DOI:
10.1039/C5RA23939G
(Paper)
RSC Adv., 2016,
6, 17567-17573
In-depth investigation on quantitative characterization of pyrolysis oil by 31P NMR†
Received
12th November 2015
, Accepted 27th January 2016
First published on 29th January 2016
Abstract
The characterization of different heteroatom functional groups by employing 31P NMR has been developed for almost 30 years. In this study, an in-depth investigation of this commonly used method has been accomplished for the analysis of pyrolysis oil. Several commonly used internal standards for 31P NMR have been examined by in situ monitoring. The results indicated that endo-N-hydroxy-5-norbornene-2,3-dicarboximide (NHND) is not stable after a long period of storage or experiment (>12 hours), but both cyclohexanol and triphenylphosphine oxide (TPPO) can be used as internal standards if a long experiment or storage is required. The pyrolysis oil has also been investigated by both short time (16 hours) in situ monitoring and long time (14 days) ex situ monitoring. The results showed that aliphatic OH, carboxylic acids and water contents are not very stable after 2 hours, and thus a short time of preparation, storage, and experiment need to be considered to ensure a precise quantitative measurement. The decomposition products are still unclear, but some preliminary investigations for different acids, (e.g. formic acid) have been accomplished. The results indicated that the aromatic carboxylic acids (benzoic acid and vanillic acid) are more stable than formic acid and acetic acid. Interestingly, the formic acid will even decompose to some other compounds at the very beginning of the in situ monitoring test. Further characterization found that water is one of the major products for the decomposition of formic acid in the 31P NMR solution. As far as we know, this is the first report on such time-dependent changes when using 31P NMR to analyze the pyrolysis oil, and these results show that proper application of this method is essential to achieve reliable quantitative data.
1. Introduction
Recently, fuels converted from biomass are increasingly being viewed as promising alternative energy resources due to the growing concerns about increasing global energy consumption and the effects of carbon dioxide emissions from fossil fuels.1 Among the various conversion technologies that are being investigated, thermochemical decomposition processes (such as pyrolysis) have been reported2 as one of the promising means to produce biofuel precursors – namely pyrolysis bio-oil. Due to the complicated composition of pyrolysis oil, subsequent upgrading is necessary,3 and requires quantitative and in-depth characterization of pyrolysis oil. Unfortunately, the complex nature of pyrolysis oil brings huge barriers for traditional analytical methods, such as GC-MS, GPC, HPLC and FT-IR. GC-MS analysis has frequently been used to identify individual components in pyrolysis oil, however, only a small portion of the pyrolysis oil (∼40%) can be detected by GC, mainly due to the poor volatility.4 HPLC would be useful for the identification and quantification of non-volatile compounds that can't be quantified by GC, but as with GC techniques, HPLC is currently only able to quantify a small portion of pyrolysis oil.5 GPC can be a useful technique for determining the molecular weight distributions of pyrolysis oils, but the distributions are not quantitative, and the data can only be used for comparing similar bio-oils.6 Spectroscopic techniques, such as FT-IR could provide insights on whole pyrolysis oil, but its application is limited due to both the dark color and complex composition of bio-oils.7 Therefore, quantitative and precise characterization of whole pyrolysis oil has significant impacts for providing insights on thermochemical decomposition pathways and on evaluating upgrading processes and catalysts.
The characterization of different heteroatom functional groups by employing 31P NMR has been developed for almost 30 years. One of the very first reports about analysis of coal pyrolysis condensates and extracts by 31P NMR was published on Energy & Fuels in 1988 by Verkade's group.8 Five different chlorophospholane based derivatizing reagents (1–5, Fig. 1) have been examined, and various model compounds, including different phenols, alcohols, aliphatic and aromatic carboxylic acids, amines, and thiols have been derivatized with different phosphorus (P) reagents and characterized by 31P NMR. The reactions between different functional groups and P reagents have been summarized in Fig. 2. Detailed assignment ranges for different functional groups have also been proposed in Verkade's research. The mixtures of such derivatives were also tested and the results were shown to be quite stable and reliable. The P reagents 1, 2, and 5 have been proposed to be the suitable reagents for complicated mixtures such as coal pyrolysis condensates and extracts, as they have less overlap between different functional groups. A new P reagent (6, Fig. 1) has been evaluated, and detailed investigations on the quantitative characterization of moisture and phenols in the coal condensate,9 coal extracts,10 and coal liquefaction residues11 have been accomplished as well. The results showed that the developed 31P NMR methods can successfully perform the quantitative measurement for phenols and moisture in complicated mixtures, such as coal liquid products.
 |
| Fig. 1 Phosphorus (P) reagents used in the literature. | |
 |
| Fig. 2 Reactions of the phosphorous reagent (TMDP) with various hydroxyl functional groups and the 31P NMR assignment of phosphitylated compounds. | |
Similar methods have been further explored by several different research groups for the characterization of biomass and biodiesel. For example, Argyropoulos' group published several review12 and research papers on characterization of lignin13,14 cellulose,15,16 and poly(hydroxyalkanoate).17 P reagents 1 and 2 (Fig. 1) have been used and evaluated, and several alcohols, carboxylic acids, and substituted phenols have been phosphitylated18 by these two P reagents. The assignment ranges have been proposed for different phenols, which is very important for further assignments for the characterization of lignin. The results also indicated that 31P NMR provides quantitative information on the contents of different hydroxyl groups in biomass components and in the polymer. Dais' groups investigated various lipid oils, moisture and model compounds by the similar 31P NMR methods.19–22 P reagent 2 (Fig. 1) was used in Dais' research, and cyclohexanol was used as internal standard. The quantitative results can be provided by employing 31P NMR for different fatty acids, glycerol, a mono- and diglyceride composition, and water in the lipid oils. Crestini's group23,24 also employed the same P reagent (P reagent 2 in Fig. 1) and a similar method to explore the chemical structure for tannin. Several model compounds which can represent the hydrolysable tannins, condensed tannins, and catechin tannins have been examined and assignment ranges for tannins have been proposed. The conclusion also indicated that the 31P NMR method is a reliable way to provide quantitative information on the tannin structure. Ragauskas' group developed similar methods as well. The applications for 31P NMR characterization on lignin,25–30 biodiesel,31,32 and pyrolysis oils33–39 have been well established and the results also indicated that this method can be used for quantitative investigations of various hydroxyl groups in different materials, including very complicated biomass components, and their thermochemical decomposition products. Various internal standards have been evaluated for the characterization of lignin.25 There were also several other applications of 31P NMR methods, for example, characterization of liquefaction products from loblolly pine,40 analysis of free hydroxylic and carboxylic groups in unsaturated polyester and alkyd resins,41 chemical derivatization for metabolite profiling of lipophilic compounds in human serum,42 investigation of hydrothermal upgrading products from algae,43 characterization of catalytic cracking products from fast pyrolysis oils with vacuum gas oil (VGO),44 and the study of hydrothermal upgrading of pyrolysis oil from Norwegian spruce.45
The primary goal for this study is to further evaluate this commonly used method and provide valuable information on the quantitative measurement of various functional groups in pyrolysis bio-oil. Several commonly used internal standards for 31P NMR have been examined by in situ monitoring. In situ monitoring with both pyrolysis oil and several model compounds has shown that some functional groups have time-dependent changes when employing 31P NMR. As far as we know, this is the first report on such phenomena when using 31P NMR to analyze pyrolysis oil, and has implications for obtaining reliable quantitative data with this method.
2. Materials and methods
2.1 Characterization of pyrolysis oil by NMR
All liquid NMR spectral data reported in this study were recorded with a Bruker Avance/DMX 400 MHz NMR spectrometer. All the NMR data processing and plots were carried out using MestReNova v7.1.0 software's default processing template (zero-filling is 32 K) and automatic phase and baseline correction.
2.1.1 Quantitative 1H-NMR. Quantitative 1H-NMR was acquired with 64 transients and 10 s pulse delay by employing a standard Bruker pulse sequence “zg” at room temperature.
2.1.2 Qualitative 13C-NMR. Qualitative 13C-NMR was acquired using a standard Bruker pulse sequence (zgig), a pulse delay of 5 s and 1000 scans at room temperature with a line-broadening (LB) of 5.0 Hz.
2.1.3 Quantitative 31P-NMR. Quantitative 31P NMR was acquired after in situ derivatization of the samples (standard mixtures and pyrolysis oil) with 2-chloro-4,4,5,5-tetramethyl-1,3,2-dioxaphospholane (TMDP, 100.0 μl) in a solution of (1.6
:
1 v/v) pyridine/CDCl3, chromium acetylacetonate (relaxation agent), and endo-N-hydroxy-5-norbornene-2,3-dicarboximide (NHND), cyclohexanol, or triphenylphosphine oxide (TPPO) as internal standards, which are used to evaluate the hydroxyl group contents of pyrolysis oil and standard samples. 31P NMR spectra data was acquired using an inverse gated decoupling pulse sequence, 90° pulse angle, 25 s pulse delay, and 128 scans at room temperature with a LB of 4.0 Hz. The standard error is ∼±1.2%. For the in situ monitoring experiments, the sample was scanned for 16 hours by using the same method. The bio-oil tested was produced from oak using the NREL thermochemical process development unit (TCPDU) operated in entrained flow mode at 500 °C without hot gas filtration.46
3. Results and discussion
3.1 Comparison of three commonly used internal standards for 31P NMR
NHND (endo-N-hydroxy-5-norbornene-2,3-dicarboximide) and cyclohexanol are the most commonly used internal standards for 31P NMR analysis of pyrolysis oils. The stability of these internal standards is extremely important for quantification; however, there has been no report on the stability of these internal standards in the literature. To further evaluate these standards, the comparison of three internal standards for 31P NMR has been accomplished by in situ monitoring. The same 31P NMR sample, which contains NHND, cyclohexanol and TPPO has been monitored in the NMR spectrometer for 16 hours with spectra taken once per hour (Fig. S1†). Since the amount of P reagent – TMDP (100.0 μl) is a known value, the total amount of P atoms in the NMR sample is known, and allows for the stability of the internal standards to be evaluated.
The integration results show that the content for NHND has been decreased by ∼4% after 16 hours (Table 1) and by ∼19% after 14 days (Table 1), which indicates that NHND is not very stable for a long period of time, but for short experiments (e.g., 1–5 hours or even an overnight experiment) NHND is still stable enough for use as the internal standard. The content for cyclohexanol is relatively stable (Table 1) during the whole monitoring process (both in situ for 16 hours and ex situ for 14 days), but the differences between each data point is slightly higher than TPPO (Table 1). The reason for such phenomena is still unclear. It's possible that there was an equilibrium for the reaction between cyclohexanol and TMDP. On the basis of results in Table 1, the TPPO has been identified as the most stable and reliable internal standard for the 31P NMR characterization, since the differences between each data point for TPPO is less than 1%, and the content for TPPO is also very stable during the entire monitoring test. The results for the stability test indicated that all of these three commonly used internal standards are stable and reliable enough for a short period of storage and experiment. For the overnight reaction or long term storage, TPPO will be the best internal standard. Given this, TPPO was used as internal standard for all subsequent experiments in this study.
Table 1 Stability for NHND, cyclohexanol, and TPPO during 16 hours of in situ monitoring and 14 days of ex situ monitoring
Time (h) |
Stabilitya (mol%) |
NHND |
Cyclohexanol |
TPPO |
The stability is calculated as: (1 − (calculated amount of internal standard)/(actual amount of internal standard)) × 100%. The calculated amount of internal standard is calculated as: (peak area integration for internal standard)/(peak area integration for total amount of P atoms (from −40 to 240 ppm excluded peak for TPPO)) × (P atoms in 100.0 μl of 2-chloro-4,4,5,5-tetramethyl-1,3,2-dioxaphospholane). |
1 |
0%b |
0% |
0% |
2 |
−1% |
−1% |
0% |
3 |
−1% |
0% |
1% |
4 |
−1% |
−1% |
0% |
5 |
−1% |
−1% |
0% |
6 |
−2% |
−2% |
1% |
7 |
−3% |
−2% |
0% |
8 |
−3% |
−2% |
0% |
9 |
−2% |
−2% |
−1% |
10 |
−3% |
−2% |
1% |
11 |
−2% |
−1% |
0% |
12 |
−4% |
−2% |
1% |
13 |
−3% |
−1% |
0% |
14 |
−3% |
−2% |
0% |
15 |
−3% |
−1% |
−1% |
16 |
−4% |
0% |
1% |
14 days |
−19% |
−1% |
0% |
3.2 Stability test for pyrolysis oil in 31P NMR solution
31P NMR has been developed for several years to provide quantitative results for biomass components and their thermochemical deconstruction products (e.g., pyrolysis bio-oil). The chemical shifts and integration regions for pyrolysis oil after derivatization with TMDP by a quantitative 31P NMR measurement are shown in Table S1.† In situ monitoring for a pyrolysis oil sample has been examined using 31P NMR and results are shown in Fig. 3 and Table 2. The time-dependent changes can be observed from the spectrum in aliphatic and acid areas. The integration results for different hydroxyl groups and water wt% for pyrolysis oil during 16 hours of in situ monitoring and 14 days of ex situ stability test have been shown in Table 2. The results indicate that the content for aliphatic OH groups has been decreased by ∼7% after 16 hours and by ∼56% after 14 days (Table 2). The content for carboxylic acid groups has been decreased by ∼46% after 16 hours and by ∼90% after 14 days (Table 2). The content for water has been decreased by ∼18% after 16 hours and by ∼60% after 14 days (Table 2). The contents for different aromatic OH groups were relatively stable during in situ monitoring, but decreased up to ∼25–66% after 14 days of storage. Since there was no precipitate during both in situ and ex situ stability tests, it's possible that the decreased contents of hydroxyl groups and water could be due to the formation of some other functional groups which have different peak ranges. There are some increasing peaks that have been shown in Fig. S2,† which may represent possible decomposition products. The contents for all the functional groups are very stable for the first two hours, but the acid groups begin to decrease significantly after the first two hours during the in situ monitoring test. Therefore, the recommended preparation and test duration should be less than 2 hours to ensure the accuracy of the quantification using this 31P NMR method. However, since the acid groups have been reported to be responsible for aging, corrosion, and viscosity problems47 for pyrolysis oils, a precise quantitative characterization of acid groups is very important for evaluating the properties of pyrolysis oil. To further investigate the stability for different carboxyl functional groups, several model compounds have been used to represent different carboxylic acids in pyrolysis oils, namely formic acid, acetic acid, benzoic acid and vanillic acid. It is worth noting here that this 31P NMR method has recently been validated for the analysis of pyrolysis bio-oil through an inter-laboratory study.48 Five laboratories participated, and inter-laboratory variabilities of less than 10% were achieved for aliphatic and phenolic OH groups in bio-oil. Carboxylic OH groups had a slightly higher variability, near 15%. These results are very promising, and this is the first time an NMR technique has been successfully validated by round robin for bio-oil.
 |
| Fig. 3 In situ monitoring for the stability of pyrolysis oil during 31P NMR with TPPO as internal standard. | |
Table 2 Hydroxyl group contents and water wt% for the pyrolysis oil during 16 hours of in situ and 14 days of ex situ stability tests with TPPO as internal standard
Time (h) |
Aliphatic OH mmol g−1 |
β-5 OH mmol g−1 |
4-O-5 OH mmol g−1 |
5-5 OH mmol g−1 |
Guaiacyl OH mmol g−1 |
1 |
5.4 |
0.8 |
0.5 |
0.4 |
0.6 |
2 |
5.4 |
0.8 |
0.5 |
0.4 |
0.6 |
3 |
5.4 |
0.8 |
0.5 |
0.4 |
0.6 |
4 |
5.4 |
0.8 |
0.5 |
0.5 |
0.6 |
5 |
5.3 |
0.8 |
0.5 |
0.4 |
0.6 |
6 |
5.2 |
0.8 |
0.5 |
0.4 |
0.6 |
7 |
5.2 |
0.7 |
0.5 |
0.4 |
0.6 |
8 |
5.2 |
0.7 |
0.5 |
0.4 |
0.6 |
9 |
5.1 |
0.7 |
0.5 |
0.4 |
0.6 |
10 |
5.2 |
0.7 |
0.5 |
0.5 |
0.6 |
11 |
5.1 |
0.7 |
0.5 |
0.4 |
0.6 |
12 |
5.1 |
0.7 |
0.5 |
0.4 |
0.6 |
13 |
5.1 |
0.7 |
0.5 |
0.4 |
0.6 |
14 |
5.0 |
0.7 |
0.5 |
0.4 |
0.6 |
15 |
5.0 |
0.7 |
0.5 |
0.4 |
0.6 |
16 |
5.0 |
0.7 |
0.5 |
0.4 |
0.6 |
14 d |
2.4 |
0.4 |
0.3 |
0.3 |
0.3 |
Time (h) |
Catechol OH mmol g−1 |
p-Hydroxy-phenyl OH mmol g−1 |
Carboxylic OH mmol g−1 |
H2O wt% |
1 |
0.3 |
0.1 |
1.3 |
26.8 |
2 |
0.3 |
0.1 |
1.3 |
26.8 |
3 |
0.3 |
0.1 |
1.2 |
26.8 |
4 |
0.3 |
0.1 |
1.2 |
26.2 |
5 |
0.3 |
0.1 |
1.0 |
25.9 |
6 |
0.3 |
0.1 |
1.0 |
25.1 |
7 |
0.3 |
0.1 |
1.0 |
24.8 |
8 |
0.3 |
0.1 |
0.9 |
24.8 |
9 |
0.3 |
0.1 |
0.9 |
24.0 |
10 |
0.3 |
0.1 |
0.8 |
23.9 |
11 |
0.3 |
0.1 |
0.8 |
23.1 |
12 |
0.3 |
0.1 |
0.7 |
22.7 |
13 |
0.3 |
0.1 |
0.7 |
22.6 |
14 |
0.3 |
0.1 |
0.7 |
22.1 |
15 |
0.3 |
0.1 |
0.7 |
22.1 |
16 |
0.3 |
0.1 |
0.7 |
22.0 |
14 d |
0.1 |
0.1 |
0.1 |
11.0 |
3.3 Stability test for different acids in 31P NMR solution
The in situ and ex situ monitoring processes for formic acid, acetic acid, benzoic acid and vanillic acid have been accomplished and the results are shown in Table 3. The benzoic acid and vanillic acid share a very close chemical shift in the 31P NMR (Fig. 4, ∼136 ppm), and the integration for these two aromatic acids was counted as one group in Table 3. The results indicated that during the in situ monitoring process, aromatic acids are relatively stable compared to formic acid and acetic acid, and that acetic acid is very stable during the first 4 hours. Interestingly, the formic acid content decreased dramatically during the very first hour of in situ monitoring, and the content decreased by >90% in only 5 hours. These results can also be seen in the spectra presented in Fig. 4. Since there was not any precipitate formed during the monitoring processes, the possible reason for such a dramatic change may be caused by the formation of new compounds. There were several new peaks formed during the in situ monitoring, located at ∼147 ppm, ∼33 ppm and ∼31 ppm. In addition, the TMDP was also continually consumed during in situ monitoring, which indicated that further reaction of the products from these acids with TMDP also consumes TMDP. The total P atom content remained the same for both short term and long term monitoring, which also indicated that there was no precipitate formed, nor any other reactions which could cause any changes in the concentration of total P atoms in the 31P NMR sample. Since there was no prior work documenting an instability of the product between formic acid and TMDP, some preliminary investigations have been performed for formic acid in the 31P NMR solution here, including in situ monitoring of the reaction between formic acid and TMDP by both 1H and 13C NMR, and GC-MS analysis of the final solution. To avoid possible oxidation reactions, the monitoring process was conducted in a high pressure NMR tube sealed under He gas.
Table 3 Hydroxyl group contents for the formic acid, acetic acid, benzoic acid and vanillic acid during 16 hours of in situ and 14 days of ex situ stability tests with TPPO as internal standard
Time (h) |
TMDPa |
Formic acidb |
Benzoic acid and vanillic acidb |
Acetic acidb |
Total P atomsc (%) |
The percentage calculated as: (detected amount of the TMDP)/(calculated amount for the left TMDP, which is assuming 100% reaction yield with known amount of acids mixture). The percentage calculated as: (detected amount of the carboxylic acids)/(feeding amount of the carboxylic acids). The percentage calculated as: (detected amount of all the P atoms)/(feeding amount of the all the P atoms, which is calculated by the known amount of TMDP). |
1 |
88% |
74% |
100% |
100% |
100% |
2 |
87% |
54% |
100% |
100% |
100% |
3 |
86% |
39% |
100% |
100% |
100% |
4 |
84% |
26% |
100% |
100% |
100% |
5 |
83% |
15% |
100% |
99% |
100% |
6 |
82% |
8% |
100% |
98% |
100% |
7 |
82% |
4% |
100% |
96% |
100% |
8 |
80% |
3% |
99% |
88% |
100% |
9 |
80% |
3% |
99% |
85% |
100% |
10 |
80% |
3% |
97% |
78% |
100% |
11 |
80% |
3% |
97% |
71% |
100% |
12 |
79% |
3% |
96% |
66% |
100% |
13 |
79% |
3% |
96% |
62% |
100% |
14 |
79% |
3% |
96% |
57% |
100% |
15 |
79% |
3% |
95% |
53% |
100% |
16 |
77% |
3% |
93% |
49% |
100% |
14 d |
74% |
2% |
82% |
3% |
100% |
 |
| Fig. 4 In situ monitoring for the stability of acids mixture in 31P NMR with TPPO as internal standard. | |
The in situ monitoring 1H NMR results (Fig. S3†) indicate that the proton in the HC
O bond in the product (compound 1 in Fig. 5) from formic acid and TMDP decreased significantly and there was only very limited amount of this peak left after 12 hours, which is consistent with the 31P NMR result (Fig. 6). The in situ 13C NMR (Fig. S4†) also showed that the product (compound 1 in Fig. 5) from formic acid and TMDP is not stable and can completely decompose. In contrast, there was a growing peak ∼6.4 ppm in the 1H NMR, which may represent the possible decomposition product. There is a similar growing peak in the in situ 13C NMR (Fig. S5†), which is ∼83.5 ppm. The 31P NMR also indicated that after the in situ monitoring the formic acid peak was no longer present, and there was a new peak present in the aliphatic range. One of the possible decomposition pathways for formic acid in the 31P NMR solution has been proposed in Fig. 5. GC-MS also showed that there was a peak which has the molecular ion-radical peak at 341, which is close to the proposed decomposition product (compound 2 in Fig. 5). In addition, 31P NMR also indicated that water (both peaks at 132 ppm and 16 ppm, compound 3 and 4 in Fig. 5, which are the products from water and TMDP) is one of the major decomposition products. Since the contents for the P contained decomposition products are much lower than the original P content for compound 1 in Fig. 5 (product from derivatization of formic acid with TMDP), some decomposition products may not contain any OH group, or may be gaseous. The tentative decomposition pathway from formic acid to formaldehyde in the 31P NMR solution has also been proposed (Fig. 5).
 |
| Fig. 5 Tentative decomposition pathways for formic acid in 31P NMR. | |
 |
| Fig. 6 Comparison before (top) and after (bottom) 16 hours of in situ monitoring of formic acid in 31P NMR with TPPO as internal standard. | |
4. Conclusions
In this study, the further investigation of 31P NMR, a commonly used method which can provide quantitative results on the different OH groups in various materials or complicated mixtures, has been accomplished. Several commonly used internal standards for 31P NMR have been examined by in situ monitoring. The results indicated that NHND is not very stable after a long period of storage (>12 hours), and cyclohexanol and TPPO can be used as internal standards if a long-term experiment or extended sample storage is required. Pyrolysis oil has also been investigated by both short time (16 hours) in situ monitoring and long time (14 days) ex situ monitoring. The results showed that aliphatic OH, carboxylic acid, and water contents are not very stable after 2 hours, and thus a short time of preparation, storage, and experiment needs to be considered to ensure a precise quantitative measurement. All the aromatic OH groups are relatively stable during the short time in situ monitoring, but the contents for these functional groups were still decreased by up to 66% after 14 days. The decomposition products are still unclear, but some preliminary investigations for different acids, especially the formic acid, have been accomplished. The results indicated that, compared to formic acid and acetic acid, the aromatic carboxylic acids (benzoic acid and vanillic acid) are more stable. Interestingly, formic acid was found to decompose starting with the very first hour of in situ monitoring. Further characterization found that water is one of the major products for the decomposition of formic acid in the 31P NMR solution. As far as we know, this is the first report effort on such phenomena when using 31P NMR to analyze pyrolysis oil, and these results are extremely important for obtaining reliable quantitative data with this method.
5. Acknowledgements
This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-08GO28308 with the National Renewable Energy Laboratory. Funding provided by U.S. DOE Office of Energy Efficiency and Renewable Energy Bioenergy Technologies Office. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.
References
- A. J. Ragauskas, C. K. Williams, B. H. Davison, G. Britovsek, J. Cairney, C. A. Eckert, W. J. Frederick Jr, J. P. Hallett, D. J. Leak, C. L. Liotta, J. R. Mielenz, R. Murphy, R. Templer and T. Tschaplinski, Science, 2006, 311, 484–489 CrossRef CAS PubMed.
- R. P. Anex, A. Aden, F. K. Kazi, J. Fortman, R. M. Swanson, M. M. Wright, J. A. Satrio, R. C. Brown, D. E. Daugaard, A. Platon, G. Kothandaraman, D. D. Hsu and A. Dutta, Fuel, 2010, 89(Suppl. 1), S29–S35 CrossRef CAS.
- D. A. Ruddy, J. A. Schaidle, J. R. Ferrell Iii, J. Wang, L. Moens and J. E. Hensley, Green Chem., 2014, 16, 454–490 RSC.
- M. Garcia-Perez, A. Chaala, H. Pakdel, D. Kretschmer and C. Roy, Biomass Bioenergy, 2007, 31, 222–242 CrossRef CAS.
- D. Mohan, C. U. Pittman and P. H. Steele, Energy Fuels, 2006, 20, 848–889 CrossRef CAS.
- D. C. Elliott, A. Oasmaa, F. Preto, D. Meier and A. V. Bridgwater, Energy Fuels, 2012, 26, 3769–3776 CrossRef CAS.
- W. Mu, H. Ben, A. Ragauskas and Y. Deng, BioEnergy Res., 2013, 6, 1183–1204 CrossRef CAS.
- A. E. Wroblewski, C. Lensink, R. Markuszewski and J. G. Verkade, Energy Fuels, 1988, 2, 765–774 CrossRef CAS.
- A. E. Wroblewski, C. Lensink and J. G. Verkade, Energy Fuels, 1991, 5, 491–496 CrossRef CAS.
- A. E. Wroblewski, K. Reinartz and J. G. Verkade, Energy Fuels, 1991, 5, 786–791 CrossRef CAS.
- T. Mohan and J. G. Verkade, Energy Fuels, 1993, 7, 222–226 CrossRef CAS.
- D. S. Argyropoulos, Res. Chem. Intermed., 1995, 21, 373–395 CrossRef CAS.
- A. Granata and D. S. Argyropoulos, J. Agric. Food Chem., 1995, 43, 1538–1544 CrossRef CAS.
- A. W. King, L. Zoia, I. Filpponen, A. Olszewska, H. Xie, I. Kilpelainen and D. S. Argyropoulos, J. Agric. Food Chem., 2009, 57, 8236–8243 CrossRef CAS PubMed.
- A. W. T. King, J. Jalomaki, M. Granstrom, D. S. Argyropoulos, S. Heikkinen and I. Kilpelainen, Anal. Methods, 2010, 2, 1499–1505 RSC.
- I. Filpponen and D. S. Argyropoulos, Ind. Eng. Chem. Res., 2008, 47, 8906–8910 CrossRef CAS.
- A. Spyros, D. S. Argyropoulos and R. H. Marchessault, Macromolecules, 1997, 30, 327–329 CrossRef CAS.
- Z.-H. Jiang, D. S. Argyropoulos and A. Granata, Magn. Reson. Chem., 1995, 33, 375–382 CrossRef CAS.
- E. Hatzakis and P. Dais, J. Agric. Food Chem., 2008, 56, 1866–1872 CrossRef CAS PubMed.
- E. Hatzakis, A. Agiomyrgianaki and P. Dais, J. Am. Oil Chem. Soc., 2009, 87, 29–34 CrossRef.
- P. Dais and A. Spyros, Magn. Reson. Chem., 2007, 45, 367–377 CrossRef CAS PubMed.
- P. Dais, A. Spyros, S. Christophoridou, E. Hatzakis, G. Fragaki, A. Agiomyrgianaki, E. Salivaras, G. Siragakis, D. Daskalaki, M. Tasioula-Margari and M. Brenes, J. Agric. Food Chem., 2007, 55, 577–584 CrossRef CAS PubMed.
- F. Melone, R. Saladino, H. Lange and C. Crestini, J. Agric. Food Chem., 2013, 61, 9307–9315 CrossRef CAS PubMed.
- F. Melone, R. Saladino, H. Lange and C. Crestini, J. Agric. Food Chem., 2013, 61, 9316–9324 CrossRef CAS PubMed.
- M. Zawadzki and A. Ragauskas, Holzforschung, 2001, 55, 283–285 CrossRef CAS.
- P. M. Froass, A. J. Ragauskas and J. e. Jiang, Ind. Eng. Chem. Res., 1998, 37, 3388–3394 CrossRef CAS.
- M. Lund and A. J. Ragauskas, Appl. Microbiol. Biotechnol., 2001, 55, 699–703 CrossRef CAS PubMed.
- M. Nagy, M. Kosa, H. Theliander and A. J. Ragauskas, Green Chem., 2010, 12, 31 RSC.
- Y. Pu, S. Cao and A. J. Ragauskas, Energy Environ. Sci., 2011, 4, 3154–3166 CAS.
- A. Tolbert, H. Akinosho, R. Khunsupat, A. K. Naskar and A. J. Ragauskas, Biofuels, Bioprod. Biorefin., 2014, 8, 836–856 CrossRef CAS.
- M. Nagy, B. J. Kerr, C. J. Ziemer and A. J. Ragauskas, Fuel, 2009, 88, 1793–1797 CrossRef CAS.
- M. Nagy, M. Foston and A. J. Ragauskas, J. Phys. Chem. A, 2010, 114, 3883–3887 CrossRef CAS PubMed.
- H. Ben and A. J. Ragauskas, Energy Fuels, 2011, 25, 2322–2332 CrossRef CAS.
- H. Ben and A. J. Ragauskas, Energy Fuels, 2011, 25, 4662–4668 CrossRef CAS.
- M. Kosa, H. Ben, H. Theliander and A. J. Ragauskas, Green Chem., 2011, 13, 3196–3202 RSC.
- H. Ben and A. J. Ragauskas, RSC Adv., 2012, 2, 12892–12898 RSC.
- H. Ben and A. J. Ragauskas, ACS Sustainable Chem. Eng., 2013, 1, 316–324 CrossRef CAS.
- H. Ben and A. J. Ragauskas, Bioresour. Technol., 2013, 147, 577–584 CrossRef CAS PubMed.
- S. Pan, Y. Pu, M. Foston and A. J. Ragauskas, BioEnergy Res., 2013, 6, 24–34 CrossRef CAS.
- Y. Celikbag, B. K. Via, S. Adhikari and Y. Wu, Bioresour. Technol., 2014, 169, 808–811 CrossRef CAS PubMed.
- A. Spyros, J. Appl. Polym. Sci., 2002, 83, 1635–1642 CrossRef CAS.
- M. A. DeSilva, N. Shanaiah, G. A. Nagana Gowda, K. Rosa-Perez, B. A. Hanson and D. Raftery, Magn. Reson. Chem., 2009, 47(Suppl. 1), S74–S80 CrossRef CAS PubMed.
- B. Patel and K. Hellgardt, Environ. Prog. Sustainable Energy, 2013, 32, 1002–1012 CrossRef CAS.
- D. V. Naik, V. Kumar, B. Prasad, M. K. Poddar, B. Behera, R. Bal, O. P. Khatri, D. K. Adhikari and M. O. Garg, RSC Adv., 2015, 5, 398–409 RSC.
- C. J. Richard, B. Patel, D. Chadwick and K. Hellgardt, Biomass Bioenergy, 2013, 56, 446–455 CrossRef CAS.
- R. M. Baldwin and C. J. Feik, Energy Fuels, 2013, 27, 3224–3238 CrossRef CAS.
- H. Ben and A. J. Ragauskas, ChemSusChem, 2012, 5, 1687–1693 CrossRef CAS PubMed.
- J. R. Ferrell III, M. V. Olarte, E. D. Christensen, A. B. Padmaperuma, R. M. Connatser, F. Stankovikj, D. Meier and V. Paasikallio, Standardization of Analytical Techniques for Pyrolysis Bio-oil: History, Challenges, and Current Status of Methods, Biofuels, Bioprod. Biorefin., 2015 Search PubMed.
Footnote |
† Electronic supplementary information (ESI) available: In situ monitoring data and GC-MS data. See DOI: 10.1039/c5ra23939g |
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