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Biofuels and Biomass
Non-target analysis of oxygenates in catalytic fast pyrolysis biocrudes by supercritical fluid chromatography-high resolution mass spectrometry Josephine Susanne Luebeck, Giorgio Tomasi, Kristoffer G. Poulsen, Ofei D. Mante, David C. Dayton, Sylvain Verdier, and Jan H. Christensen Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b02983 • Publication Date (Web): 11 Dec 2018 Downloaded from http://pubs.acs.org on December 17, 2018
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Non-target analysis of oxygenates in catalytic fast pyrolysis biocrudes by supercritical fluid chromatography-high resolution mass spectrometry Josephine S. Lübeck, Giorgio Tomasi, Kristoffer G. Poulsen, Ofei D. Mante‡, David C. Dayton‡, Sylvain Verdier†, Jan H. Christensen* Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen, Thorvaldsensvej 40, DK-1871 Frederiksberg, Denmark
ABSTRACT: Catalytic fast pyrolysis (CFP) biocrudes can comprise up to 30 wt% of oxygen contained in compounds such as polyphenols, acids, carbonyls and anhydrosugars and thus require upgrading by, e.g., hydrotreatment to produce transport fuels. The chemical characterization of phenolic and acidic compounds in biocrudes is of great importance to optimize the CFP process. In this study, an analytical workflow is proposed for non-target chemical fingerprinting analysis of CFP biocrudes using supercritical fluid chromatography-high resolution mass spectrometry (SFC-HRMS) with negative electrospray ionization (ESI-), followed by multivariate data analysis. The method was developed and tested on five biocrude samples from loblolly pine (Pinus taeda) with varying oxygen content (14.928.8wt% wet basis) due to different CFP conditions. The pixel-based analysis displayed the chemical variation between all samples. 24 regions of interest were tentatively identified, incl. mono- and polyphenols, fatty acids, methylated and methoxylated phenols. The identification workflow and MS/MS analysis were prioritized on the peaks with the highest relative concentration. The developed SFC-ESI--HRMS method shows high repeatability and analyzed oxygen-containing compounds with hydroxyl and/or carboxyl moieties in combination with other moieties of up to 400 Da. ACS Paragon Plus Environment
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INTRODUCTION Advanced biofuel is a term used to describe infrastructure-compatible hydrocarbon liquids that are direct replacements for petroleum-derived transportation fuels. Converting biomass into intermediates that can be upgraded into fuels by adapting existing crude oil processing technologies is a common strategy for advanced biofuel production. Direct biomass liquefaction technologies such as traditional fast pyrolysis, catalytic fast pyrolysis (CFP), hydropyrolysis and hydrothermal liquefaction are being developed to produce liquid intermediates that can be further upgraded to finished fuels. Biooil produced by traditional fast pyrolysis from various feedstocks such as wood or agricultural waste 1-3, is technically challenging to upgrade into finished fuels since its elemental composition resembles that of biomass rather than that of petroleum. The chemical and physical properties of bio-oil, such as high oxygen content, high moisture content, low pH, and poor thermal stability, are not directly translatable to petroleum processing technologies. Consequently, recent research and development efforts have focused on developing catalytic processes (CFP, hydropyrolysis and hydrothermal liquefaction) to produce hydrocarbon-rich intermediates called biocrudes. Relatively, the biocrudes are of improved chemical and physical properties to better match petroleum processes for advanced biofuel production 4. The progress on CFP 4, hydropyrolysis 5 and hydrothermal liquefaction 6 are well documented in the literature. The role of the catalyst in direct biomass liquefaction processes is to promote deoxygenation of the pyrolysis vapors while minimizing carbon loss to char, gas, and coke. Oxygen removal can occur by dehydration (loss of H2O), decarboxylation (loss of CO2), and decarbonylation (loss of CO) and the focus of most studies is on minimizing oxygen content in biocrude while maximizing biocrude yield. Nevertheless, upgrading these lower oxygen content biocrudes still poses a significant challenge suggesting that a more detailed understanding of the chemical composition of biocrude is necessary to understand how specific oxygenates react during hydroprocessing instead of focusing only on bulk oxygen content. ACS Paragon Plus Environment
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The chemical composition of biocrudes depends on the conditions during CFP and the nature of the feedstock 7, 8. The deoxygenation reactions during CFP stimulate the formation of aromatic hydrocarbons, phenolic, methoxylated and carboxylated compounds, hydroxylated aldehydes and ketones, sugars and water. These constituents originate from the decomposition of main biomass components such as cellulose, hemicellulose or lignin 1,
9-12.
The determination of the chemical
composition poses an analytical challenge due to the large span in molecular masses, vapor pressures, polarities, and chemical functionalities 1-3. Gas chromatography with mass spectrometry (GC-MS) or flame ionization detection (FID) has been employed most commonly for the characterization of volatiles in bio-oils with more than 300 identified structures 3. Comprehensive two-dimensional GC (GC×GC) with FID and time of flight (TOF)MS was applied for the characterization of an upgraded bio-oil, identifying 41 oxygenated compounds of diverse chemical families such as ketones, furans, phenols, guaiacols, etc. 13, 14. Further, thermally-labile and compounds with low vapor pressures caused by a high polarity cannot be analyzed by GC without, e.g., derivatization 15. These substances can be covered by liquid chromatography (LC)-based methods apart from heavy polar compounds 3. For example, comprehensive two-dimensional reversed phase-liquid chromatography (RPLC×RPLC) with UV detection was used to separate various types of oxygenates such as furans, phenolic acids or flavonoids 16, 17. High-end equipment, long analysis times, large consumptions of solvents, and in many cases the absence of high-resolution mass spectrometry (HRMS) highlight the obstacles of LC-based methods for chemical characterization of bio-oils or biocrudes. HRMS alone, in the form of Fouriertransform ion cyclotron resonance (FTICR) MS has been used extensively in the petroleomic analysis of bio-oils despite its high costs. It is able to describe an entire sample regardless of the volatility of its components and allowed the identification of components with up to 17 oxygen atoms so far 1. Supercritical fluid chromatography (SFC) was investigated as a complementary separation technique for the characterization of oxygenated compounds in bio-oils and processed lignin samples 18, 19. In SFC, a super- or subcritical fluid (mostly CO2 or a mixture with an organic modifier) is employed as the mobile phase allowing for a higher optimal velocity, higher diffusivity and lower viscosity than in ACS Paragon Plus Environment
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typical LC-solvents. Also, higher densities than of gasses, lower temperatures and shorter analysis times than in GC make SFC a highly efficient chromatographic platform without the risk of thermal degradation of some constituents in biocrudes 20. The separation in SFC can be described as normalphase-like, thus, compounds of higher polarity are retained more strongly than nonpolar compounds. This makes SFC comparable to normal-phase LC evading the use of toxic solvents such as toluene or hexane and can be highly advantageous, especially in hyphenation with electrospray ionization (ESI) MS which is selective in negative mode for phenolics and acids 10. Supercritical fluid chromatography has been applied as a pre-fractionation step in order to improve the separation in GC×GC for a coal tar middle distillate 21, 22. Sarrut et al. hyphenated RPLC with SFC and UV detection to a maximum degree of orthogonality achieving slightly higher peak capacities with the RPLC×SFC configuration than with RPLC×RPLC and tested it successfully on an aqueous fraction of a bio-oil 23. Compound identification is key in the non-target analysis of biocrudes. Compound Structure Identification (CSI):FingerID is currently considered one of the best automatic MS/MS analysis tools 24, 25.
CSI:FingerID uses the fragmentation pattern to predict the fingerprint of a molecular structure and
returns potential candidates after an automated search in a molecular structure database, e.g. HMDB. It recently has been applied by Larson et al. 26, who positively identified 59 compounds in lignin with GCMS and CSI:FingerID compared to identification with NIST library (22 compounds). Peak prioritization of the identification workflow, however, is another key component in non-target analysis especially when larger data sets are analyzed. Prioritization can be intensity-based, according to the presence of distinctive isotopic patterns or effect-directed analysis (EDA)-based
27.
Another way to prioritize is to
look at the biggest variance between samples, e.g. by using the pixel-based approach 28. The pixel-based approach bypasses the need for data reduction by retrieving every detector intensity at every retention time point - so called ‘pixel’ - and uses them as input for a principal component analysis (PCA) instead of employing specific peak areas or intensities alone 28. That approach was applied herein to highlight the biggest variation between biocrude samples and to prioritize peaks for identification. To the best of ACS Paragon Plus Environment
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our knowledge, the pixel-based approach in combination with the identification workflow has not been applied on SFC-MS data and biocrude samples beforehand. The aim of this study was to develop and test a non-target workflow for the analysis of oxygencontaining compounds in biocrudes using SFC-ESI--HRMS and multivariate data analysis. The nontarget workflow was developed and validated on five CFP biocrudes. The multivariate data analysis workflow consists of three tiers: (i) visual inspection of chromatograms to identify interesting chromatographic regions, (ii) pixel-based PCA of the base peak ion chromatograms (BPC) to highlight the largest variation between the biocrudes; and (iii) feature detection, extraction and peak annotation to identify peaks that are characteristic for one or several of the CFP biocrudes.
EXPERIMENTAL SECTION Chemicals. Five oxygenated polycyclic aromatic compounds (OPACs, 4 µg/mL) were selected from a set of compounds used in Lübeck et al. 29 to develop the method: 1-naphthoic acid, 1hydroxynaphthalene, 1-hydroxypyrene, 1-pyrenecarboxylic acid were purchased from Sigma-Aldrich (St. Louis, MO, USA); 1,7-dihydroxynaphthalene was bought from Acros Organics (Geel, Belgium) (Table SIa.1). Eugenol (1 µg/mL) and linoleic acid (0.066 µg/mL) were bought from Sigma-Aldrich (St. Louis, MO, USA). Carbon dioxide (CO2 N48) with a purity of ≥99.998% was purchased from AirLiquide (Paris, France). Ethanol, methanol (MeOH), isopropanol and dichloromethane (DCM) (all HPLC grade) were bought from Sigma-Aldrich (St. Louis, MO, USA). Water was obtained in-house with a type I ultrapure water purification system from ELGA-Veolia LabWater (High Wycombe, UK). Leucine-enkephaline (0.2 ng/mL) was acquired from Waters (Milford, MA, USA). Samples. The sample set consisted of five biocrude feeds (F1-F5) made by CFP of loblolly pine sawdust (Pinus taeda) and produced by RTI International. These biocrudes were selected as they were produced from the same biomass and the same catalyst in the CFP unit providing a range of total oxygen content and variable chemical composition. Sample information and CFP parameters are shown ACS Paragon Plus Environment
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in Table 1. The oxygen content varied between 14.9 and 28.8wt% wet basis. The pilot-scale catalytic biomass pyrolysis unit at RTI International, used to produce the five biocrudes, was described in previous works 30,
31.
The same samples were analyzed with different analytical platforms to gain a
comprehensive characterization of the chemical composition
14.
A biocrude from another origin was
used as a quality control (QC) sample throughout the study (29.0wt% oxygen). The samples were stored in the dark at room temperature. 0.1 g of the samples was dissolved in 10 mL DCM:MeOH (1:1) and stored at -20°C in 20 mL amber-glass vials. For the analysis, samples were diluted 1:10 and divided into three analytical replicates (henceforth designated by the letters A, B and C). SFC-HRMS. Supercritical Fluid Chromatography. All sample replicates were analyzed with an Acquity UPC2 from Waters (Milford, MA, USA) together with five replicates of the QC sample and six of the method blank (DCM:MeOH, 1:1). Carbon dioxide and ethanol were used as A- and B-solvents, respectively. Ethanol was chosen based on a study by Lübeck et al. that tested the separation of 20 OPACs under a variety of conditions 29. The gradient program was: hold at 1% B for 0.75 min, increase with 0.33% B/min for 10.5 min, 1.92% B/min for 13.25 min, hold at 30% B for 5.5 min, back to original conditions within 0.1 min and an isocratic hold for 4.9 min. Based on 29, the separation was performed on a Torus 2-picolylamine column (3.0×100 mm, 1.7 µm) plus VanGuard pre-column (2picolylamine, 2.1×5.0 mm, 1.7 µm), both from Waters (Milford, MA, USA) at 40°C column temperature. The total run time was 35 min with a flow rate of 1.5 mL/min, and an injection volume of 1.5 µL. The automated backpressure regulator was set to 150 bar. High-Resolution Mass Spectrometry. The mass analyser (quadrupole time-of-flight, qTOF) was a G2-Si Synapt MS from Waters (Milford, MA, USA). The interface between SFC and mass spectrometer was established via the Waters UPC2 splitter device with MeOH as a make-up solvent (flow rate: 0.3 mL min-1). The mass spectrometer was set up as follows: capillary voltage, 2.5 kV; source temperature, 120°C; scan rate, 2 scans s-1; and mass range, 50 to 600 Da. A lockspray source was used, and mass calibration was performed every 30 s, with leucine-enkephaline as the reference compound (mass: 554.2615 Da). First, the TOF-MS was run in scan mode. Second, MS/MS spectra ACS Paragon Plus Environment
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were collected for the 24 regions of interest that were investigated further after the pixel-based analysis (see below). A list of identified parent ions and the largest observable fragments (if present) served as the input to the TOF-MRM (multiple reaction monitoring) mode of the mass analyser. The cone voltage was set to 25 V, scan time to 0.2 s, and the collision energy was ramped from 10 to 40 V. Data analysis. The BPCs of the feeds were cut at 23 min and above 400 Da to remove the unresolved peak region at the end of each run, because hardly any non-zero signal was registered above the mass limit; afterwards, they were pre-processed, stacked in a data matrix and analysed by pixel-based PCA 28, 32.
Prior to creating the BPCs, the chromatograms were binned to nominal mass (bin intervals: [m
-0.1, m + 0.9)); while in theory this could lead to slightly different BPCs, the differences between binned and raw data were minuscule (Figure SIa.1). The considerable reduction in size allowed a more expedient handling and facilitated plots. The data set (15 samples × 3873 retention times) was split into a training set (n = 10) constructed on two replicates of all five feeds and a validation set (n = 5) with the remaining replicates. The validation set was projected onto the final model as a quality control. The chromatograms were pre-processed to reduce non-chemical variation: first, background ions that were observed both in the blanks and the samples were removed, namely m/z 175, 311, 325 and 339; second, the baseline was removed by subtracting the lowest part of the convex hull from each BPC
33.
Subsequently, the chromatograms were aligned with a two-step procedure: first, iCOShift on the whole chromatogram 34 and second, Correlation Optimized Warping (COW) 35. Finally, the aligned chromatograms were normalized to unitary Euclidean norm to remove absolute concentration as a salient source of variation 28, 33. The average BPC from the training set was used as the target for both alignment algorithms allowing for a maximum absolute correction of 1% of the total length of the BPC. The segment length (2.092 min) and slack (3.5 s) parameters for COW were obtained via the optimCOW algorithm by Skov et al. 36 (the initial settings for the search algorithm are shown in Table SIa.2). Pre-processing and analysis were performed with in-house scripts using Matlab 9.0 (R2016A, MathWorks Inc., Natick, MA, USA).
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Identification. Both MS and MS/MS spectra of the largest (in absolute terms) loading coefficients obtained from the pixel-based analysis served as input for SIRIUS (v4.0) and the incorporated CSI:FingerID (https://bio.informatik.uni-jena.de/software/sirius/)
24, 37.
SIRIUS was used to determine
the molecular formula based on accurate mass, isotopic patterns, and fragmentation patterns, whereas CSI:FingerID uses the fragmentation pattern to predict the fingerprint of the molecular structure and returns potential candidates after an automated database search. Databases used herein included PubChem 38, KEGG 39, CompTox 40, HMDB 41. Ranking occurred firstly after the CSI:FingerID scoring system, and secondly, after the similarity score which in some instances lead to a different ranking. After the database search, candidates containing at least one hydroxyl- and/ or carboxyl moieties were prioritized over solely carbonyl moieties due to the nature of the ionization mode (deprotonation in ESI-). Results were compared with literature to investigate retention, elution orders and group-type separations. Additionally, an identification confidence level was applied as proposed by Schymanski et al. 42. The classification into these levels was achieved manually and should be regarded as a scale for the identification confidence. Level 1 implies an unambiguous identification confirmed with a reference standard; Level 2 indicates a probable structure confirmed by an unambiguous match with a literature or library spectrum or by diagnostic evidence; Level 3 describes tentative candidates with information for possible structures but insufficient evidence for one specific structure; a compound is labelled Level 4 if only an unequivocal molecular formula can be assigned; and Level 5 means only an exact mass can be presented without the possibility of allocating a molecular formula.
RESULTS AND DISCUSSION Optimization of SFC analysis and repeatability. Initial tests on the samples revealed carryover, unresolved regions at the end of the chromatograms and compounds eluting in subsequent runs due to strong retention (Figure SIa.2). Several measures were therefore taken to minimize carryover and to improve repeatability. Firstly, to remove residual sample components from the injection system, the ACS Paragon Plus Environment
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weak and strong solvents making up the washing solutions of the sample needle were changed from methanol for both to isopropanol and methanol:water (80:20) respectively. The BPCs of blanks analysed in-between sample runs showed a considerable reduction in contamination and carryover (Figure SIa.2). Secondly, to ensure a more efficient cleaning of the SFC system one blank with the same gradient as the samples, and an additional blank with a shorter but steeper gradient (termed Clean) were run between each sample. Finally, the runs in the analytical sequence were blocked according to the replicates (i.e., A before B and C) (Table SIa.3).
Figure 1. BPC of a biocrude sample (Feed 3). Extracted ion chromatograms (EIC) of an OPAC standard mixture (grey box, same time axis as for the BPC). Hy-Na: hydroxynaphthalene ([M-H]: 143.0502); NaCA: naphthoic acid (171.0451); Hy-Pyr: hydroxypyrene (217.0659); 1,7Dihy-Na: 1,7dihydroxynaphthalene (159.0451); Pyr-CA: pyrenecarboxylic acid (245.0608). With a polar 2-picolylamine column, retention mechanisms in SFC are comparable to normal-phase LC. The results of the targeted analysis of the five OPACs were comparable to the results observed in Lübeck et al.
29;
as such, carboxylated OPACs were more strongly retained, and the corresponding
peaks were broader and expressed more tailing than their hydroxylated derivatives, e.g. hydroxynaphthalene eluted earlier than naphthoic acid (Figure 1). Further, an increase in π-system (e.g. hydroxynaphthalene and hydroxypyrene) or an increase in the number of functional groups (e.g., hydroxynaphthalene and 1,7-dihydroxynaphthalene) lead to stronger retention. ACS Paragon Plus Environment
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Although these compounds were not expected in such a high abundance in CFP biocrudes, their retentive behavior could give some indication on the retention of the monoaromatic products from the CFP such as methoxy- or polyphenols 11, 12. The repeatability of the method was assessed by the analysis of five analytical replicates of the QC sample (Figure SIa.3). Seven randomly chosen peaks along the time axis that cover the whole gradient in the BPC were chosen to assess retention time shifts and signal to noise (S/N) ratios along the gradient and between multiple runs (Table SIa.4). The relative standard deviations (RSD) of the retention times were below 0.2%RSD, for S/N between 15.4%RSD for peak #5 (S/N=2.74) and 20.1%RSD for peak #6 (S/N=36.4). This level of uncertainty was deemed acceptable. Visual inspection. The chromatograms of the five biocrudes were examined to get a first idea of the composition of each biocrude sample and of potential regions of interest that could help to distinguish the biocrudes with pixel-based multivariate data analysis. Two regions in the BPCs contain features that are likely good candidates to identify common patterns across the samples (Figure 2). The first region (A) is defined by intense peaks with retention times < 10 min and with m/z between 250 and 320; the second region (B) is comprised of broad peaks between 11 and 20 min and with m/z < 200. The peak broadening could be caused by the strong retention of more polar compounds (especially for region B) or by the insufficient resolution of compounds with the same nominal mass. In particular, Feed 3, 4 and 5 (i.e. high oxygen content, Figure 2.c-2.e) exhibit very similar patterns only with differences in intensity in region B (more polar and lower mass compounds) and are not as easy to distinguish by visual inspection as Feed 1 and Feed 2 (Figure 2.a-2.b). These feeds show different patterns: in Feed 1 both regions are less intense whereas in Feed 2 intensities are high for region A and low for region B. The low intensities can partly be explained by the low oxygen contents (17.0 and 14.9wt% for Feed 1 and Feed 2, respectively), whilst the differences in composition of oxygenates are likely to originate from the lightness of the fraction (viz. Feed 1 is a light and Feed 2 a heavy fraction) and the temperature of the simulated distillation, which was lower for Feed 1 (143°C/ 220°C/ 450°C at T5/ T50/ T95) than for Feed 2 (171°C/ 342°C/ 488°C). Figure 2.f shows the similarities between the feeds based on their ACS Paragon Plus Environment
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BPCs, but it also highlights regions of large variation, especially for Feed 2. The visual inspection of the BPC alone would be cumbersome, hence, PCA was employed to better identify the difference between the biocrudes and perform a more detailed characterization and tentative identification of diagnostic compounds.
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Energy & Fuels a) Feed 1 (17.0wt%)
b) Feed 2 (14.9wt%)
A
A
B
B
c) Feed 3 (25.5wt%)
d) Feed 4 (28.0wt%)
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B
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f) BPC of one replicate of all feeds
e) Feed 5 (28.8wt%)
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Feed 5 Feed 4 Feed 3 Feed 2 Feed 1
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Intensity ×107 [A.U.]
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1.2 1.0
B
0.8 0.6 0.4 0.2 0.0 0
5
t10 R [min]
15
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Figure 2. Two-dimensional landscapes (m/z vs. time [min]) (a-e) and the BPCs of Replicate A (f) of the five feeds. Region A (low tR, high m/z) and B (high tR, low m/z) are the regions of interest. All chromatograms were binned to nominal mass as described in the experimental section and Feed 2 was cropped at a maximum intensity of 107 A.U. to facilitate comparisons. Data analysis. Three PCA models were calculated on BPCs. The first model (henceforth referred to as “global model”) was calculated using two replicates of each of the feeds (training set) while the five remaining runs were projected on the model (test set). Figure 3 displays the score plots for the global model.
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The first three principal components (PCs) explain 92.0% of the variance (viz., 65.0%, 20.3%, and 6.5% respectively for PC1, 2 and 3), Figure 3.a and 3.b. The loadings did not show patterns of nonchemical shift and replicate feeds cluster together in the score plots, also those included in the validation set (open markers). This, again, is indicative of good repeatability of the method and of the successful pre-processing 28. The loading plots of the global model are shown in Figure 4; regions of interest with the largest loading coefficients (in absolute terms) are annotated with numbers. PC1 mainly depicts Feed 2, whose samples have large positive scores; thus, this feed is characterized by relatively higher intensities for peak clusters 8-9, 11 and 13 (i.e., those with positive loading coefficients) and relatively lower ones for the others. PC2 separates Feed 1 (large positive scores) from Feeds 2, 3 and 4 (around zero) and 5 (large negative ones). a)
b)
Figure 3. Score plot of PC1 vs. PC2 (a) and PC1 vs. PC3 (b) for the global model. The test set (open markers) were projected onto the model.
Thus, Feed 1 has a high relative intensity of peaks 1, 4-7, 19 and 22-24, whereas Feed 5 has large relative intensity of peaks 2, 8-18 and 20 (large negative loading coefficients). The separation between Feeds 3, 4 and 5 is limited also along PC2, but it is apparent along PC3 (Figure 3.b), which indicates that Feeds 3 and 4 are relatively high (compared to Feed 5) for peaks 2, 3, 5, 13 and 19-21. PC2 correlates to some extent to the type of biocrude, i.e. Feed 1 is a light fraction, whereas Feeds 2 and 5 ACS Paragon Plus Environment
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are heavy ones and 3 and 4 are mixtures of light + heavy. Feeds 3 and 4 overlap in all the relevant PCs, which is consistent with the fact that they are sampling replicates originating from the same CFP process (i.e., they underwent the same catalytic processes and thus are expected to express strong similarities in their chemical composition – Table 1). Kristensen et al. analyzed by GC×GC-HRMS and derivatization and observed similar results in PCA regarding Feeds 3 and 4 due to their similarity in their chemical composition 14.
Figure 4. PC1 to PC3 loading plots. Numbers 1-24 indicate peaks/ areas with a high influence on the model (descriptive peaks). The dashed line in each loading plot represents the average BPC for the five feeds. In a tridimensional score plot, the feeds essentially form an irregular tetrahedron in the PC space having the replicates of Feeds 1, 2, 3/4, and 5 as vertices. While this shows that the method is indeed capable of separating biocrudes that have undergone different CFP processes, it has been shown that models on subsets of samples may capture finer differences 28. Due to the limited number of samples in this data set, it is difficult to find meaningful sub-models. For instance, any 3-feed subset would be close ACS Paragon Plus Environment
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to calculating a PCA on one of the faces of the tetrahedron; more interestingly, the loadings of the first PC calculated on the BPCs of two feeds will approximate the vector of differences between the corresponding biocrudes (note that this is true so long as the same number of replicates for each feed is included, as is the case here). Thus, for example, in the sub-model where only Feeds 1 and 2 are analyzed, the PC directly connects the two clusters formed by the replicate samples of each feed and the difference between two feeds is not split between multiple components as in the global model. Once the sign of the scores is taken into account, the loadings of the sub-model can then be interpreted straightforwardly (Figure 5).
Figure 5. PC1 loadings (left) and scores (right) for the two sub-models. PC1 was found to contain all chemical information for each sub-model: a) Feed 3, 4 vs. 5, b) Feed 1 vs. 2. Numbers 1-24 indicate peaks/ areas with a high influence on the model (descriptive peaks). Scores represent the average of all triplicates plus standard deviation (error bars). The dashed line in each loading plot represents the average BPC of the samples included in each sub-model. Two comparisons were made: a) Feeds 3, 4 vs. 5, which were most similar in the global model in PC1 vs. PC2; and b) Feed 1 vs. Feed 2, which are lowest in oxygen content among the measured samples (17.0 and 14.9wt%, respectively). Sub-model a), in which PC1 explains 79.9% of the variance, displays ACS Paragon Plus Environment
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the chemical differences between Feeds 3, 4 and 5 which were separated along PC3 in the global model (Figure 3.b). As expected, the first PC in sub-model a) (Figure 5.a) has a strong similarity with PC3 in the global model, which only explained 6.5% of the variance. Likewise, the first PC in sub-model b) (Figure 5.b), which explains 96.7% of the variance, has a strong similarity with PC1 in the global model (65.0% explained). The differences between the sub-models and the global model are subtle: in submodel a) the coefficients for peaks 1 and 22 are close to zero, and peaks 6 and 7 have negative coefficients; in sub-model b) only peak 2 changes sign compared to the global model. In summary: from sub-model a), Feeds 3 and 4 are relatively high for peaks 2-7, 19, 21 and 23 (negative loading coefficients) whereas Feed 5 is especially high for 8, 10, 14-18 and 24 (positive coefficients). Sub-model b) shows the variation between Feed 1 and 2. Therefore, Feed 1 is characterized by relatively higher intensities of peaks 1, 3-7 and 14-23, and Feed 2 by components 2, 8, 9, 11-13 and 24. Tentative identification. The discriminating power of the high resolution mass spectrometer based on an example of m/z 161 in Feed 2 is shown in Figure SIa.4. At nominal mass 161, signals cover the chromatographic space from 0 to 18 min. A mass resolution of ±0.01 Da was sufficient to retrieve three exact masses, i.e., 161.096, 161.060 and 161.024 corresponding to three distinct sum formulae after a quick search via Elemental Composition v.4.0 (MassLynx): C11H14O, C10H10O2, and C9H6O3, respectively. The lack of fragmentation in electrospray ionization MS can exacerbate an unambiguous identifications as the same parent ion can lead to various empirical formulae and subsequently hits in databases. In tandem MS, fragmentation is enhanced with higher collision energies (here a ramp between 10 and 40 V) and provides more informative fragmentation patterns. A list of tentatively identified compounds is provided in Table 2, with additional information for each compound in Table SIb.1 and Figures SII.1-36. For all measured parent ions, the M+1 isotopic peak was detected. The determination of the molecular formulae [CxHyOz] was straightforward, i.e., 82.9% (30 out of 36) of the compounds were ranked first ACS Paragon Plus Environment
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and 17.1% (6 out of 36) second with the SIRIUS software. Despite the additional MS/MS data, the database search in CSI:FingerID resulted in several hundreds of candidates per molecular formula ranked according to similarity scores which required an additional manual inspection of the likely candidates. CSI:FingerID primarily returned monophenolic structures with carbonyl-, methyl- and/or methoxyl moieties which are also typical products of the pyrolysis of lignin 19, 43. Additionally, some fatty acids and polyphenols were identified. For example, the presence of fatty acids (peaks 8 [a-d] and 9) could be verified with standards of similar fatty acids ranging from m/z 254 to 281 that eluted in the same retention time range as the identified fatty acids (Figure SIb.1). Lipid-like structures elute in retention time ranges according to their compound class (e.g., fatty acids or phospholipids). This has been described elsewhere for SFC
44.
The elution of fatty acids and other types of lipids provides an
advantage of SFC over GC as the latter often requires derivatization or sample clean-up to detect that class of compounds. However, due to the high complexity of the mixture and diverse isomers, only linoleic acid (peak 9) could be matched with a standard at Level 1 confidence (Figure SIb.2) 42. As aforementioned, these biocrude samples have been analyzed also with GC×GC-HRMS by Kristensen et al.
14.
Derivatization prior GC×GC was necessary for the analysis of polar oxygenates
such as acids and sugars. Furthermore, fractionation as sample preparation was inevitable to simplify the GC×GC chromatograms and to some extend pre-concentrate polar oxygenates which would be hidden by the high intensity of nonpolar compounds. With SFC, the entire sample could be analyzed without fractionation given the selective range in negative ESI towards ionizable oxygenates (viz. phenolic compounds). Similar to the results herein, Kristensen et al. identified some fatty acids (viz. palmitic and stearic acid) and monoaromatic phenols (e.g. catechol, hydroxybenzaldehyde, -acetophenone, vanillin, acetovanillone and coniferyl aldehyde)
14.
The fact that two different analytical platforms lead to very
similar results is confirmative for the quality of the identification workflows. A disadvantage of this ACS Paragon Plus Environment
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SFC method is that sugars and acids with more than one carboxyl group could not be analyzed as they are too strongly retained on the column. These, however, have been identified with GC×GC-HRMS and derivatization, e.g. levoglucosan and methylsuccinic acid (two carboxyl groups). Additional optimization of the SFC method could aid the elution of these compounds, e.g. by adding an appropriate additive like ammonium acetate to the modifier and thereby increasing the eluent strength. Nevertheless, SFC was able to detect phenolic compounds with more than one (aromatic) ring (peaks 11a, 12, 13, 2224, Table 2 and Figures SII.19, 22, 23, 34-36) which were not detected by Kristensen et al. 14. Additionally, it would be interesting to perform the same non-target workflow in positive ionization mode to identify, e.g. carbonyls. This study, however, focused on negative ionization mode as compounds like carbonyls have been covered partially in 14. Another challenge occurred with the identification of peak 14 (C10H12O2, tR=9.69 min). Database searches of the MS and MS/MS spectra with CSI:FingerID ranked eugenol-like structures first, which is in contrast to what was observed by Prothmann et al. 19 on the same type of column (2-PIC). There, a eugenol standard eluted within the isocratic hold of 99% CO2 (tR < 1 min) which implies a false positive match. Hence, a eugenol standard was analysed herein confirming the results of 19 and thereby rejecting the match by CSI:Finger ID for peak 14 because both retention time and fragmentation pattern disagreed (Figure SIb.3). Subsequently, the MS/MS spectrum of peak 14 was analysed with another in silico fragmentation web tool (MetFrag 45). There, eugenol was not ranked as one of the 2594 candidates but 1-(3-Hydroxy-4-methylphenyl)propan-2-one was the first phenolic compound which has also been suggested herein (Table 2). Additionally, the stability of the standard eugenol was poor causing several peaks in the chromatogram that represent degradation products, including iso-vanillin (peak 3a, C8H8O3, Figure SIb.4) and coniferyl aldehyde (peak 5, C10H10O3, Figure SIb.5). Both were identified in the samples and thereby matched with a Level 2 confidence. The elution order of peaks 2b, 3a, 5, 16 and 17 was also in coherence with results from 19 strengthening the level of identification further but not higher than Level 3. Several isomers have been found for peaks 4a/ 4b (propylphenols), 6b/ 7 (methoxy-benzoic acids), ACS Paragon Plus Environment
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peak
group
11
(diterpenoid/
fatty
acids),
15a/b
(catechol/methoxyphenol)
and
20/ 21
(coumaraldehydes). It is not clear which peak corresponds to which isomer, hence, they are only tentative candidates, categorized as Level 3 or 4 depending on whether several types of compounds were suggested for the same molecular formula. As shown by the tentative identification, the biocrude samples were dominated by aromatic oxygenates with different distributions of carbonyl-, hydroxyl, alkyl- and/or methoxyl moieties. The differences observed in the chemical composition of the biocrude samples are primarily due to the pyrolysis conditions. Several studies show that catalytic pyrolysis operating conditions such as temperature, vapor residence time and weight hour space velocity (WHSV) affect the product distribution and composition 46-49. The five samples analysed in the study were produced at three distinct pyrolysis conditions. The major pyrolysis parameter that changed during the CFP tests was the temperature. As shown in Table 1, Feeds 1 and 3/4 were all sampled from batches of biocrude produced at 520°C during an extended period of 30 h with continuous catalyst circulation and regeneration 31. Specifically, Feed 1 (light fraction) was from the batch collected further downstream of the condensation train with a second coalescing filter after the product gas stream was cooled to 4°C in a chilled water-cooled heat exchanger. Feeds 3/4 are both blends of the light and heavy fraction (collected with a first coalescing filter with input vapor temperature of 95-98°C). Feed 2 was a heavy fraction from a batch produced at 465°C during a CFP run that lasted for 13 hours. Lastly, Feed 5 was collected during a CFP test at 575°C that lasted for proximately 7 hours. One of the main findings is that the biocrude sample (Feed 2) produced at a relatively lower pyrolysis temperature (465°C) contained higher relative concentrations of fatty acids such as linoleic acid which were not obvious in the other samples. High relative concentrations of these compounds were also observed with GC×GC-HRMS for that particular feed 14. It can be inferred that these types of carboxylic acids are generated during pyrolysis of the pine feedstock but are cracked at higher CFP temperatures. This observation is similar to previous studies
48, 50.
For instance, in a study on catalytic
pyrolysis of corncob with alumina catalyst, it was found that the concentration of carboxylic acids such ACS Paragon Plus Environment
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as linoleic acid, benzeneacetic acid, and hexadecanoic acid decreased with increase in temperature
48.
Oxygenated aromatics such as guaiacols and naphthols increase with decrease in temperature
46.
Additionally, oxygenates with a higher order of functionality (e.g. peak 24) were higher in Feed 2 due to less cracking. Overall, the lack of severe catalytic cracking at 465°C explains why the loading coefficients for compounds with molecular weights > 200 Da were dominant in Feed 2. In contrast, higher temperature decomposes heavy molecular weight oxygenated compounds
47.
The
results also reveal that at moderate temperatures (520°C), cracking of the methoxyl moieties attached to the aromatic oxygenates occurs. This is the reason for the relatively high concentrations of simple phenols like propylphenol and diethylphenol in Feeds 1 and 3/4, and is also confirmed by the results observed with GC×GC-HRMS of the same samples Nugranad
46
14.
Similar results were reported by Williams and
in catalytic pyrolysis of rice husks. Furthermore, pyrolysis temperatures higher than the
520°C seem to promote slightly different type of reactions. For example, the presence of higher relative concentrations of catechols, hydroxybenzaldehyde, and hydroxyacetophenone in Feed 5 seems to indicate that demethylation of methoxyphenols are enhanced at high pyrolysis temperature.
CONCLUSION Herein, a SFC-ESI--HRMS analysis of biocrudes was applied for the chemical characterization of phenolic and acidic oxygenates. The method was tested on five CFP biocrudes with varying oxygen content and pyrolytic conditions. Supercritical fluid chromatography in combination with the pixelbased approach and identification via CSI:FingerID has proven as a successful alternative for the analysis of thermally labile, mid-polar and compounds with a molecular weight < 400 Da. The SFC method is faster compared to GC and does not require sample pretreatment, derivatization or high temperatures that could lead to thermal degradation of some compounds, highlighting the advantages of SFC over GC when analyzing biocrudes. The pixel-based analysis was able to differentiate between the biocrude samples based on the relative variation of levels of oxygenates and lead to the prioritization of specific peaks and regions. A list of 24 regions of interest was tentatively identified with different levels ACS Paragon Plus Environment
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of confidence that were assigned to specific biocrudes according to their relative concentrations. Similar compounds (e.g. coniferyl aldehyde or hydroxyacetophenone) as in GC×GC-HRMS analysis of the same biocrude samples were identified. The compounds included monoaromatic structures with alkyl -, methoxyphenyl, carboxyl and/or dihydroxyl moieties, but also fatty acids/alcohols, diterpenoids and stilbenoids. The study also revealed the merits and pitfalls of identification and database matching without the use of target analytes. In the future, more samples need to be analyzed to validate the applicability of the pixel-based approach with SFC derived data and for the non-target workflow. The use of positive ionization mode would give additional information on the composition of biocrudes.
ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website. Detailed information on analytical sequence, binned vs. raw data, method optimization, quality control information, results of identification with CSI:FingerID. AUTHOR INFORMATION Corresponding Author *Tel:
+4535332456, E-mail address:
[email protected] Present Addresses † Haldor
‡
Topsoe A/S, Haldor Topsøes Allé 1, DK-2800 Kgs. Lyngby, Denmark
Energy Technology Division, RTI International, 3040 East Cornwallis Road, Research Triangle Park,
North Carolina 27709, United States ACKNOWLEDGEMENTS The U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office is acknowledged for financial support under contract EE-0005358 (Catalytic ACS Paragon Plus Environment
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Upgrading of Thermochemical Intermediates to Hydrocarbons). The authors would like to thank L. Malmquist for his support in the laboratory, and Samin Fathali Nejad for her input on the identification. REFERENCES 1. Kanaujia, P. K.; Sharma, Y. K.; Agrawal, U. C.; Garg, M. O., Analytical approaches to characterizing pyrolysis oil from biomass. TrAC 2013, 42, 125-136. 2. Undri, A.; Abou-Zaid, M.; Briens, C.; Berruti, F.; Rosi, L.; Bartoli, M.; Frediani, M.; Frediani, P., A simple procedure for chromatographic analysis of bio-oils from pyrolysis. J. Anal. Appl. Pyrolysis 2015, 114, 208-221. 3. Staš, M.; Kubička, D.; Chudoba, J.; Pospíšil, M., Overview of Analytical Methods Used for Chemical Characterization of Pyrolysis Bio-oil. Energy Fuels 2014, 28, (1), 385-402. 4. Venderbosch, R. H., A Critical View on Catalytic Pyrolysis of Biomass. ChemSusChem 2015, 8, (8), 1306-1316. 5. Dayton, D. C.; Wang, K.; Peters, J. E.; Mante, O. D., CHAPTER 5 Catalytic Biomass Pyrolysis with Reactive Gases. In Fast Pyrolysis of Biomass: Advances in Science and Technology, The Royal Society of Chemistry: 2017, pp 78-95. 6. Elliott, D. C.; Biller, P.; Ross, A. B.; Schmidt, A. J.; Jones, S. B., Hydrothermal liquefaction of biomass: developments from batch to continuous process. Bioresour Technol 2015, 178, 147-156. 7. Wang, K.; Mante, O. D.; Peters, J. E.; Dayton, D. C., Influence of the Feedstock on Catalytic Fast Pyrolysis with a Solid Acid Catalyst. Energy Technol. 2017, 5, (1), 183-188. 8. Mante, O. D.; Dayton, D. C.; Soukri, M., Production and distillative recovery of valuable ligninderived products from biocrude. RSC Adv. 2016, 6, (96), 94247-94255. 9. Meier, D.; Windt, M., Analysis of Bio-Oils. In Transformation of Biomass, John Wiley & Sons, Ltd: 2014; pp 227-256. 10. Smith, E. A.; Park, S.; Klein, A. T.; Lee, Y. J., Bio-oil Analysis Using Negative Electrospray Ionization: Comparative Study of High-Resolution Mass Spectrometers and Phenolic versus Sugaric Components. Energy Fuels 2012, 26, (6), 3796-3802. 11. Alsbou, E.; Helleur, R., Whole sample analysis of bio-oils and thermal cracking fractions by PyGC/MS and TLC–FID. J. Anal. Appl. Pyrolysis 2013, 101, 222-231. 12. Attia, M.; Farag, S.; Habibzadeh, S.; Hamzehlouia, S.; Chaouki, J., Fast pyrolysis if lignocellulosic biomass for the Production of Energy and Chemicals: A Critical Review. Curr. Org. Chem. 2016, 20, 2458-2479. 13. Omais, B.; Crepier, J.; Charon, N.; Courtiade, M.; Quignard, A.; Thiebaut, D., Oxygen speciation in upgraded fast pyrolysis bio-oils by comprehensive two-dimensional gas chromatography. Analyst 2013, 138, (8), 2258-2268. 14. Kristensen, M.; Hansen, A. B.; Mante, O. D.; Dayton, D. C.; Verdier, S.; Christensen, P.; Christensen, J. H., Complementary Analysis of the Water-Soluble and Water-Insoluble Fraction of Catalytic Fast Pyrolysis Biocrudes by Two-Dimensional Gas Chromatography. Energy Fuels 2018, 32, (5), 5960-5968. 15. Mohan, D.; Pittman, C. U.; Steele, P. H., Pyrolysis of wood/biomass for bio-oil: A critical review. Energy Fuels 2006, 20, (3), 848-889. ACS Paragon Plus Environment
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31. Mante, O. D.; Dayton, D. C.; Carpenter, J. R.; Wang, K.; Peters, J. E., Pilot-scale catalytic fast pyrolysis of loblolly pine over γ-Al2O3 catalyst. Fuel 2018, 214, 569-579. 32. Christensen, J. H.; Tomasi, G.; Hansen, A. B., Chemical fingerprinting of petroleum biomarkers using time warping and PCA. Environ. Sci. Technol. 2005, 39, (1), 255-260. 33. Christensen, J. H.; Tomasi, G.; de Lemos Scofield, A.; de Fatima Guadalupe Meniconi, M., A novel approach for characterization of polycyclic aromatic hydrocarbon (PAH) pollution patterns in sediments from Guanabara Bay, Rio de Janeiro, Brazil. Environ. Pollut. 2010, 158, (10), 3290-3297. 34. Tomasi, G.; Savorani, F.; Engelsen, S. B., icoshift: An effective tool for the alignment of chromatographic data. J. Chromatogr. A 2011, 1218, (43), 7832-7840. 35. Nielsen, N.-P. V.; Carstensen, J. M.; Smedsgaard, J., Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping. J. Chromatogr. A 1998, 805, (1), 17-35. 36. Skov, T.; van den Berg, F.; Tomasi, G.; Bro, R., Automated alignment of chromatographic data. J. Chemom. 2006, 20, (11-12), 484-497. 37. Bocker, S.; Letzel, M. C.; Liptak, Z.; Pervukhin, A., SIRIUS: decomposing isotope patterns for metabolite identification. Bioinformatics 2009, 25, (2), 218-224. 38. Kim, S.; Thiessen, P. A.; Bolton, E. E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B. A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S. H., PubChem Substance and Compound databases. Nucleic Acids Res. 2016, 44, (Database issue), D1202-D1213. 39. Kanehisa, M.; Sato, Y.; Kawashima, M.; Furumichi, M.; Tanabe, M., KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016, 44, (D1), D457-D462. 40. Williams, A. J.; Grulke, C. M.; Edwards, J.; McEachran, A. D.; Mansouri, K.; Baker, N. C.; Patlewicz, G.; Shah, I.; Wambaugh, J. F.; Judson, R. S.; Richard, A. M., he CompTox Chemistry Dashboard: a community data resource for environmental chemistry. J. Chemom. 2017, 9, (61), 1-27. 41. Wishart, D. S.; Feunang, Y. D.; Marcu, A.; Guo, A. C.; Liang, K.; Vázquez-Fresno, R.; Sajed, T.; Johnson, D.; Li, C.; Karu, N.; Sayeeda, Z.; Lo, E.; Assempour, N.; Berjanskii, M.; Singhal, S.; Arndt, D.; Liang, Y.; Badran, H.; Grant, J.; Serra-Cayuela, A.; Liu, Y.; Mandal, R.; Neveu, V.; Pon, A.; Knox, C.; Wilson, M.; Manach, C.; Scalbert, A., HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2018, 46, (Database issue), D608-D617. 42. Schymanski, E. L.; Jeon, J.; Gulde, R.; Fenner, K.; Ruff, M.; Singer, H. P.; Hollender, J., Identifying small molecules via high resolution mass spectrometry: communicating confidence. Environ. Sci. Technol. 2014, 48, (4), 2097-2098. 43. Zakzeski, J.; Bruijnincx, P. C.; Jongerius, A. L.; Weckhuysen, B. M., The catalytic valorization of lignin for the production of renewable chemicals. Chem. Rev. 2010, 110, (6), 3552-3599. 44. Laboureur, L.; Ollero, M.; Touboul, D., Lipidomics by Supercritical Fluid Chromatography. Int. J. Mol. Sci. 2015, 16, (6), 13868-13884. 45. Ruttkies, C.; Schymanski, E. L.; Wolf, S.; Hollender, J.; Neumann, S., MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J. Cheminform. 2016, 8, (3), 1-16. 46. Williams, P. T.; Nugranad, N., Comparison of products from the pyrolysis and catalytic pyrolysis of rice husks. Energy 2000, 25, (6), 493-513. 47. Sharma, R. K.; Bakhshi, N. N., Upgrading of Wood-Derived Bio-Oil over Hzsm-5. Bioresour. Technol. 1991, 35, (1), 57-66. 48. Ates, F.; Isikdag, M. A., Influence of temperature and alumina catalyst on pyrolysis of corncob. Fuel 2009, 88, (10), 1991-1997. ACS Paragon Plus Environment
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49. Putun, E., Catalytic pyrolysis of biomass: Effects of pyrolysis temperature, sweeping gas flow rate and MgO catalyst. Energy 2010, 35, (7), 2761-2766. 50. Mante, O. D.; Agblevor, F. A.; McClung, R., A study on catalytic pyrolysis of biomass with Yzeolite based FCC catalyst using response surface methodology. Fuel 2013, 108, (Supplement C), 451464.
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Table 1. Properties of the five CFP biocrudes. Sample
C [wt%] a H [wt%] b O [wt%] c
N [wt ppm] d
S [wt%] e
CFP severity [°C]
Moisture content [wt%] f
Fraction g
Feed 1
73.9
8.3
17.0
708.2
0.02700
520
8.0
Light
Feed 2
72.8
6.7
14.9
628.6
0.00661
465
8.9
Heavy
Feed 3
64.5
7.3
25.5
986.7
0.01081
520
9.4
15% Light + 85% Heavy
Feed 4
62.9
7.0
28.0
966.9
0.01018
520
9.9
15% Light + 85% Heavy
Feed 5
62.6
6.5
28.8
-
0.00582
575
10.4
Heavy
a ASTM D5291; b ASTM D7171; c Perkin Elmer 2400 Series II analyser; d ASTM D5762; e ASTM D4294; f ASTM E203; g Refer to 30, Figure 1.
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Table 2. Candidates for the 24 regions of interest from the pixel-based analysis (Table S4 for further details).
Peak No.
tR [min]a
Observed Theoretical Mass mass [M- mass [M-H]- accuracy H]- [m/z]b [m/z] [ppm]
Molecular formula
Suggested compoundc
Compound class
Confidence leveld
Highest relative abundancee F3, F4 vs. F5
F1 vs. F2
1
0.56
161.0605†
161.0603
1.2
C10H10O2
Cinnamaldehyde
3-(3-Hydroxy-2-methylphenyl)prop-2-enal
3
2a
2.00
179.0710
179.0708
1.1
C10H12O3
Methoxyphenol
Propioguaiacone
3
F3, F4
F2
2b
2.10
165.0553
165.0552
0.6
C9H10O3
Methoxyphenol
Acetovanillone
3
F3, F4
F2
3a
2.41
151.0398
151.0395
2.0
C8H8O3
Methoxyphenol
Iso-vanillin
2
F3, F4
F1
3b
2.52
193.0524†
193.0501
1.6
C10H10O4
Methoxyphenol
1-(3-Hydroxy-4-methoxyphenyl)propane-1,2-dione
4
F3, F4
F1
4a
2.82
135.0811
135.0811
0.0
C9H12O
Alkylphenol
3-Propylphenol
3
F3, F4
F1
2.92
135.0814†
135.0811
2.2
C9H12O
Alkylphenol
2-Propylphenol
3
F3, F4
F1
4c
3.04
149.0970†
149.0966
-2.7
C10H14O
Alkylphenol
2,6-Diethylphenol
3
F3, F4
F1
5
3.96
177.0555
177.0552
1.7
C10H10O3
Methoxyphenol
Coniferyl aldehyde
2
F3, F4
F1
6a
4.41
133.0658†
133.0653
3.8
C9H10O
Alkylphenol
2-Propenylphenol
3
F3, F4
F1
6b
4.50
191.0709
191.0708
0.5
C11H12O3
Methoxy-benzoic acid
4-Methyl-3-prop-1-en-2-yloxybenzoic acid
4
F3, F4
F1
7
4.70
191.0708
191.0708
0.0
C11H12O3
Methoxy-benzoic acid
3-Methyl-5-prop-2-enoxybenzoic acid
4
F3, F4
F1
8a
5.29
255.2322†
255.2324
-0.8
C16H32O2
Fatty acyl
Palmitic acid
4
F2
8b
5.44
269.2479
269.2481
-0.7
C17H34O2
Fatty acyl
Margaric acid
4
F2
8c
5.50
283.2636
283.2637
-0.4
C18H36O2
Fatty alcohol
(E,5S,8S)-Octadec-6-ene-5,8-diol
3
F2
5.69
281.2484†
281.2481
1.1
C18H34O2
Fatty acid
7-Octadecanoic acid
3
F2
5.90
279.2329†
279.2324
1.8
C18H32O2
Fatty acid
Linoleic acid
1
F2
10
7.06
135.0451†
135.0446
3.7
C8H8O2
Hydroxyphenol
4-(1-Hydroxyvinyl)phenol
4
11
7.89/ 7.96/ 8.35
301.2163†
301.2168
-1.7
C20H30O2
Diterpenoid/ Fatty acid
(+)-Pisiferol/ (E)-7-[(1S,5Z)-5-[(E)-oct-2enylidene]cyclopent-2-en-1-yl]hept-5-enoic acid/ 2Butyl-10-phenyldec-6-enoic acid
12
8.98
227.1074†
227.1072
0.9
C15H16O2
Methoxystilbenoid
13
9.18
299.2013†
299.2011
3.3
C20H28O2
14
9.69
163.0763†
163.0759
2.5
149.0606†
149.0603
2.0
4b
8d 9
15a/ b 9.94/ 10.17
F1
F5
4/ 4/ 4
F2
4-[2-(4-Methoxyphenyl)ethyl]phenol
3
F1
Methoxy-benzoic acid
1-[3-(3,3-dimethylcyclohexyl)phenyl]-cyclopentane1-carboxylic acid
4
F2
C10H12O2
Carbonylphenol
1-(3-Hydroxy-4-methylphenyl)propan-2-one
4
F5
F1
C9H10O2
Catechol/ Methoxyphenol 4-Allylcatechol/ 2-Methoxy-4-vinylphenol
4/ 3
F5
F1
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16a
10.24
135.0451†
135.0446
3.7
C8H8O2
Catechol
Vinylcatechol
4
F5
F1
16b
10.47
135.0448†
135.0446
1.5
C8H8O2
Carbonylphenol
3-Hydroxyacetophenone
3
F5
F1
17
10.60
121.0291†
121.0290
0.8
C7H6O2
Carbonylphenol
3-Hydroxybenzaldehyde
3
F5
F1
18/ 19 10.85/ 11.79 161.0604†
161.0603
0.6
C10H10O2
Hydroquinone/ Carbonylphenol
2,3-Bis(ethenyl)benzene-1,4-diol/ 3-(3Hydroxyphenyl)but-1-en-1-one
4/ 4
F5/ F3, F4
F1
20/ 21 12.02/ 12.40 147.0447†
147.0446
0.7
C9H8O2
Cinnamaldehyde
m-Coumaraldehyde/ p-Coumaraldehyde
4/ 4
F3, F4
F1
22
12.88
225.0916†
225.0916
0.0
C15H14O2
Methoxyphenol (2 rings) 4-[1-(4-Methoxyphenyl)vinyl]phenol
4
F1
23
14.33
249.1285†
249.1279
2.4
C18H18O
Alkylphenol
4-Prop-2-enyl-2-(3-prop-2-enylphenyl)phenol
4
F1
24
14.82
287.0926†
287.0919
3.1
C16H16O5
Dihydroxybenzaldehyde derivative
1-(2,4-dihydroxyphenyl)-2-(2,4-dimethoxyphenyl) ethanone
4
F3, F4
F2
a)
Retention times extracted from highest loading coefficients from the pixel-based analysis; b) Masses extracted from raw data, c) Suggested structure after search with CSI:FingerID in numerous databases; based on chromatographic information and compared to literature; d) According to 42 e) Qualitative information from pixel-based analysis, i.e. loading coefficients in each sub-model F3, F4 vs. F5 and F1 vs. F2, †) Base peak ion.
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