis in daily products and environmental samples. With the aid of 254 references, sample preparation methods between 2006 and 2015 were discussed. These included those for the determination of total elemental content (e.g. acid digestion, alkaline fusion, dry ashing and direct solid analysis). In addition, methods used for extraction/separation e.g. evaporation, centrifugation, filtration, centrifugal ultrafiltration and dialysis and the extraction/preconcentration procedures such as liquid–liquid extraction (LLE), solid phase extraction (SPE), magnetic solid phase extraction (MSPE) and cloud point extraction were also discussed. As well as these methods that are most commonly used with atomic spectrometric detection, other methods that may be used for electron microscopic studies, e.g. direct analysis, coating, fixation, resin embedding, dehydration, microtomy as well as the most recent applications, were also reviewed.

3.10.2 Single particle-ICP-MS. Single particle ICP-MS has continued to be one of the most favoured methods of analysis for nanoparticles. As discussed previously, the focus has changed slightly from theoretical aspects on optimisation of the process to actual applications. There have, however, still been plenty of papers produced that describe optimisation or approaches to overcoming problems with SP-ICP-MS. Two papers have discussed approaches to sensitivity drift correction during SP-ICP-MS. In the first,³⁰¹ theoretical modelling and experimental data agreed and indicate that signal drift can account for a 25% error in the particle sizing. A method was developed that used an internal standard to correct for the problem. The method could correct for a 50% drift for 30 and 60 nm Au nanoparticles. Cornelis and Rauch³⁰² also tackled the problem of sensitivity drift during SP-ICP-MS analyses of 10 and 60 nm Au nanoparticles. They stated that deconvolution, a method that models signal distributions of dissolved signals, fails sometimes when using samples and standards affected by drift. The method reported in the paper enabled drift correction by adjusting the local (moving) averages and standard deviations to the respective values at a reference time. Signals from larger particles did have to be removed prior to the drift correction. This was done using a 3 × sigma method. Flicker noise was also shown to be corrected for.

The dwell time required for SP-ICP-MS is a topic that has been covered at length over several years. Despite this, research is on-going. Abad-Alvaro et al.³⁰³ studied the effects of dwell times of 10 ms, 5 ms, 100 μs and 50 μs on the quality of the data. The precision was approximately 5% when ms dwell times were used. The precision improved to approximately 1% when μs dwell times were used. The more rapid dwell times (100 μs or less) decreased the occurrence of multiple nanoparticle events and extended the linear range. It had the bonus of reducing the contribution of the background and/or the presence of dissolved species. The dwell time to be used would depend on the particle size and concentration in the sample. If the concentration is high, then a shorter dwell time is necessary to avoid multi-particle events. However, the longer dwell times are necessary if the particles are larger in size or refractory. This is necessary so that the entire particle may be atomised and the signal from it captured in one pulse. If a very short dwell time is used, then the signal arising from one particle may be recorded over several time sectors. The authors therefore concluded that, although the short dwell times provided improved precision of the data, their use should not be discounted.

The transport of nanoparticle – based solutions to the plasma is another factor that can affect accuracy and precision. This is because it is a possible source of bias in the results. Miyashita et al.³⁰⁴ described the high transport efficiency produced by a total-consumption sample introduction system and explained its benefits for particle size evaluation. The system used was a wide bore, high performance concentric nebuliser with a small-volume on axis cylinder spray chamber. The transport efficiency could effectively be close to 100%, thereby eliminating the potential bias. This means that the post-analysis mathematics is also simplified because there is no need for a transport efficiency correction to be made when calculating particle number concentration or particle sizing. Platinum nanoparticles of nominally 70 nm diameter were used to calculate the transport efficiency which was approximately 93%. Precision of this measurement was 1% RSD for four consecutive measurements. Having tested their methodology, the authors used it to determine the particle diameters of Pt and Ag nanoparticles with nominal diameters of 30–100 nm. Results were in good agreement with those obtained using TEM.

The use of flow injection (FI)-SP-ICP-MS to overcome the necessity of determining transport efficiency and uptake rate has been described by Lamsal et al.³⁰⁵ Unlike normal SP-ICP-MS, the transport efficiency in the FI version is unaffected by changes in the uptake rate. A measurement of the transport efficiency is only needed if particle number measurements are required. Analysis of a suspension of Au nanoparticles using both SP-ICP-MS and FI-SP-ICP-MS yielded particle size measurements that were both in agreement with the nominal diameter of 60 nm. Three sample loading concentrations: 40, 100 and 200 ng L⁻¹ were used. The dwell time used was 5 ms and the settling time 200 μs. A loop of 100 μL was used for sample introduction. The size detection limit was 20 nm, which was similar to that achieved using conventional SP-ICP-MS.

Sotebier et al.^306,307 have published two papers that have used isotope dilution SP-ICP-MS. The first concerned the use of using a quadrupole-based ICP-MS instrument, a dwell time of 10 ms and a modified mass flow equation for the determination of size, particle number and number size distribution of Ag nanoparticles. Citrate stabilised Ag nanoparticles of nominal diameters 30, 40, 50 and 80 nm were investigated. A spike of 1 μg L^{−1 109}Ag⁺ was added to the nanoparticle suspensions (nominally 200 ng L⁻¹) and therefore the signal from this spike dominated the signal at m/z 109. Therefore, the signals from the nanoparticles were visible only in the ¹⁰⁷Ag⁺ scan. Changing the spike concentration between 0.5 and 4 μg L⁻¹ did not change the particle diameter measurements, indicating that the calculated diameter is independent of the spiking concentration. The seawater matrix affected the particle size measurements because of a difference in transport efficiency between the matrix and an aqueous standard (6.1% compared with 7.3%). In addition, the signal intensity of the nanoparticles was influenced by the matrix more strongly than the ionic solutions. This led to the particle size measurements being smaller in the seawater matrix than aqueous standards. The sample matrix had the added disadvantage of deteriorating the instrumental sensitivity leading to the particle size LOD being increased to 30 nm.

The use of a coupled method combining high-performance liquid chromatography and isotope dilution inductively coupled plasma mass spectrometry (HPLC-ICP-MS) was described in a second paper.³⁰⁷ Single particle ICP-MS was also employed as an independent check method. The different sized nanoparticles and ionic Ag were separated on two C18, reversed phase column using sodium dodecyl sulfate, penicillamine and ammonium acetate at pH 6.7 as an eluent and a flow rate of 0.5 mL min⁻¹. The ¹⁰⁹Ag isotopic spike (1.91 μg L⁻¹) was added post column at a flow rate of 94 μL min⁻¹. One column had pore size of 1000 Angstrom whereas the other had 4000 Angstrom. The column separated the nanoparticles according to size in a similar fashion to size exclusion chromatography; i.e. the smaller particles elute later than larger ones. The column with the smaller pore size separated the particles more rapidly than the larger pore version. The authors concluded that other interactions between nanoparticles and the columns must exist because larger nanoparticles did not elute from the column and recovery rates decreased with increasing particle size. An increase in column back pressure after several injections occurred, indicating a build-up of nanoparticles/agglomerates. Washing the column with acetonitrile restored the usual back pressure, indicating that the nanoparticles could be removed.

Frechette-Viens³⁰⁸ noted that significant loss of La was observed when La₂O₃ nanoparticles were analysed. Losses due to adsorption to sample container walls were significant (>72%) for fluorinated ethylene propylene (FEP) bottles, but somewhat less (<10%) for polypropylene. Further losses were observed when nebulisers made from perfluoro alkoxy alkane (PFA) or glass, PFA capillary tubing, or polyvinyl chloride (PVC) pump tubing were used. The presence of natural organic matter in the samples alleviated these problems to an extent, which led the authors to conclude that the method could still be used for environmental samples. Experiments using SP-ICP-MS were undertaken using a dwell time of 500 μs (after optimization), a concentration of 1 μg L⁻¹ and transport efficiency was evaluated using NIST 8013 Au nanoparticles. The particle size distribution was plotted for samples that had spent one day in FEP and polypropylene bottles, with results indicating that those stored in FEP were marginally smaller. A further aspect of this study was the use of a Chelex-100 chelation column to retain ionic La. This had the effect of clarifying the border between ionic La and smaller nanoparticulate La. The particle size LOD was therefore reduced from 26 nm to 17 nm.

Montano et al.³⁰⁹ described methods for the detection and characterization of silica colloids by single particle inductively coupled plasma mass spectrometry. Silicon is a problematic analyte for quadrupole ICP-MS instruments because of polyatomic interferences arising from nitrogen dimers. The authors adopted three methods to overcome this difficulty, including the use of helium collision cell gas, the use of ammonia as a reaction gas and a new method involving the use of μs-SP-ICP-MS. The use of collision cell gases can lead to a dramatic loss of sensitivity, especially at the low mass range. The μs-SP-ICP-MS method circumvented this problem because at the shorter dwell times the proportion of signal attributable to a nanoparticle event is greater relative to the constant signal from the nitrogen dimer. Particle size comparisons between SP-ICP-MS (with and without ammonia or helium) with TEM and SEM measurements were made. The particle size LOD was particularly poor for the helium cell, so no data were obtained below 40 nm particles. The ammonia cell was somewhat better, with the size LOD being approximately 200 nm. Unsurprisingly, the conclusion was that optimal accuracy of detection of nanoparticles was achieved through obtaining a balance between reducing background interferences and maintaining the signal generated by the nanoparticle.

Schwertfeger et al.³¹⁰ developed a novel method of overcoming problems associated with dissolved ions confounding nanoparticle determinations during single-particle inductively coupled plasma mass spectrometry measurements. The authors proposed a simple, systematic dilution series approach in which the first dilution is used to quantify the dissolved analyte, the second dilution is used to bring the particle signal into the required “very dilute” range and a third dilution is used as an analytical quality control. This dilution approach was used to analyse suspensions of certified Ag and Au nanoparticles with known size (NIST 8012 and NIST 8013) that had been spiked with a known concentration of ionic analyte. The dilution improved the resolution between ionic and nanoparticulate. This improves the accuracy of particle counts, quantification of particulate mass and particle size measurement.

The behaviour of silver nanoparticles surface-coated with polymers in lake water has been investigated by Jimenez-Lamana and Slaveykova.³¹¹ The authors used SP-ICP-MS, asymmetric flow field flow fractionation (AF4) and surface plasmon resonance to make a thorough study of the nanoparticle agglomeration behaviour. Citrate, polyvinyl pyrrolidone (PVP) and lipoic acid coatings on Ag nanoparticles were all tested along with the influence of size (20 and 50 nm), exposure time and the presence of dissolved organic matter. The surface coating was the dominant factor determining the behaviour although dissolved organic matter and exposure time were also factors. The PVP coated particles agglomerated to a lesser extent than either the citrate or lipoic acid-coated nanoparticles. The dissolved organic matter appeared to assist in stabilising the materials, but the extent of this stabilisation was dependent on the nature of the organic matter.

The stability of silver nanoparticles under environmental conditions in aqueous samples was studied by Telgmann et al.³¹² The particles were placed in ultra-pure water and were then exposed to different temperatures, different pH and to ozonation. The differing solutions were analysed using SP-ICP-MS and dynamic light scattering. Extremes of pH led to increased dissolution, whereas low temperature decreased the rate of dissolution. Addition of chloride resulted in agglomeration and precipitation of the Ag nanoparticles, whereas ozonation led to a rapid decline in the number and size of particles. A concomitant increase in the concentration of Ag ions indicated that ozonation resulted in oxidative dissolution of the nanoparticles. Azodi et al.³¹³ also studied the dissolution of Ag nanoparticles in waters. Both PVP- and citrate-coated nanoparticles were studied. In accordance with the study by Telgmann, the dissolved oxygen content was one of the determining factors on the dissolution, with low oxygen content leading to less dissolution. Waste water contained significantly less (71% less) dissolved Ag than pure water. The authors attributed this to reformation of particulates through reactions with sulfide-based chemicals, including inorganic sulfides, organo-sulfur and cysteine. Both TOF-SIMS and XPS were used to elucidate the structure of the bonding of the S-containing groups with the nanoparticles. Kim et al.³¹⁴ also studied agglomeration of Ag nanoparticles. Both citrate-coated and PVP-coated particles of two sizes were tested. The citrate-coated particles agglomerated with increasing ionic strength, whereas the PVP-coated particles were more stable. The critical aggregation concentrations of sodium nitrate were 85 mM and 100 mM for 60 nm and 100 nm particles, respectively. Both sizes of particle were present at 2 mg L⁻¹. The authors went on to estimate the number of individual nanoparticles that constitute an agglomerate. The determination of nanoparticles in polymers or food simulant leachates from polymers has been reported in several papers.^55–58 These are all discussed in the Polymers section of this review (Section 2.7) so will not be discussed further here.

Hsiao et al.³¹⁵ measured the uptake of TiO₂ (7 or 20 nm) and Ag (50 or 75 nm) nanoparticles into Neuro-2a cells. The cells were treated with 2 or 10 mg L⁻¹ nanoparticle suspensions for 24 hours. After washing in saline solution to remove the nanoparticles loosely attached to the cell walls, the cells were analysed using one of three techniques. The cells were either fixed and then analysed using LA-ICP-MS or lysed and analysed using standard ICP-MS (after digestion of the lysate) or SP-ICP-MS. The LA-ICP-MS enabled localisation of the particulates within individual cells. The standard ICP-MS method yielded data on mass concentration whereas the SP-ICP-MS provided number concentration, size analysis and size distribution. All three techniques demonstrated that the smaller particles were taken up by the cells in preference to the larger ones. However, SP-ICP-MS showed that, once inside the cells, the nanoparticles formed micron-sized agglomerates. Laser ablation-ICP-MS showed that the larger Ag nanoparticles (75 nm) often became adsorbed to the cell membrane, rather than fully penetrating it, like the 50 nm ones did. The LA-ICP-MS approach was capable of analysing the small nanoparticles (7 nm), which was not possible using SP-ICP-MS. High cell to cell variability was also observed. Merrifield et al.³¹⁶ used FFF with UV detection as well as SP-ICP-MS to characterise mixtures of monometallic and bimetallic (core–shell) nanoparticles. Again, full experimental details were given. The SP-ICP-MS parameters included a dwell time of 50 μs, a sample uptake rate of 0.49 mL min⁻¹ and the transport efficiency was calculated through aspiration of a standard NIST Au nanoparticle suspension (NIST 8012 and NIST 8013). Analysis of the NIST Au nanoparticle suspension using SP-ICP-MS yielded particle sizes in good agreement with nominal values. A series of particle number concentration determinations were reported showing precision within 0.5%. Analysis of the 30 nm Au nanoparticle solution yielded particle number concentration data within 3% of the values stated by NIST. The 60 nm Ag and 60 nm Au nanoparticles as well as the 60 nm bi-metallic particles (30 nm core and 15 nm shell) all eluted.

The final paper to use SP-ICP-MS was presented by Johnson et al.³¹⁷ The technique was combined with sucrose density gradient centrifugation (SDGC) for the separation of Au nanoparticles. A mixture of three Au nanoparticle sizes (30, 80 and 150 nm) was fully separated using the SDGC. However, two other mixtures (30, 60 and 100 nm as well as 20, 50 and 100 nm) were less successfully separated. The SDGC procedure was easily overwhelmed when the particle number concentration is excessive. Despite these problems, the combination of the two techniques enabled detection and some particle sizing of Au nanoparticles at sub-ng L⁻¹ concentrations, which is lower than that possible by other techniques, e.g. SEM, TEM, AFM, and DLS.

Conflicts of interest

There are no conflicts of interest.

Abbreviations

2D	Two dimensional
3D	Three dimensional
AAS	Atomic absorption spectrometry
AES	Auger electron spectrometry
AFFF	Asymmetric field flow fractionation
AF4	Asymmetric flow-field flow fractionation
AFS	Atomic fluorescence spectrometry
AFM	Atomic force microscopy
AMS	Accelerator mass spectrometry
APM	Atom probe microscopy
APT	Atom probe tomography
ASTM	American society for testing of materials
ATR	Attenuated total reflection
BCR	Community bureau of reference
CCD	Charge coupled device
CE	Capillary electrophoresis
CRM	Certified reference material
CPFAAS	Collinear photofragmentation atomic absorption spectrometry
CS	Continuum source
CT	Computerised tomography
CV	Cold vapour
CXRF	Coincidence X-ray fluorescence
DA	Discriminant analysis
DLS	Dynamic light scattering
DLTV	Diode laser thermal vaporisation
DRC	Dynamic reaction cell
DSC	Differential scanning calorimetry
EBS	Elastic back scattering spectroscopy
EDAX	Energy dispersive X-ray analysis
EDS	Energy dispersive spectrometry
EDTA	Ethylenediamine tetraacetic acid
EDXRD	Energy dispersive X-ray diffraction
EDXRF	Energy dispersive X-ray fluorescence
EPMA	Electron probe microanalysis
ERDA	Elastic recoil detection analysis
ESI-MS	Electrospray ionisation mass spectrometry
ETAAS	Electrothermal atomic absorption spectrometry
ETV	Electrothermal vaporisation
EXAFS	Extended X-ray absorption fine structure
FAAS	Flame atomic absorption spectrometry
FFF	Field flow fractionation
FI	Flow injection
FI-CVG	Flow injection chemical vapour generation
FTIR	Fourier transform infrared
FWHM	Full width at half maximum
GA-XRD	Grazing incidence X-ray diffraction
GC	Gas chromatography
GD-MS	Glow discharge mass spectrometry
GD-OES	Glow discharge optical emission spectrometry
GI-SAXS	Grazing incidence small angle X-ray scattering
GI-XRD	Grazing incidence X-ray diffraction
GI-XRF	Grazing incidence X-ray fluorescence
HG	Hydride generation
HPLC	High performance liquid chromatography
HR-CS-AAS	High resolution continuum source atomic absorption spectrometry
hTISIS	Heated torch integrated sample introduction system
IAEA	International atomic energy agency
IBA	Ion beam analysis
ICP	Inductively coupled plasma
ICP-MS	Inductively coupled plasma mass spectrometry
ICP-OES	Inductively coupled plasma optical emission spectrometry
ICP-QMS	Inductively coupled plasma quadrupole mass spectrometry
ICP-TOF-MS	Inductively coupled plasma time-of-flight mass spectrometry
ID	Isotope dilution
IL-DLLME	Ionic liquid-dispersive liquid–liquid microextraction
IP	Institute of petroleum
IRMS	Isotope ration mass spectrometry
ISO	International organisation for standardisation
LA	Laser ablation
LASIL	Laser ablation of sample in liquid
LC	Liquid chromatography
LEIS	Low energy ion scattering
LIBS	Laser induced breakdown spectrometry
LIF	Laser induced fluorescence
LIPS	Laser induced plasma spectroscopy
LOD	Limit of detection
LOQ	Limit of quantification
LTE	Local thermal equilibrium
MALDI-TOF	Matrix-assisted laser desorption ionisation time-of-flight
MC	Multicollector
MEIS	Medium energy light scattering
MHCD	Micro hollow glow discharge
MIP	Microwave induced plasma
MIP-AES	Microwave plasma atomic emission spectrometry
MS	Mass spectrometry
MCR-ALS	Multi curve resolution-alternating least squares
MWTN	Microwave thermal nebuliser
NAA	Neutron activation analysis
NAAR	Neutron activation autoradiography
Nd : YAG	Neodymium doped-yttrium aluminium garnet
Nd : YLF	Neodymium doped-yttrium lithium fluoride
ND	Neutron diffraction
NIST	National institute of standards and technology
NMR	Nuclear magnetic resonance
NRA	Nuclear reaction analysis
OES	Optical emission spectrometry
PARCI	Plasma assisted reaction chemical ionisation
PCA	Principal components analysis
PDA	Phase-doppler anemometry
PGAA	Prompt gamma neutron activation analysis
PGM	Platinum group metals
PIGE	Particle induced gamma ray emission
PIXE	Particle-induced X-ray emission
PSDA	Particle size distribution analysis
PLS	Partial least squares
PLS-DA	Partial least squares discriminant analysis
ppb	Parts per billion
ppm	Parts per million
PVG	Photochemical vapour generation
RBS	Rutherford backscattering spectrometry
RDA	Regularised discriminant analysis
REE	Rare earth elements
rf	Radiofrequency
RIMS	Resonance ionisation mass spectrometry
RSD	Relative standard deviation
SEM	Scanning electron microscopy
SEM-EDS	Scanning electron microscopy-energy dispersive spectrometry
SF	Sector field
SIFT-MS	Selected ion flow tube mass spectrometry
SIMCA	Soft independent modelling of class analogy
SIMS	Secondary ion mass spectrometry
SP	Single particle
SR	Synchrotron radiation
SRM	Standard reference material
SRS	Synchrotron radiation source
SXRF	Synchrotron X-ray fluorescence
STXM	Scanning transmission X-ray microscopy
TE	Trace element
TEM	Transmission electron microscopy
TGA	Thermogravimetic analysis
TIMS	Thermal ionisation mass spectrometry
TLC	Thin layer chromatography
TPR	Temperature programmed reduction
TXRF	Total reflection X-ray fluorescence
UOP	Universal oil products standards
USGS	United states geological survey
UV-Vis	Ultraviolet-visible
VOC	Volatile organic carbon
VUV	Vacuum ultraviolet
WDXRF	Wavelength dispersive X-ray fluorescence
WC-AES	Tungsten coil atomic emission spectrometry
XAFS	X-ray absorption fine structure spectrometry
XANES	X-ray absorption near-edge structure
XAS	X-ray absorption spectroscopy
XPS	X-ray photoelectron spectroscopy
XRD	X-ray diffraction
XRF	X-ray fluorescence
XRPD	X-ray powder diffraction
XRR	X-ray reflectometry

References

1. S. Carter, A. Fisher, R. Garcia, B. Gibson, J. Marshall and I. Whiteside, J. Anal. At. Spectrom., 2016, 31(11), 2114–2164.
2. E. G. Zhang, Q. Chen, Q. X. Wang and B. Huang, Surf. Rev. Lett., 2016, 23(6), 10.
3. I. Bacic, H. O. Curkovic, L. Curkovic, V. Mandic and Z. Sokcevic, Int. J. Electrochem. Sci., 2016, 11(11), 9192–9205.
4. T. L. Zhang, D. H. Xia, H. S. Tang, X. F. Yang and H. Li, Chemom. Intell. Lab. Syst., 2016, 157, 196–201.
5. Z. M. Wang, C. H. Yan, J. Dong, T. L. Zhang, J. Wei and H. Li, RSC Adv., 2016, 6(80), 11.
6. H. Y. Kong, L. X. Sun, J. T. Hu and P. Zhang, Spectrosc. Spectrum Anal., 2016, 36(5), 1451–1457.
7. C. M. Li, Z. Q. Hao, Z. M. Zou, R. Zhou, J. M. Li, L. B. Guo, X. Y. Li, Y. F. Lu and X. Y. Zeng, Opt. Express, 2016, 24(8), 7850–7857.
8. Y. Zhang, Y. H. Jia, C. Yang, D. L. Li, J. Liu, Y. Y. Chen, Y. Liu and Y. X. Duan, Front. Phys., 2016, 11(6), 7.
9. D. Janis, P. G. Jonsson, A. Appell and J. Janis, Ironmak. Steelmak., 2016, 43(2), 121–129.
10. B. Meermann, K. Wichmann, F. Lauer, F. Vanhaecke and T. A. Ternes, J. Anal. At. Spectrom., 2016, 31(4), 890–901.
11. C. Vasilescu, P. Osiceanu, J. M. C. Moreno, S. I. Drob, S. Preda, M. Popa, I. Dan, M. Marcu, M. Prodana, I. A. Popovici, D. Ionita and E. Vasilescu, Mater. Sci. Eng., C, 2017, 71, 322–334.
12. G. L. Li, L. Li, J. L. Hao, J. Zheng, W. H. Tian and X. G. Li, Mater. Lett., 2016, 176, 253–256.
13. A. Smaldone, A. De Bonis, A. Galasso, A. Guarnaccio, A. Santagata and R. Teghil, Z. Phys. Chem., 2016, 230(9), 1201–1217.
14. J. Viljanen, Z. W. Sun and Z. T. Alwahabi, Spectrochim. Acta, Part B, 2016, 118, 29–36.
15. Y. Wang, A. M. Chen, L. Z. Sui, S. Y. Li, D. L. Liu, X. W. Wang, Y. F. Jiang, X. R. Huang and M. X. Jin, Phys. Plasmas, 2016, 23(11), 7.
16. S. Guirado, F. J. Fortes and J. J. Laserna, Appl. Spectrosc., 2016, 70(4), 618–626.
17. Y. Xin, L. X. Sun, Z. J. Yang, P. Zeng, Z. B. Cong and L. F. Qi, Front. Phys., 2016, 11(5), 10.
18. S. A. Davari, S. Hu and D. Mukherjee, Talanta, 2017, 164, 330–340.
19. M. Resano, E. Garcia-Ruiz and R. Garde, J. Anal. At. Spectrom., 2016, 31(11), 2233–2241.
20. J. Milot, F. Poitrasson, S. Baron and M. P. Coustures, J. Archaeol. Sci., 2016, 76, 9–20.
21. L. Torrisi, A. Italiano and A. Torrisi, Appl. Surf. Sci., 2016, 387, 529–538.
22. M. Clemenza, A. Contini, G. Baccolo, M. L. di Vacri, M. Ferrante, S. Nisi, M. Carpinelli, O. Cremonesi, S. Enzo, E. Fiorini, G. Mulas, M. Prata, E. Previtali, A. Salvini and V. Sipala, J. Radioanal. Nucl. Chem., 2017, 311(2), 1495–1501.
23. Z. Kowalewska, J. Pilarczyk and L. Gosciniak, Spectrochim. Acta, Part B, 2016, 120, 45–56.
24. F. R. F. Simoes, N. M. Batra, B. H. Warsama, C. G. Canlas, S. Patole, T. F. Yapici and P. Costa, Anal. Chem., 2016, 88(23), 11783–11790.
25. D. L’Hermite, E. Vors, T. Vercouter and G. Moutiers, Environ. Sci. Pollut. Res., 2016, 23(9), 8219–8226.
26. Y. Zhao, X. Y. Chen, D. Y. Xu, S. Y. Zhang and J. Liao, Spectrosc. Spectrum Anal., 2016, 36(10), 3303–3307.
27. F. Trichard, V. Forquet, N. Gilon, C. P. Lienemann and F. Baco-Antoniali, Spectrochim. Acta, Part B, 2016, 118, 72–80.
28. F. Trichard, N. Gilon, C. P. Lienemann and F. Baco-Antoniali, J. Anal. At. Spectrom., 2016, 31(3), 712–721.
29. G. Corazza, A. S. Henn, M. F. Mesko, F. A. Duarte, E. M. M. Flores and P. A. Mello, J. Braz. Chem. Soc., 2016, 27(9), 1569–1576.
30. Z. Y. Hou, Z. Wang, T. B. Yuan, J. M. Liu, Z. Li and W. D. Ni, J. Anal. At. Spectrom., 2016, 31(3), 722–736.
31. A. A. Artemyeva, W. S. Seames, D. T. Pierce and E. I. Kozliak, Fuel, 2017, 188, 544–552.
32. N. Mketo, P. N. Nomngongo and J. C. Ngila, TrAC, Trends Anal. Chem., 2016, 85, 107–116.
33. G. T. Druzian, C. K. Giesbrecht, F. C. Rosa, R. C. L. Guimaraes, R. A. Guarnieri, P. A. Mello and E. M. M. Flores, Quim. Nova, 2016, 39(9), 1065–1070.
34. C. Walkner, R. Gratzer, T. Meisel and S. N. H. Bokhari, Org. Geochem., 2017, 103, 22–30.
35. J. F. Casey, Y. J. Gao, W. H. Yang and R. Thomas, Spectroscopy, 2016, 31(10), 11–22.
36. Z. X. Ling, Y. M. He, P. Y. Wang, Y. Zhou, F. Ahmad and F. F. Zhao, Anal. Lett., 2016, 49(15), 2461–2473.
37. C. Sanchez, C. P. Lienemann and J. L. Todoli, Spectrochim. Acta, Part B, 2016, 124, 99–108.
38. P. Sommersacher, N. Kienzl, T. Brunner and I. Obernberger, Energy Fuels, 2016, 30(4), 3428–3440.
39. E. N. Rao, P. Mathi, S. A. Kalam, S. Sreedhar, A. K. Singh, B. N. Jagatap and S. V. Rao, J. Anal. At. Spectrom., 2016, 31(3), 737–750.
40. Q. Q. Wang, L. A. He, Y. Zhao, Z. Peng and L. Liu, Laser Phys., 2016, 26(6), 8.
41. C. M. Mahoney, R. T. Kelly, L. Alexander, M. Newburn, S. Bader, R. G. Ewing, A. J. Fahey, D. A. Atkinson and N. Beagley, Anal. Chem., 2016, 88(7), 3598–3607.
42. K. M. Evans-Nguyen, J. Gerling, H. Brown, M. Miranda, A. Windom and J. Speer, Analyst, 2016, 141(12), 3811–3820.
43. N. Lewen, Preparation of Pharmaceutical Samples for Elemental Impurities Analysis: Some Potential Approaches, Spectroscopy, Apr 01, 2016, vol. 31, issue 4, pp. 36–43.
44. C. G. Jin, AAPS PharmSciTech, 2016, 17(5), 1141–1149.
45. B. L. Ferreira, L. Vitali and E. S. Chaves, J. Braz. Chem. Soc., 2016, 27(4), 794–798.
46. M. S. Jimenez, J. M. Luque-Alled, T. Gomez and J. R. Castillo, Electrophoresis, 2016, 37(10), 1376–1383.
47. Z. X. Zhong and G. K. Li, J. Sep. Sci., 2017, 40(1), 152–169.
48. A. D. Augusto, E. F. Batista and E. R. Pereira, Anal. Methods, 2016, 8(29), 5851–5860.
49. A. I. Barros, D. V. de Babos, E. C. Ferreira and J. A. G. Neto, Talanta, 2016, 161, 547–553.
50. F. L. Melquiades and A. M. A. da Silva, Anal. Methods, 2016, 8(19), 3920–3926.
51. T. Trejos, P. Torrione, R. Corzo, A. Raeva, K. Subedi, R. Williamson, J. Yoo and J. Almirall, J. Forensic Sci., 2016, 61(3), 715–724.
52. R. E. Goacher, L. G. DiFonzo and K. C. Lesko, Anal. Chem., 2017, 89(1), 759–766.
53. C. M. Chan and L. T. Weng, Materials, 2016, 9(8), 19.
54. M. Whitt, W. Brown, J. E. Danes and K. L. Vorst, J. Plastic Film Sheeting, 2016, 32(2), 189–207.
55. K. Ramos, M. M. Gomez-Gomez, C. Camara and L. Ramos, Talanta, 2016, 151, 83–90.
56. A. Ozaki, E. Kishi, T. Ooshima, A. Hase and Y. Kawamura, Food Addit. Contam., Part A, 2016, 33(9), 1490–1498.
57. F. Liu, C. Y. Hu, Q. Zhao, Y. J. Shi and H. N. Zhong, Food Addit. Contam., Part A, 2016, 33(11), 1741–1749.
58. S. A. Ntim, T. A. Thomas and G. O. Noonan, Food Addit. Contam., Part A, 2016, 33(5), 905–912.
59. A. Turner, Mar. Pollut. Bull., 2016, 111(1–2), 136–142.
60. A. Turner and K. S. Lau, Mar. Pollut. Bull., 2016, 112(1–2), 265–270.
61. J. D. Wang, J. P. Peng, Z. Tan, Y. F. Gao, Z. W. Zhan, Q. Q. Chen and L. Q. Cai, Chemosphere, 2017, 171, 248–258.
62. H. K. Imhof, C. Laforsch, A. C. Wiesheu, J. Schmid, P. M. Anger, R. Niessner and N. P. Ivleva, Water Res., 2016, 98, 64–74.
63. H. P. Mi, H. M. Zhu, G. R. Li, X. Yu, B. B. Ma, X. L. Zhou, Y. S. Wang, Z. H. Su and X. Deng, Spectrosc. Spectrum Anal., 2017, 37(2), 646–650.
64. S. R. Krzyzaniak, R. F. Santos, F. M. D. Nora, S. M. Cruz, E. M. M. Flores and P. A. Mello, Talanta, 2016, 158, 193–197.
65. L. M. Hallerstig, P. Granath, L. Lindgren and M. Tranberg, Anal. Methods, 2017, 9(1), 149–153.
66. S. Trautner, J. Jasik, C. G. Parigger, J. D. Pedarnig, W. Spendelhofer, J. Lackner, P. Veis and J. Heitz, Spectrochim. Acta, Part A, 2017, 174, 331–338.
67. X. L. Wang, H. Wang, C. Chen and Z. D. Jia, IEEE Trans. Plasma Sci., 2016, 44(11), 2766–2771.
68. M. Kayar, B. Izgi and M. A. Cimenoglu, Int. J. Mass Spectrom., 2016, 399, 10–16.
69. A. Guzzonato, F. Puype and S. J. Harrad, Chemosphere, 2016, 159, 89–95.
70. Y. H. Chu, H. Y. Liao, K. Y. Lin, H. Y. Chang, W. L. Kao, D. Y. Kuo, Y. W. You, K. J. Chu, C. Y. Wu and J. J. Shyue, Analyst, 2016, 141(8), 2523–2533.
71. L. Rzeznik, Y. Fleming, T. Wirtz and P. Philipp, Beilstein J. Nanotechnol., 2016, 7, 1113–1128.
72. A. Pelster, M. Korsgen, T. Kurosawa, H. Morita and H. F. Arlinghaus, Anal. Chem., 2016, 88(19), 9638–9646.
73. T. Greunz, H. Duchaczek, R. Sagl, J. Duchoslav, R. Steinberger, B. Strauss and D. Stifter, Appl. Surf. Sci., 2017, 396, 665–671.
74. A. H. Rezaei, M. H. Keshavarz, M. K. Tehrani, S. M. R. Darbani, A. H. Farhadian, S. Jabbarmousavi and A. Mousaviazar, Appl. Optic., 2016, 55(12), 3233–3240.
75. I. Patel, C. J. Venkatramani, A. Stumpf, L. Wigman and P. Yehl, Org. Process Res. Dev., 2017, 21(2), 182–186.
76. M. A. Bazhenov, V. D. Tikhova and V. P. Fadeeva, J. Anal. Chem., 2016, 71(11), 1089–1095.
77. A. C. M. Aleluia, F. A. de Santana, G. C. Brandao and S. L. C. Ferreira, Microchem. J., 2017, 130, 157–161.
78. E. K. Choe, Y. Z. Kim, Y. Dal Cho and S. H. Son, Text. Res. J., 2016, 86(17), 1847–1858.
79. A. V. Zmozinski, T. Pretto, A. R. Borges, A. T. Duarte and M. G. R. Vale, Microchem. J., 2016, 128, 89–94.
80. M. Lagerstrom, J. Strand, B. Eklund and E. Ytreberg, Environ. Pollut., 2017, 220, 1333–1341.
81. T. Pluhacek, J. Hanzal, J. Hendrych and D. Milde, Pharmazie, 2016, 71(4), 177–180.
82. M. H. Gonzalez, C. S. da Silva, C. D. B. Amaral, S. R. Bianchi, L. H. B. de Oliveira, J. S. Coelho, A. Oliveira and A. R. A. Nogueira, J. Braz. Chem. Soc., 2017, 28(1), 98–105.
83. N. M. Hepp and I. C. James, X-Ray Spectrom., 2016, 45(6), 330–338.
84. N. Altunay and R. Gurkan, Turk. J. Chem., 2016, 40(6), 988–1001.
85. C. A. Hess, P. Olmedo, A. Navas-Acien, W. Goessler, J. E. Cohen and A. M. Rule, Environ. Res., 2017, 152, 221–225.
86. D. Holloway, P. Campo, K. Nahan, A. Donnell, J. A. Landero, J. A. Caruso and A. P. Vonderheide, Anal. Lett., 2016, 49(15), 2490–2500.
87. A. Figueiredo, T. Fernandes, I. M. Costa, L. Goncalves and J. Brito, J. Pharm. Biomed. Anal., 2016, 122, 52–58.
88. R. Corzo, K. Subedi, T. Trejos and J. R. Almirall, J. Forensic Sci., 2016, 61(3), 725–734.
89. A. K. Myakalwar, S. K. Anubham, S. K. Paidi, I. Barman and M. K. Gundawar, Analyst, 2016, 141(10), 3077–3083.
90. J. Lee, Y. S. Nam, J. Min, K. B. Lee and Y. Lee, J. Forensic Sci., 2016, 61(3), 815–822.
91. J. J. Melendez-Perez, D. N. Correa, V. V. Hernandes, D. R. de Morais, R. B. de Oliveira, W. de Souza, J. M. Santos and M. N. Eberlin, Appl. Spectrosc., 2016, 70(11), 1910–1915.
92. K. Janssens, G. Van der Snickt, F. Vanmeert, S. Legrand, G. Nuyts, M. Alfeld, L. Monico, W. Anaf, W. De Nolf, M. Vermeulen, J. Verbeeck and K. De Wael, Top. Curr. Chem., 2016, 374(6), 52.
93. B. Brunetti, C. Miliani, F. Rosi, B. Doherty, L. Monico, A. Romani and A. Sgamellotti, Top. Curr. Chem., 2016, 374(1), 35.
94. O. H. Barbu and A. Zahariade, Color Res. Appl., 2016, 41(3), 321–324.
95. B. Mindur, A. Sikorska and W. Dabrowski, Acta Phys. Pol., B, 2016, 47(2), 335–340.
96. K. Nakano, A. Tabe, S. Shimoyama and K. Tsuji, Microchem. J., 2016, 126, 496–500.
97. D. Thurrowgood, D. Paterson, M. D. de Jonge, R. Kirkham, S. Thurrowgood and D. L. Howard, Sci. Rep., 2016, 6, 10.
98. O. Otlowska, M. Slebioda, M. Wachowiak and M. Sliwka-Kaszynska, Anal. Methods, 2017, 9(1), 94–102.
99. G. Abdel-Maksoud, Y. M. Issa and M. Magdy, Measurement, 2016, 91, 210–220.
100. A. G. Nord, K. Tronner, K. Billstrom and B. S. Zerpe, J. Cult. Herit., 2017, 23, 162–169.
101. D. Cristea-Stan, B. Constantinescu, C. Chiojdeanu and C. A. Simion, Rom. J. Phys., 2017, 62(1–2), 7.
102. S. X. Serafima, O. G. Duliu, M. M. Manea and G. Niculescu, Rom. Rep. Phys., 2016, 68(1), 191–202.
103. K. Janssens, G. Van der Snickt, M. Alfeld, P. Noble, A. van Loon, J. Delaney, D. Conover, J. Zeibel and J. Dik, Microchem. J., 2016, 126, 515–523.
104. I. Reiche, M. Eveno, K. Muller, T. Calligaro, L. Pichon, E. Laval, E. Mysak and B. Mottin, Appl. Phys. A: Mater. Sci. Process., 2016, 122(11), 13.
105. M. A. Garrote, M. D. Robador and J. L. Perez-Rodriguez, Spectrochim. Acta, Part A, 2017, 173, 279–291.
106. P. Baraldi, S. Bracci, E. Cristoferi, S. Fiorentino, M. Vandini and E. Venturi, J. Cult. Herit., 2016, 21, 802–808.
107. J. G. Wang, S. C. Hao, W. H. Zhou, X. K. Qi and J. L. Shi, J. Nanosci. Nanotechnol., 2016, 16(4), 3583–3586.
108. M. Kim, J. Lee, J. M. Doh, H. Ahn, H. D. Kim, Y. M. Yang and Y. Lee, Surf. Interface Anal., 2016, 48(7), 409–414.
109. M. Eveno, E. Mysak, K. Muller, G. Bastian, N. Pincas and I. Reiche, Stud. Conserv., 2016, 61(2), 102–112.
110. M. Bakiler, B. Kirmizi, O. O. Ozturk, O. B. Hanyali, E. Dag, E. Caglar and G. Koroglu, Microchem. J., 2016, 126, 263–273.
111. R. Debastiani, R. Simon, D. Batchelor, G. Dellagustin, T. Baumbach and M. Fiederle, Microchem. J., 2016, 126, 438–445.
112. I. Garofano, J. L. Perez-Rodriguez, M. D. Robador and A. Duran, J. Cult. Herit., 2016, 22, 1028–1039.
113. D. Magrini, S. Bracci, E. Cantisani, C. Conti, A. Rava, A. Sansonetti, W. Shank and M. Colombini, Spectrochim. Acta, Part A, 2017, 173, 39–45.
114. K. F. Gebremariam, L. Kvittingen and F. G. Banica, Archaeometry, 2016, 58(2), 271–283.
115. M. D. Robador, L. De Viguerie, J. L. Perez-Rodriguez, H. Rousseliere, P. Walter and J. Castaing, Archaeometry, 2016, 58(2), 255–270.
116. B. A. Schmidt, M. A. Ziemann, S. Pentzien, T. Gabsch, W. Koch and J. Kruger, Stud. Conserv., 2016, 61(2), 113–122.
117. M. Qasim, M. Anwar-ul-Haq, M. S. Afgan, M. A. Kalyar and M. A. Baig, Anal. Lett., 2016, 49(13), 2108–2118.
118. Y. Lee, S. H. Nam, K. S. Ham, J. Gonzalez, D. Oropeza, D. Quarles, J. Yoo and R. E. Russo, Spectrochim. Acta, Part B, 2016, 118, 102–111.
119. L. J. Zheng, S. Niu, A. Q. Khan, S. Yuan, J. Yu and H. P. Zeng, Spectrochim. Acta, Part B, 2016, 118, 66–71.
120. I. M. Hwang, J. S. Yang, S. H. Kim, N. Jamila, N. Khan, K. S. Kim and H. Y. Seo, Anal. Lett., 2016, 49(17), 2807–2821.
121. O. I. Yurchenko, O. S. Kalinenko, A. N. Baklanov, E. A. Belov and L. V. Baklanova, J. Appl. Spectrosc., 2016, 83(1), 105–110.
122. N. I. Petrova, O. V. Lundovskaya and A. I. Saprykin, Inorg. Mater., 2016, 52(10), 1020–1024.
123. N. N. Khomichenko, A. V. Shaverina, A. R. Tsygankova and A. I. Saprykin, Inorg. Mater., 2016, 52(14), 1405–1412.
124. S. C. Wu, X. C. Zeng, X. F. Dai, Y. P. Hu, G. Li and C. J. Zheng, Spectrochim. Acta, Part B, 2016, 123, 129–133.
125. E. S. Koshel, V. B. Baranovskaya and T. Y. Gubanova, Inorg. Mater., 2016, 52(14), 1449–1454.
126. A. Virgilio, D. Schiavo, L. M. Costa, J. A. Nobrega, B. T. Jones and G. L. Donati, Talanta, 2016, 161, 826–829.
127. V. Azzi, V. Kazpard, B. Lartiges, A. Kobeissi, A. Kanso and A. G. El Samrani, Clean: Soil, Air, Water, 2017, 45(1), 9.
128. R. C. Machado, A. Virgilio, C. D. B. Amaral, D. Schiavo, J. A. Nobrega and A. R. A. Nogueira, J. Braz. Chem. Soc., 2016, 27(11), 2076–2081.
129. S. O. Souza, D. V. L. Avila, A. S. C. Monteiro, C. A. B. Garcia, J. P. H. Alves, T. A. Maranhao and R. G. O. Araujo, J. Braz. Chem. Soc., 2016, 27(4), 799–806.
130. T. C. de Oliveira, R. T. Rampazzo, M. F. Ferrao and D. Pozebon, J. Braz. Chem. Soc., 2016, 27(11), 2021–2033.
131. D. F. Andrade and E. R. Pereira, J. Agric. Food Chem., 2016, 64(41), 7890–7898.
132. N. Laczai, L. Kovacs, A. Peter and L. Bencs, Spectrochim. Acta, Part B, 2016, 117, 8–15.
133. G. Dravecz, N. Laczai, I. Hajdara and L. Bencs, Spectrochim. Acta, Part B, 2016, 126, 1–5.
134. A. Ali, M. Z. Khan, I. Rehan, K. Rehan and R. Muhammad, J. Spectrosc., 2016, 2016, 1835027.
135. G. E. Belyaev, A. A. Vasin, A. V. Lisitsyn, A. M. Frolov and M. A. Sheindlin, High Temp., 2017, 55(1), 57–62.
136. J. Helsper, R. J. B. Peters, M. E. M. van Bemmel, Z. E. H. Rivera, S. Wagner, F. von der Kammer, P. C. Tromp, T. Hofmann and S. Weigel, Anal. Bioanal. Chem., 2016, 408(24), 6679–6691.
137. L. Sanchez-Garcia, E. Bolea, F. Laborda, C. Cubel, P. Ferrer, D. Gianolio, I. da Silva and J. R. Castillo, J. Chromatogr. A, 2016, 1438, 205–215.
138. J. Soto-Alvaredo, F. Dutschke, J. Bettmer, M. Montes-Bayon, D. Profrock and A. Prange, J. Anal. At. Spectrom., 2016, 31(7), 1549–1555.
139. E. V. Alonso, M. M. L. Guerrero, M. T. S. Cordero, J. M. C. Pavon and A. G. de Torres, J. Anal. At. Spectrom., 2016, 31(12), 2391–2398.
140. A. Ghorbani-Choghamarani, B. Tahmasbi and P. Moradi, RSC Adv., 2016, 6(49), 43205–43216.
141. B. Tahmasbi and A. Ghorbani-Choghamarani, Catal. Lett., 2017, 147(3), 649–662.
142. R. K. Rai, K. Gupta, D. Tyagi, A. Mahata, S. Behrens, X. C. Yang, Q. Xu, B. Pathak and S. K. Singh, Catal. Sci. Technol., 2016, 6(14), 5567–5579.
143. I. Bucsi, A. Mastalir, A. Molnar, K. L. Juhasz and A. Kunfi, Struct. Chem., 2017, 28(2), 501–509.
144. R. Bhaskar, H. Joshi, A. K. Sharma and A. K. Singh, ACS Appl. Mater. Interfaces, 2017, 9(3), 2223–2231.
145. F. Schroder, U. K. Sharma, M. Mertens, F. Devred, D. P. Debecker, R. Luque and E. V. Van der Eycken, ACS Catal., 2016, 6(12), 8156–8161.
146. M. I. Dominguez, B. Cojocaru, M. Tudorache, J. A. Odriozola, M. A. Centeno and V. I. Parvulescu, C. R. Chim., 2016, 19(10), 1156–1165.
147. R. M. A. Saboya, J. A. Cecilia, C. Garcia-Sancho, F. M. T. Luna, E. Rodriguez-Castellon and C. L. Cavalcante, Appl. Clay Sci., 2016, 124, 69–78.
148. J. Bok-Badura, A. Jakobik-Kolon, A. Lamacz and K. Karon, J. Chem., 2017, 2017, 4954080.
149. J. J. Velasco-Velez, V. Pfeifer, M. Havecker, R. Wang, A. Centeno, A. Zurutuza, G. Algara-Siller, E. Stotz, K. Skorupska, D. Teschner, P. Kube, P. Braeuninger-Weimer, S. Hofmann, R. Schlogl and A. Knop-Gericke, Rev. Sci. Instrum., 2016, 87(5), 7.
150. Z. X. Feng, W. T. Hong, D. D. Fong, Y. L. Lee, Y. Yacoby, D. Morgan and Y. Shao-Horn, Acc. Chem. Res., 2016, 49(5), 966–973.
151. S. Kato, S. K. Matam, P. Kerger, L. Bernard, C. Battaglia, D. Vogel, M. Rohwerder and A. Zuttel, Angew. Chem., Int. Ed., 2016, 55(20), 6028–6032.
152. M. Gerstl, A. Nenning, R. Iskandar, V. Rojek-Wockner, M. Bram, H. Hutter and A. K. Opitz, Materials, 2016, 9(8), 23.
153. D. Barreca, G. Carraro, A. Gasparotto, C. Maccato, C. Sada, E. Bontempi, M. Brisotto, O. Pliekhova and U. L. Stangar, Environ. Sci. Pollut. Res., 2016, 23(20), 20350–20359.
154. E. V. Salomatina, A. S. Loginova, S. K. Ignatov, A. V. Knyazev, I. V. Spirina and L. A. Smirnova, J. Inorg. Organomet. Polym. Mater., 2016, 26(6), 1280–1291.
155. D. K. Nandi, J. Manna, A. Dhara, P. Sharma and S. K. Sarkar, J. Vac. Sci. Technol., A, 2016, 34(1), 8.
156. H. K. Chen, W. F. Chen, P. Koshy, E. Adabifiroozjaei, R. Liu, L. R. Sheppard and C. C. Sorrell, J. Taiwan Inst. Chem. Eng., 2016, 67, 202–210.
157. Q. He, J. Wang, Y. X. Mo, C. Wei, X. Fang, Z. Xing, S. C. Zhang and X. R. Zhang, Talanta, 2017, 165, 39–43.
158. E. Mossini, E. Macerata, M. Giola and M. Mariani, Nukleonika, 2015, 60(3), 597–602.
159. S. C. Paul, G. van Zijl, A. J. Babafemi and M. J. Tan, Constr. Build. Mater., 2016, 114, 232–240.
160. M. Bonta, A. Eitzenberger, S. Burtscher and A. Limbeck, Cem. Concr. Res., 2016, 86, 78–84.
161. A. S. Rother, T. Dietz, P. Kohns and G. Ankerhold, Tech. Mess., 2017, 84(1), 23–31.
162. F. Caruso, S. Mantellato, M. Palacios and R. J. Flatt, Cem. Concr. Res., 2017, 91, 52–60.
163. A. Heisig, L. Urbonas, R. E. Beddoe and D. Heinz, Mater. Struct., 2016, 49(10), 4291–4303.
164. R. Guo, L. Zhang, J. J. Fan, X. J. Zhang and Y. Li, Spectrosc. Spectrum Anal., 2016, 36(7), 2249–2254.
165. H. L. Yin, Z. Y. Hou, L. Zhang, X. J. Zhang, Z. Wang and Z. Li, J. Anal. At. Spectrom., 2016, 31(12), 2384–2390.
166. R. Hernandez-Garcia, M. E. Villanueva-Tagle, F. Calderon-Pinar, M. D. Durruthy-Rodriguez, F. W. B. Aquino, E. R. Pereira and M. S. Pomares-Alfonso, Microchem. J., 2017, 130, 21–26.
167. C. Schiavo, L. Menichetti, E. Grifoni, S. Legnaioli, G. Lorenzetti, F. Poggialini, S. Pagnotta and V. Palleschi, J. Instrum., 2016, 11, 9.
168. L. Fantuzzi, M. A. C. Ontiveros and X. Aquilue, Archaeometry, 2016, 58, 1–22.
169. S. Baklouti, L. Maritan, L. Casas, N. L. Ouazaa, R. Jarrega, M. Prevosti, C. Mazzoli, B. Fouzai, S. L. Kassaa and M. Fantar, Appl. Clay Sci., 2016, 132, 140–154.
170. L. E. Karran and B. J. Colston, J. Cult. Herit., 2016, 18, 290–298.
171. G. Ricci, L. Caneve, D. Pedron, N. Holesch and E. Zendri, Microchem. J., 2016, 126, 104–112.
172. Y. Q. Li, Y. M. Yang, J. Zhu, X. G. Zhang, S. Jiang, Z. X. Zhang, Z. Q. Yao and G. Solbrekken, Ceram. Int., 2016, 42(7), 8495–8500.
173. V. M. Ferreras, J. M. G. Esparraguera, A. Hein, S. Pidaev, E. V. Rtveladze and S. B. Bolelov, Archaeometry, 2016, 58(5), 736–764.
174. B. G. Oztoprak, M. A. Sinmaz and F. Tulek, Appl. Phys. A: Mater. Sci. Process., 2016, 122(5), 11.
175. L. Li, Y. Huang, H. Y. Sun, L. T. Yan, S. L. Feng, Q. Xu and X. Q. Feng, Nucl. Instrum. Methods Phys. Res., Sect. B, 2016, 381, 52–57.
176. F. Turco, P. Davit, C. Maritano, L. Operti, G. Fenoglio and A. Agostino, Archaeometry, 2016, 58(5), 765–778.
177. P. Pince, B. Vekemans, P. Vandenabeele, E. Haerinck and B. Overlaet, Spectrochim. Acta, Part B, 2016, 123, 150–156.
178. M. Tabata and M. Nakano, Bunseki Kagaku, 2016, 65(11), 657–666.
179. L. T. Yi, Z. G. Liu, K. Wang, X. Lin, M. Chen, S. Q. Peng, K. Yang and J. B. Wang, Appl. Phys. A: Mater. Sci. Process., 2016, 122(4), 7.
180. T. Wang, T. Q. Zhu, Z. Y. Feng, B. Fayard, E. Pouyet, M. Cotte, W. De Nolf, M. Salome and P. Sciau, Anal. Chim. Acta, 2016, 928, 20–31.
181. S. Rella, G. E. De Benedetto, I. Marchetta and C. Malitesta, J. Cult. Herit., 2016, 17, 194–197.
182. T. Q. Zhu, Y. C. Zhang, H. Xiong, Z. Y. Feng, Q. Li and B. L. Cao, Archaeometry, 2016, 58(6), 966–978.
183. Y. B. Zhu, Anal. Sci., 2016, 32(11), 1237–1243.
184. P. Devangad, V. K. Unnikrishnan, M. M. Tamboli, K. M. M. Shameem, R. Nayak, K. S. Choudhari and C. Santhosh, Anal. Methods, 2016, 8(39), 7177–7184.
185. J. M. Li, Y. B. Chu, N. Zhao, R. Zhou, R. X. Yi, L. B. Guo, J. Y. Li, X. Y. Li, X. Y. Zeng and Y. F. Lu, Chin. J. Anal. Chem., 2016, 44(7), 1042–1046.
186. Y. Wang, A. M. Chen, Y. F. Jiang, L. Z. Sui, X. W. Wang, D. Zhang, D. Tian, S. Y. Li and M. X. Jin, Phys. Plasmas, 2017, 24(1), 8.
187. M. Jarosova, D. Walaszek, B. Wagner, D. Milde and E. Bulska, J. Anal. At. Spectrom., 2016, 31(10), 2089–2093.
188. J. I. Kimura, Q. Chang, N. Kanazawa, S. Sasaki and B. S. Vaglarov, J. Anal. At. Spectrom., 2016, 31(3), 790–800.
189. V. S. Bronsky and S. N. Shilobreeva, J. Anal. Chem., 2016, 71(10), 1033–1040.
190. A. A. I. Khalil and M. A. Morsy, Talanta, 2016, 154, 109–118.
191. M. E. Regier, R. L. Hervig, M. L. Myers, K. Roggensack and C. J. N. Wilson, Chem. Geol., 2016, 447, 27–39.
192. M. Emami, S. Nekouei, H. Ahmadi, C. Pritzel and R. Trettin, Int. J. Appl. Glass Sci., 2016, 7(1), 59–68.
193. Y. M. Li, W. Stone, E. H. Schemitsch, P. Zalzal, M. Papini, S. D. Waldman and M. R. Towler, J. Biomater. Appl., 2016, 31(5), 674–683.
194. S. W. Lee, J. S. Ryu, J. S. Min, M. Y. Choi, K. S. Lee and W. J. Shin, Rapid Commun. Mass Spectrom., 2016, 30(13), 1612–1618.
195. J. S. Fernandes, P. Gentile, R. Moorehead, A. Crawford, C. A. Miller, R. A. Pires, P. V. Hatton and R. L. Reis, Cryst. Growth Des., 2016, 16(7), 3731–3740.
196. A. K. Carter, L. Dussubieux and N. Beavan, Archaeometry, 2016, 58(3), 401–412.
197. R. Wen, Z. Q. Zhao, J. Ma and J. X. Wang, Spectrosc. Spectrum Anal., 2016, 36(9), 2961–2965.
198. I. M. N. Ribeiro, R. P. Freitas, C. Calza, A. L. C. Oliveira, V. S. Felix, D. S. Ferreira, R. T. Batista, E. A. S. Goncalves, M. O. Pereira, P. C. L. Brito, T. A. Lima, A. R. Pimenta, M. J. Anjos and R. T. Lopes, Vib. Spectrosc., 2016, 87, 111–115.
199. R. Bugoi, I. Poll, G. Manucu-Adamesteanu, T. Calligaro, L. Pichon and C. Pacheco, J. Radioanal. Nucl. Chem., 2016, 307(2), 1021–1036.
200. M. Phelps, I. C. Freestone, Y. Gorin-Rosen and B. Gratuze, J. Archaeol. Sci., 2016, 75, 57–71.
201. E. Gliozzo, B. Lepri, L. Sagui and I. T. Memmi, Archaeol. Anthrop. Sci., 2017, 9(2), 165–180.
202. E. Gliozzo, M. Turchiano, F. Giannetti and A. S. Barbone, Archaeometry, 2016, 58, 81–112.
203. E. Gliozzo, M. Turchiano, F. Giannetti and I. Memmi, Archaeometry, 2016, 58, 113–147.
204. N. T. Shardakov, A. E. Shavkunova and V. V. Stepanovskikh, Glass Phys. Chem., 2017, 43(1), 23–27.
205. N. Topic, I. B. Radovic, S. Fazinic and Z. Skoko, Archaeometry, 2016, 58(4), 574–592.
206. J. Henderson, S. Chenery, E. Faber and J. Kroger, Microchem. J., 2016, 128, 134–152.
207. I. Ortega-Feliu, B. Gomez-Tubio, M. A. Respaldiza, F. Capel and X. Nieto-Prieto, J. Anal. At. Spectrom., 2016, 31(3), 773–779.
208. M. C. Caggiani, A. Vlangone, F. Mastrorocco, C. Taccogna, R. Laviano and L. C. Giannossa, Microchem. J., 2017, 131, 21–30.
209. I. Coutinho, F. Berger, A. Rodrigues, M. L. Coutinho, A. Machado, L. C. Alves and M. Vilarigues, X-Ray Spectrom., 2016, 45(6), 308–317.
210. L. Van Wersch, C. Loisel, F. Mathis, D. Strivay and S. Bully, Archaeometry, 2016, 58(6), 930–946.
211. T. Pradell, G. Molina, S. Murcia, R. Ibanez, C. Liu, J. Molera and A. J. Shortland, Int. J. Appl. Glass Sci., 2016, 7(1), 41–58.
212. S. Almaviva, L. Caneve, F. Colao and G. Maddaluno, Phys. Scr., 2016, 91(4), 7.
213. G. S. Maurya, R. Kumar, A. Kumar and A. K. Rai, Spectrochim. Acta, Part B, 2016, 126, 17–22.
214. M. Pribula, J. Kristof, M. Suchonova, M. Hornackova, J. Plavcan, A. Hakola and P. Veis, Phys. Scr., 2016, T167, 5.
215. B. Lopez-Miranda, B. Zurro, A. Baciero and M. Martinez, Rev. Sci. Instrum., 2016, 87(11), 4.
216. E. E. Mukhin, P. Andrew, A. D. Anthoine, A. N. Bazhenov, R. Barnsley, I. M. Bukreev, V. L. Bukhovets, A. P. Chernakov, A. E. Gorodetsky, M. M. Kochergin, A. N. Koval, A. B. Kukushkin, A. S. Kukushkin, G. S. Kurskiev, M. G. Levashova, A. E. Litvinov, V. N. Litunovsky, A. V. Markin, I. V. Mazul, S. V. Masyukevich, I. V. Miroshnikov, A. S. Nemov, A. N. Novokhatsky, A. G. Razdobarin, E. V. Sherstnev, D. S. Samsonov, V. V. Semenov, A. S. Smirnov, G. De Temmerman, S. Y. Tolstyakov, R. K. Zalavutdinov and M. J. Walsh, Nucl. Fusion, 2016, 56(3), 10.
217. P. J. Skrodzki, J. R. Becker, P. K. Diwakar, S. S. Harilal and A. Hassanein, Appl. Spectrosc., 2016, 70(3), 467–473.
218. H. Bergsaker, I. Bykov, Y. Zhou, P. Petersson, G. Possnert, J. Likonen, J. Pettersson, S. Koivuranta, A. M. Widdowson and J. E. T. Contributors, Phys. Scr., 2016, T167, 8.
219. I. Bykov, H. Bergsaker, G. Possnert, Y. Zhou, K. Heinola, J. Pettersson, S. Conroy, J. Likonen, P. Petersson, A. Widdowson and J. E. T. Contributors, Nucl. Instrum. Methods Phys. Res., Sect. B, 2016, 371, 370–375.
220. A. Semerok, D. L’Hermite, J. M. Weulersse, J. L. Lacour, C. Cheymol, M. Kempenaars, N. Bekris and C. Grisolia, Spectrochim. Acta, Part B, 2016, 123, 121–128.
221. X. N. Lu, J. J. Neeway, J. V. Ryan and J. C. Du, J. Non-Cryst. Solids, 2016, 452, 161–168.
222. P. S. Somayajulu, A. Sengupta, A. K. Karande, R. Malav, D. K. Das and M. Afzal, J. Radioanal. Nucl. Chem., 2016, 308(2), 495–503.
223. M. Bavio, M. Fernandez, E. Zubillaga, R. Servant and E. Gautier, Microchem. J., 2016, 128, 320–324.
224. A. Saha, S. B. Deb and M. K. Saxena, J. Anal. At. Spectrom., 2016, 31(7), 1480–1489.
225. J. Zhang, Z. Qin, D. C. Deng, J. S. Liao, X. J. Wei and N. Zhang, J. Anal. At. Spectrom., 2016, 31(4), 934–939.
226. D. P. Montoya, B. T. Manard and N. Xu, J. Radioanal. Nucl. Chem., 2016, 307(3), 2009–2014.
227. N. Xu, D. Gallimore, E. Lujan, K. Garduno, L. Walker, F. Taylor, P. Thompson and L. Tandon, J. Radioanal. Nucl. Chem., 2016, 307(2), 1203–1213.
228. A. Sengupta, T. Ippili, S. Jayabun, M. Singh and S. K. Thulasidas, J. Radioanal. Nucl. Chem., 2016, 310(1), 59–67.
229. V. C. Adya, A. Sengupta, S. K. Jayabun and S. K. Thulasidas, J. Radioanal. Nucl. Chem., 2016, 308(2), 765–772.
230. V. C. Adya, A. Sengupta, S. K. Thulasidas and V. Natarajan, J. Radioanal. Nucl. Chem., 2016, 307(2), 1489–1497.
231. L. M. Carter, J. D. Brockman, S. K. Loyalka and J. D. Robertson, J. Radioanal. Nucl. Chem., 2016, 307(3), 1771–1775.
232. R. Plukiene, A. Plukis, A. Puzas, R. Gvozdaite, V. Barkauskas, G. Duskesas, J. V. Cizdziel, D. Bussan and V. Remeikis, Nucl. Eng. Des., 2016, 300, 530–535.
233. I. W. Croudace, P. E. Warwick, D. G. Reading and B. C. Russell, TrAC, Trends Anal. Chem., 2016, 85, 120–129.
234. P. Nagy, N. Vajda, T. Pinter and K. Fel, J. Radioanal. Nucl. Chem., 2016, 307(2), 1045–1053.
235. J. L. Steeb, C. J. Mertz, M. R. Finck, G. Engelstad, K. P. Carney and D. B. Chamberlain, J. Radioanal. Nucl. Chem., 2016, 307(1), 751–760.
236. A. M. Gaffney, A. Hubert, W. S. Kinman, M. Magara, A. Okubo, F. Pointurier, K. C. Schorzman, R. E. Steiner and R. W. Williams, J. Radioanal. Nucl. Chem., 2016, 307(3), 2055–2060.
237. T. M. Kayzar and R. W. Williams, J. Radioanal. Nucl. Chem., 2016, 307(3), 2061–2068.
238. Z. Varga, A. Nicholl, M. Wallenius and K. Mayer, J. Radioanal. Nucl. Chem., 2016, 307(3), 1919–1926.
239. S. A. Stratz, S. A. Jones, C. J. Oldham, A. D. Mullen, A. V. Jones, J. D. Auxier and H. L. Hall, J. Radioanal. Nucl. Chem., 2016, 310(3), 1273–1276.
240. W. Wang, Z. M. Li, J. Xu, G. Q. Zhou, L. H. Zhai, M. Li, Y. Y. Su and G. Y. Wei, Chin. J. Anal. Chem., 2016, 44(7), 1053–1058.
241. J. G. Tarolli, B. E. Naes, B. J. Garcia, A. E. Fischer and D. Willingham, J. Anal. At. Spectrom., 2016, 31(7), 1472–1479.
242. A. J. Fahey, E. E. Groopman, K. S. Grabowski and K. C. Fazel, Anal. Chem., 2016, 88(14), 7145–7153.
243. J. Park, T. H. Kim, C. G. Lee, J. Lee, S. H. Lim, S. H. Han and K. Song, J. Radioanal. Nucl. Chem., 2017, 311(2), 1535–1544.
244. B. H. Isselhardt, S. G. Prussin, M. R. Savina, D. G. Willingham, K. B. Knight and I. D. Hutcheon, J. Anal. At. Spectrom., 2016, 31(3), 666–678.
245. S. F. Boulyga, A. Koepf, S. Konegger-Kappel, Z. Macsik and G. Stadelmann, J. Anal. At. Spectrom., 2016, 31(11), 2272–2284.
246. A. D. Pollington, W. S. Kinman, S. K. Hanson and R. E. Steiner, J. Radioanal. Nucl. Chem., 2016, 307(3), 2109–2115.
247. K. J. Mathew and A. Haoszbek, J. Radioanal. Nucl. Chem., 2016, 307(3), 1681–1687.
248. F. Esaka, K. Yasuda, D. Suzuki, Y. Miyamoto and M. Magara, Talanta, 2017, 165, 122–127.
249. B. H. Isselhardt, M. R. Savina, A. Kucher, S. D. Gates, K. B. Knight and I. D. Hutcheon, J. Radioanal. Nucl. Chem., 2016, 307(3), 2487–2494.
250. S. K. Hanson, A. D. Pollington, C. R. Waidmann, W. S. Kinman, A. M. Wende, J. L. Miller, J. A. Berger, W. J. Oldham and H. D. Selby, Proc. Natl. Acad. Sci. U. S. A., 2016, 113(29), 8104–8108.
251. S. Dulanska, B. Horvathova, B. Remenec and L. Matel, J. Radioanal. Nucl. Chem., 2016, 310(2), 645–650.
252. S. Asai, T. Yomogida, M. Saeki, H. Ohba, Y. Hanzawa, T. Horita and Y. Kitatsuji, Anal. Chem., 2016, 88(24), 12227–12233.
253. H. Isnard, A. Nonell, M. Marie and F. Chartier, Radiochim. Acta, 2016, 104(2), 131–139.
254. S. K. Aggarwal, Radiochim. Acta, 2016, 104(7), 445–455.
255. K. J. Mathew, F. E. Stanley, M. R. Thomas, K. J. Spencer, L. P. Colletti and L. Tandon, Anal. Methods, 2016, 8(40), 7289–7305.
256. M. V. Penkin, M. A. Humphrey, A. A. Kryzhanovsky, V. N. Vyachin and A. Iyengar, J. Radioanal. Nucl. Chem., 2016, 307(3), 2091–2094.
257. M. Liezers, O. T. Farmer, M. L. Thomas, G. J. Hager and G. C. Eiden, J. Radioanal. Nucl. Chem., 2016, 307(3), 1795–1800.
258. S. Baghdadi, G. Cote, C. Beyaert, B. Mane, A. Peroux, L. Masclet, F. Rebiere, M. Agarande and C. Bouvier-Capely, J. Anal. At. Spectrom., 2016, 31(7), 1406–1413.
259. W. D. Locklair, J. M. Mannion, S. M. Husson and B. A. Powell, J. Radioanal. Nucl. Chem., 2016, 307(3), 2333–2338.
260. S. Asai, Y. Hanzawa, M. Konda, D. Suzuki, M. Magara, T. Kimura, R. Ishihara, K. Saito, S. Yamada and H. Hirota, Anal. Chem., 2016, 88(6), 3149–3155.
261. K. Campbell, A. Unger, W. Kerlin, T. Hartmann, J. Bertoia, E. Judge, M. Dirmyer and K. Czerwinski, J. Radioanal. Nucl. Chem., 2016, 310(2), 533–540.
262. J. Krajko, Z. Varga, M. Wallenius and K. Mayer, Radiochim. Acta, 2016, 104(7), 471–479.
263. R. Sajimol, S. Bera, S. Nalini, N. Sivaraman, M. Joseph and T. Kumar, J. Radioanal. Nucl. Chem., 2016, 309(2), 563–573.
264. M. N. Drozdov, Y. N. Drozdov, A. V. Novikov, P. A. Yunin and D. V. Yurasov, Tech. Phys. Lett., 2016, 42(3), 243–247.
265. M. N. Drozdov, Y. N. Drozdov, A. Csik, A. V. Novikov, K. Vad, P. A. Yunin, D. V. Yurasov, S. F. Belykh, G. P. Gololobov, D. V. Suvorov and A. Tolstogouzov, Thin Solid Films, 2016, 607, 25–31.
266. M. Hashiguchi, I. Sakaguchi, Y. Adachi and N. Ohashi, Jpn. J. Appl. Phys., 2016, 55(10), 6.
267. B. Gong and C. E. Marjo, Surf. Interface Anal., 2016, 48(7), 422–427.
268. R. J. H. Morris, T. P. A. Hase, A. M. Sanchez and G. Rowlands, J. Am. Soc. Mass Spectrom., 2016, 27(10), 1694–1702.
269. Y. Liu, Y. Tong, S. Y. Li, Y. Wang, A. M. Chen and M. X. Jin, Chin. Opt. Lett., 2016, 14(12), 5.
270. J. H. Choi, Y. Moon, S. H. Lee, J. H. In and S. Jeong, Int. J. Precis. Eng. Manuf., 2016, 3(2), 167–171.
271. D. Mao, G. Blatz, C. E. Wickersham and M. Gloeckler, Sol. Energy Mater. Sol. Cells, 2016, 157, 65–73.
272. B. Fernandez, L. Lobo, N. Reininghaus, R. Pereiro and A. Sanz-Medel, Talanta, 2017, 165, 289–296.
273. A. Yamazaki, Y. Orikasa, K. Chen, Y. Uchimoto, T. Kamiya, M. Koka, T. Satoh, K. Mima, Y. Kato and K. Fujita, Nucl. Instrum. Methods Phys. Res., Sect. B, 2016, 371, 298–302.
274. D. N. Wang and L. C. Zuin, J. Power Sources, 2017, 337, 100–109.
275. S. Oswald, M. Hoffmann and M. Zier, Appl. Surf. Sci., 2017, 401, 408–413.
276. Y. Shilina, B. Ziv, A. Meir, A. Banerjee, S. Ruthstein, S. Luski, D. Aurbach and I. C. Halalay, Anal. Chem., 2016, 88(8), 4440–4447.
277. J. Kumm, H. Samadi, R. V. Chacko, P. Hartmann and A. Wolf, J. Appl. Phys., 2016, 120(2), 9.
278. S. K. Lim, D. K. Kim, H. C. Hwang, J. S. Kim, W. S. Park, Y. D. Cho, C. S. Shin, W. K. Park, J. H. Lee, D. H. Kim and H. D. Lee, ECS J. Solid State Sci. Technol., 2016, 5(6), P349–P352.
279. C. Noel and L. Houssiau, J. Am. Soc. Mass Spectrom., 2016, 27(5), 908–916.
280. R. J. H. Morris, Appl. Surf. Sci., 2016, 390, 778–783.
281. Z. Essa, B. Pelletier, P. Morin, P. Boulenc, A. Pakfar, C. Tavernier, F. Wacquant, C. Zechner, M. Juhel, J. L. Autran and F. Cristiano, Solid-State Electron., 2016, 126, 163–169.
282. J. Seto, K. Ohdaira and H. Matsumura, Jpn. J. Appl. Phys., 2016, 55(4), 4.
283. D. G. Filatova, V. V. Es’kina, A. S. Chizhov, M. N. Rumyantseva, P. A. Shaposhnik, V. B. Baranovskaya and Y. A. Karpov, J. Anal. Chem., 2016, 71(5), 496–499.
284. Y. Q. Luo, S. Gamliel, S. Nijem, S. Aharon, M. Holt, B. Stripe, V. Rose, M. I. Bertoni, L. Etgar and D. P. Fenning, Chem. Mater., 2016, 28(18), 6536–6543.
285. J. W. Roberts, P. R. Chalker, K. B. Lee, P. A. Houston, S. J. Cho, I. G. Thayne, I. Guiney, D. Wallis and C. J. Humphreys, Appl. Phys. Lett., 2016, 108(7), 5.
286. P. P. Michalowski, W. Kaszub, A. Merkulov and W. Strupinski, Appl. Phys. Lett., 2016, 109(1), 4.
287. N. Ghanbari, T. Waldmann, M. Kasper, P. Axmann and M. Wohlfahrt-Mehrens, J. Phys. Chem. C, 2016, 120(39), 22225–22234.
288. J. A. Espinoza-Figueroa, V. H. Mendez-Garcia, M. A. Vidal, E. Cruz-Hernandez, M. Lopez-Lopez and S. Gallardo-Hernandez, J. Vac. Sci. Technol., B: Nanotechnol. Microelectron.: Mater., Process., Meas., Phenom., 2016, 34(2), 4.
289. M. A. Olgar, J. Klaer, R. Mainz, S. Levcenco, J. Just, E. Bacaksiz and T. Unold, Thin Solid Films, 2016, 615, 402–408.
290. B. Hala, M. Turkowska, K. Berezecka and E. Strawa, Przem. Chem., 2016, 95(2), 293–296.
291. H. L. Kang, J. B. Lao, Z. P. Li, W. Q. Yao, C. Liu and J. Y. Wang, Appl. Surf. Sci., 2016, 388, 584–588.
292. V. Winkler, G. Kilibarda, S. Schlabach, D. V. Szabo, T. Hanernann and M. Bruns, J. Phys. Chem. C, 2016, 120(43), 24706–24714.
293. M. Evertz, F. Horsthemke, J. Kasnatscheew, M. Borner, M. Winter and S. Nowak, J. Power Sources, 2016, 329, 364–371.
294. V. Savvilotidou, A. Antoniou and E. Gidarakos, Waste Manag., 2017, 59, 394–402.
295. S. Meziani, A. Moussi, L. Mahiou and R. Outemzabet, Mater. Sci., 2016, 34(2), 315–321.
296. D. Shin, J. Kim, T. Gershon, R. Mankad, M. Hopstaken, S. Guha, B. T. Ahn and B. Shin, Sol. Energy Mater. Sol. Cells, 2016, 157, 695–702.
297. M. Kim, J. Lee, H. Kim, K. B. Lee and Y. Lee, J. Nanosci. Nanotechnol., 2016, 16(10), 10748–10752.
298. J. M. Costa-Fernandez, M. Menendez-Miranda, D. Bouzas-Ramos, J. R. Encinar and A. Sanz-Medel, TrAC, Trends Anal. Chem., 2016, 84, 139–148.
299. M. D. Montano, J. W. Olesik, A. G. Barber, K. Challis and J. F. Ranville, Anal. Bioanal. Chem., 2016, 408(19), 5053–5074.
300. I. De la Calle, M. Menta and F. Seby, Spectrochim. Acta, Part B, 2016, 125, 66–96.
301. H. El Hadri, E. J. Petersen and M. R. Winchester, Anal. Bioanal. Chem., 2016, 408(19), 5099–5108.
302. G. Cornelis and S. Rauch, Anal. Bioanal. Chem., 2016, 408(19), 5075–5087.
303. I. Abad-Alvaro, E. Pena-Vazquez, E. Bolea, P. Bermejo-Barrera, J. R. Castillo and F. Laborda, Anal. Bioanal. Chem., 2016, 408(19), 5089–5097.
304. S. Miyashita, H. Mitsuhashi, S. Fujii, A. Takatsu, K. Inagaki and T. Fujimoto, Anal. Bioanal. Chem., 2017, 409(6), 1531–1545.
305. R. P. Lamsal, G. Jerkiewicz and D. Beauchemin, Anal. Chem., 2016, 88(21), 10552–10558.
306. C. A. Sotebier, D. J. Kutscher, L. Rottmann, N. Jakubowski, U. Panne and J. Bettmer, J. Anal. At. Spectrom., 2016, 31(10), 2045–2052.
307. C. A. Sotebier, S. M. Weidner, N. Jakubowski, U. Panne and J. Bettmer, J. Chromatogr. A, 2016, 1468, 102–108.
308. L. Frechette-Viens, M. Hadioui and K. J. Wilkinson, Talanta, 2017, 163, 121–126.
309. M. D. Montano, B. J. Majestic, A. K. Jamting, P. Westerhoff and J. F. Ranville, Anal. Chem., 2016, 88(9), 4733–4741.
310. D. M. Schwertfeger, J. R. Velicogna, A. H. Jesmer, R. P. Scroggins and J. I. Princz, Anal. Chem., 2016, 88(20), 9908–9914.
311. J. Jimenez-Lamana and V. I. Slaveykova, Sci. Total Environ., 2016, 573, 946–953.
312. L. Telgmann, M. T. K. Nguyen, L. Shen, V. Yargeau, H. Hintelmann and C. D. Metcalfe, Anal. Bioanal. Chem., 2016, 408(19), 5169–5177.
313. M. Azodi, Y. Sultan and S. Ghoshal, Environ. Sci. Technol., 2016, 50(24), 13318–13327.
314. H. A. Kim, B. T. Lee, S. Y. Na, K. W. Kim, J. F. Ranville, S. O. Kim, E. Jo and I. C. Eom, Chemosphere, 2017, 171, 468–475.
315. I. L. Hsiao, F. S. Bierkandt, P. Reichardt, A. Luch, Y. J. Huang, N. Jakubowski, J. Tentschert and A. Haase, J. Nanobiotechnol., 2016, 14, 13.
316. R. C. Merrifield, C. Stephan and J. R. Lead, Talanta, 2017, 162, 130–134.
317. M. E. Johnson, A. R. M. Bustos and M. R. Winchester, Anal. Bioanal. Chem., 2016, 408(27), 7629–7640.

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