Bio-Oil from Waste: A Comprehensive Analytical Study by Soft

Article pubs.acs.org/EF

Bio-Oil from Waste: A Comprehensive Analytical Study by Soft-Ionization FTICR Mass Spectrometry Stefano Chiaberge,*,† Irene Leonardis,‡ Tiziana Fiorani,† Pietro Cesti,† Samantha Reale,*,‡ and Francesco De Angelis‡ †

Centro Ricerche per le Energie Non Convenzionali, Istituto Eni Donegani, via Fauser 4, 28100 Novara, Italy Dipartimento di Scienze Fisiche e Chimiche e Consorzio INCA, Università degli Studi dell’Aquila, via Vetoio, 67100 L’Aquila, Italy

‡

S Supporting Information *

ABSTRACT: Organic solid wastes are potential feedstocks for the production of liquid biofuels, which could be suitable alternatives to fossil fuels for the transport and heating sectors and for industrial use as well. By hydrothermal liquefaction (HTL), the wet biomass is partially transformed into a water-immiscible oil-like organic matter called bio-oil. In this study, three different mass spectrometric ionization techniques, namely, ESI, APCI, and APPI, in combination with a high-resolution FTICR mass analyzer were used in a comparative approach for the characterization of HTL bio-oil. In terms of the number of assigned molecular formulas, the three ionization techniques gave comparable results but with different distributions of the molecular classes. APPI, in particular, was demonstrated to be the ionization technique that best fits the actual elemental composition of the bio-oil sample. Our results, obtained by the integration of the three mass spectrometric ionization techniques, offer the opportunity to detect and identify by FTICR mass spectrometry the heteroaromatic compounds in bio-oil. Both aromatic molecules and nitrogen-containing species raise concern for the subsequent upgrading process of the bio-oil into a diesel-like fuel.

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INTRODUCTION Biofuel production is becoming increasingly important because of the need to reduce environmental contamination and dependence on fossil fuels and for its potential to generate economic value from waste residues.1 Furthermore, the production and consumption of biofuels generate in total less greenhouse gas emissions than fossil fuels and can even be greenhouse-gas-neutral if efficient methods for production are employed.2 In order to go toward sustainable development, several biomass sources (including wood and wood wastes, energy crops, aquatic plants, agricultural crops and their waste byproducts, and municipal and animal wastes) are currently being studied as potential sources of biofuels.3,4 Liquefaction and pyrolysis are the two major technologies to produce bio-oils. Pyrolysis oils are water-soluble and have a higher oxygen content, and therefore a lower energy content, than liquefaction-derived oils.5 The hydrothermal liquefaction (HTL) process is one of the most promising methods for converting biomass wastes into biofuels since water at high temperature and pressure has remarkable properties as a reaction medium.3 Moreover, this process does not require feedstock drying, which is an expensive and energy-consuming process. Besides, the organic fraction of municipal solid waste has proved to be an ideal feedstock for bio-oil production, being an already collected, readily available material and, on the other hand, a highly perishable wet biomass.6 The wet biomass, once subjected to HTL, is partially transformed into a water-immiscible oil-like organic phase called bio-oil. The chemical composition of bio-oils is determined by the nature of the original biomass, and consequently, the more complex is the original biomass composition, the more complex will be the resulting bio-oil. The complexity of bio-oil originates © 2014 American Chemical Society

from a series of multicomponent reactions that transform the waste biomass (mainly carbohydrates, lipids, and proteins)7−10 in an organic mixture formed by a great variety of components. The ultrahigh resolution of Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), with its unsurpassed mass resolution and mass accuracy, enables molecularlevel analysis of complex mixtures11−22 that can be correlated with bulk measurements.23,24 Thanks to the high resolution and mass accuracy, the unique elemental composition (CcHhNnOoSs) can be generated for each mass spectrometric peak.25,26 Chemical characterization of bio-oil composition is fundamental in order to proceed to the subsequent bio-oil upgrading steps to improve its technological properties and thus convert it into a valuable energy source. We recently used a combined approach of atmosphericpressure photoionization (APPI) FTICR-MS, GC−MS, 1H and 13 C NMR spectroscopy, and elemental analysis to provide a molecular-level description of the HTL bio-oil obtained from wet, urban biomasses.11 In the present work, we implemented the molecular description of the bio-oil by integrating our APPI mass spectrometric results with the other commonly used mass spectrometric ionization methods,27 namely, electrospray ionization (ESI) and atmospheric-pressure chemical ionization (APCI). Complex samples such as bio-oils contain a wide range of constituents,1,11,28−30 and their analysis by a single mass spectrometric ionization technique can indeed result in an incomplete or even misleading description.27 Each ionization Received: December 12, 2013 Revised: February 17, 2014 Published: February 19, 2014 2019

dx.doi.org/10.1021/ef402452f | Energy Fuels 2014, 28, 2019−2026

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Figure 1. Low-resolution (linear ion trap) positive-ion mass spectra of the bio-oil sample using (a) ESI, (b) APCI, (c) and APPI. The total ion currents obtained for the ESI, APCI, and APPI techniques were 1.19 × 108, 4.24 × 108, and 3.03 × 108, respectively. Sample Preparation for FTICR-MS Analysis. The bio-oil (10 mg) was dissolved in CH2Cl2 (1 mL). For ESI and APCI analyses, the stock solution (100 μL) was further diluted with methanol to a final concentration of 0.5 mg mL−1. For APPI measurements, the stock solution was diluted with a 50:50 (v/v) toluene/methanol solution to a final concentration of 0.5 mg mL−1. Toluene acts both as a solvent and as dopant in the ionization process.32 FTICR-MS Analysis. MS analyses were performed on a 7 T FTICR-MS instrument (LTQ-FT Ultra, Thermo Scientific) equipped with ESI, APCI, and APPI ion sources. The mass spectra were collected in positive mode using ESI, APCI, and APPI after the optimization of the conditions for each ion source. For the ESI measurements, the sample was infused at a flow rate of 5 μL min−1. Typical ESI(+) conditions were as follows: source voltage, 3.5 kV; capillary voltage, 43 V; tube lens voltage, 130 V; capillary temperature, 275 °C; sheath gas, 10 (arbitrary units), auxiliary gas, 5 (arbitrary units). For the APPI and APCI measurements, the sample was infused at a flow rate of 50 μL min−1 under the following conditions: capillary temperature, 275 °C; vaporizer temperature, 350 °C; sheath gas, 50 for APCI and 60 for APPI (arbitrary units); tube lens voltage, 105 V for

method has its specific application according to the molecular weight, polarity, and physico-chemical properties of the analytes. For this reason, the elaboration of data sets obtained by only a single ionization method gives only a partial description of the sample. We show here that comparison and integration of more than one mass spectrometric ionization technique results in the determination of a larger range of compounds, thus providing a more detailed characterization of the bio-oil sample.

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MATERIALS AND METHODS

Bio-oil. The bio-oil sample was prepared by HTL of the organic fraction of readily available municipal solid wastes, which were treated at 310 °C for 1 h as described previously.31 Elemental Analysis. The bulk elemental composition of the biooil, expressed as C, H, N, and S contents, was determined using a ThermoQuest EA1100 elemental analyzer. Each sample was analyzed in triplicate. The oxygen content was determined by difference. 2020


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Figure 2. High-resolution FTICR mass spectra of bio-oil using (a) ESI, (b) APCI, and (c) APPI in positive-ion mode. APCI and 65 V for APPI; capillary voltage, 43 V for APCI and 19 V for APPI. The APCI corona discharge current was set to 5 μA. The spectra were acquired both with a low-resolution linear ion trap in full scan centroid mode (scan range of m/z 100−1000) and with a 7 T ultrahigh-resolution FTICR cell with a mass range of m/z 100− 1000. Ion trap parameters were as follows: number of microscans, 5; maximum injection time, 10 ms; normal scan rate; automatic gain control (AGC), 3 × 104; dynode voltage, −15 kV. For the FTICR instrument, the resolution was set to 400 000 (at m/z 400) and the ion accumulation time, defined by the AGC, was set to 106. A minimum of 100 scans were collected and averaged for each analysis to improve the signal-to-noise ratio. Data Analysis. Data were processed using the Xcalibur software (Thermo Scientific) after setting the following restrictions to the element ranges: 12C10−60 13C0−2 H10−100 N0−6 32S0−2 34S0−1 O0−6, with the error range set at ±2.5 ppm. These restrictions are required because of the great number of possible different combinations of elements that can be generated from a single accurate mass. The molecular formulas were assigned to the peaks presenting magnitudes above the threshold level of 3σ (σ = standard deviation) of the baseline

noise. They were then considered in the following data evaluation process. The first step of molecular formula assignment was done below 400 Da, since in this interval the assignments are more reliable because of the lower number of possible combinations for a single mass. Second, the peaks at higher mass (above 400 Da) were assigned through the Kendrick mass.33 The lists of masses and the corresponding molecular formulas were then grouped using custom-designed software (ISOMASS),18 and the mass peaks relevant to isotopic distributions were identified and deleted. The relevant signals were categorized according to different parameters, such as the number of heteroatoms (N, O, and S) and the number of unsaturations [expressed as the number of double bond equivalents (DBE)].34−39 For each molecular formula, the DBE was calculated according to the following equation (for CcHhNnOoSs):

DBE = c −

h n + +1 2 2

Molecular formulas were assigned to approximately 90% of the peaks presenting relative intensities higher than 0.2%. From the lists of 2021


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peaks, the ISOMASS software18 was also used to calculate the weighted-average molecular weight (AMW) of the sample at both low and high resolution. The elemental compositions (C, H, N, O, S) for the lists of the assigned molecular formulas derived from the different ionization methods were calculated as nonweighted and weighted averages. Elemental compositions as nonweighted averages (Mn)27 were calculated using eq 1: Mn =

∑ MiNi/∑ Ni

more than 90% of the experimental peaks of the three mass spectra (see Materials and Methods), and 2042, 2356, and 2247 different molecular formulas were assigned to the ESI, APCI, and APPI mass spectra, respectively. The differences among the numbers of molecular formulas for the various ionization techniques are rather small, with only a difference of ca. 13% between the lowest number (2042 assigned formulas by ESI) and the highest one (2356 assigned formulas by APCI). Most likely, the relatively low number of assigned molecular formulas in the ESI spectrum depends on the low abundance of polar species in HTL bio-oils,11 as the ESI technique more effective for their ionization. Polar species, in fact, concentrate in the aqueous phase during the separation of the organic phase (biooil) at the end of the HTL process (Figure S1 in the Supporting Information).31 Besides, the Venn diagram27 in Figure 3 clearly shows the areas where the three techniques overlap in terms of assigned molecular formulas.

(1)

where Mi = cC × 12/Mtheoretical in the case of carbon atoms and Ni is the number of assigned peaks within the corresponding measurement. Elemental compositions as weighted averages (Mw) were calculated using eq 2: Mw =

∑ niWi

(2)

where Wi is the atomic mass of the most abundant isotope (12 for C, 1.008 for H, 14.003 for N, 15.995 for O) and ni = nXIrel/∑Irel (i.e., the number of atoms of element X in each molecular formula multiplied by the weighted relative intensity of the corresponding peak).

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RESULTS AND DISCUSSION The mass spectra of the bio-oil sample were compared in the positive-ion mode only. Negative ionization, in fact, allowed the detection of only fatty acids, generating almost complete signal suppression for the other compounds. Indeed, fatty acids are very abundant in bio-oils obtained by liquefaction,11 and they derive from the transformation of lipidic components of the original biomass. The most abundant fatty acids found were those with 16 and 18 carbon atoms with different amounts of unsaturation, mainly C18:1, C18:2, C16:0, and C18:0 (according to their relevant abundances). Their total concentration in the bio-oil sample was found to be about 10% by weight, as determined by GC−MS measurement.11 The positive-ion-mode ESI, APCI, and APPI mass spectra obtained by low-resolution linear ion trap instruments are shown in Figure 1. All the three mass spectra show signals distributed over the whole acquisition range according to a Gaussian shape in the mass range between m/z 100 and 1000 that is distorted toward high masses. This distortion is particularly evident in the ESI mass spectrum (Figure 1a), where a series of partially overlapped additional Gaussian distributions appear, spanning in the mass range between m/z 100 and 400. The APCI and APPI mass spectra (Figure 1b,c, respectively) show some intense peaks in the mass range between m/z 250 and 400, relevant to fatty acid amides,40 which emerge from the average distribution. These amides have been already extensively described,40 and they are formed by condensation reactions between the decomposition products of amino acids and fatty acids that take place during the HTL process. The AMWs calculated from the low-resolution mass spectra were 378.1, 426.3, and 419.8 for ESI, APCI, and APPI, respectively. The high-resolution mass spectra are reported in Figure 2. Once again the ESI mass spectrum (Figure 2a) shows a series of Gaussian-shaped peak distributions in the low mass range, whereas the fatty acid amides are more easily revealed by the APCI and APPI techniques (Figure 2b,c) as relatively more intense peaks. The AMWs for the high-resolution mass spectra were calculated as 332.5, 357.0, and 341.6 for ESI, APCI, and APPI, respectively. In both the low- and high-resolution analyses, ESI gave a slightly lower AMW value. The high resolution and mass accuracy of the FTICR measurement allowed molecular formulas to be assigned to

Figure 3. Venn diagram scaled with respect to the number of assigned molecular formulas according to the ionization technique employed. The total number of assigned molecular formulas derived from unifying the three sets of data is 3178.

The majority of the formulas (1309 formulas, i.e, 55% for APCI, 58% for APPI, and 64% for ESI) are common to the three techniques, suggesting that all of them can be successfully used for rather detailed characterization of bio-oils. Furthermore, it clearly appears that APCI and APPI give similar results, not only in terms of absolute number of assigned molecular formulas but also because they have 1907 formulas in common (of which 598 are unique to APPI and APCI). Conversely, ESI shares 1350 formulas with APPI (of which only 41 are unique to ESI and APPI) and 1519 with APCI (of which 210 are shared by only ESI and APCI). The unique assignments for each ionization technique can give information on how specific and discriminating each ionization method is. On this basis, the most discriminating ionization technique appears to be ESI, since 482 formulas were assigned exclusively in the ESI spectrum (24% of the total number of ESI-assigned formulas). In the APPI spectrum we found 299 unique assignments (13% of the total number of APPI-assigned formulas) and in the APCI spectrum 239 (10% of the total number of APCI-assigned formulas). Remarkably, the total number of assigned molecular formulas deriving from unifying the three sets of data is 3178. The assigned molecular formulas (without considering the isotopic distributions) were grouped into classes according to their heteroatom contents (see Materials and Methods for details).34−39 Their relative contributions in the bio-oil sample, 2022


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Figure 4. Plots of DBE vs nC for the molecular formulas present exclusively in the ESI spectra of classes N2 (left) and N3 (right). The grayscale code is used for relative abundance within the specific class.

together with their DBEs and AMWs, are reported in Table S1 in the Supporting Information. Noticeably, the 1309 molecular formulas common to the three ionization techniques (Figure 3) mostly belong to the O1N1, O1N2, and O2N2 classes, which are the most abundant classes. As to the 482 molecular formulas uniquely detected by the ESI technique, they are more than 70% distributed in the mass range between m/z 300 and 600 (Figure S2 in the Supporting Information), this percentage pertaining mainly to the N2 and N3 classes. The distributions of these two classes can be represented in a graph where DBE is plotted versus the number of carbons (nC), with the relative abundance being expressed as a grayscale code11 (Figure 4). N2 and N3 formulas unique to ESI are placed in a lower DBE region compared with the formulas shared with APCI and APPI (Figure S3 in the Supporting Information). In particular, the most abundant N2 peaks belong to the series of homologues characterized by DBE = 3 and nC = 10−14. These peaks can be related to imidazole compounds with alkyl side chain substituents.11 In addition, the most populated ESI N3 series of homologues are related to DBE = 6 and nC ≈ 14− 18 and to DBE = 11 and nC ≈ 30−35. Accordingly, these values indicate the presence of highly unsaturated nitrogen heteroaromatic compounds. The 239 molecular formulas uniquely present in the APCI plots are concentrated in the mass range between m/z 400 and 600 (about 72%; Figure S2 in the Supporting Information). O1N1, O1N2, and O2N1 are the most abundant classes in this range. Conversely, the 299 unique formulas found in APPI are mainly focused in two mass ranges: the first, between m/z 200 and 300, accounts for 22% and is due mainly to the CH and O2N1 classes; the second, between m/z 500 and 600, represents 28% and is related to the O2N2 and O3N3 classes. These distributions suggest that a part of the N- and Ocontaining classes can be ionized by all the three ionization techniques while another part of them, generally at higher molecular weights, presents characteristics that make them ionizable only by APCI and APPI. On the other hand, the ESI technique, which works better with more polar molecules, is more effective at lower molecular weights, where the N/C ratio becomes higher (see the Van Krevelen plot41,42 of H/C vs N/C in Figure S4 in the Supporting Information). The relative abundances of the various classes are reported in Figure 5. O1N1 is the most abundant one with both APCI and APPI, probably because of the high content of fatty acid

Figure 5. Relative abundances of the most abundant molecular classes identified in the bio-oil sample by the three ionization techniques.

amides, which can be more efficiently detected with these techniques. A similar situation holds for all of the O−N classes. Conversely, the N2 class (and the Nx classes in general) appear to be very sensitive to investigation by ESI, thus giving a clear indication of their possible basicity. The differences between the molecular classes related to the ionization efficiency were overcome by reporting in a bar chart the number of assigned molecular formulas instead of the relative abundances (Figure S6 in the Supporting Information).27 Although there is not a complete correspondence between this data elaboration and the one reported in Figure 5, a general trend for the three ionization techniques is confirmed: ESI has a higher population of N-containing compounds, whereas APCI and APPI, which have similar distributions, have a higher number of peaks belonging to classes with higher numbers of oxygen atoms, in particular O2N1 and O2N2. Furthermore, focusing the attention on these two rather abundant classes, it is possible to compare their distributions by plotting DBE versus nC, with the relative abundance being expressed as a grayscale code (Figure 6). Concerning the O2N1 class (Figure 6, left), even though the average DBEs obtained with the three techniques are almost the same (between 7 and 8; Table S1 in the Supporting Information), the distributions 2023


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Figure 6. Plots of DBE vs nC for the (left) O2N1 and (right) O2N2 classes by (top) ESI, (middle) APCI, and (bottom) APPI ionization. The grayscale code is used for relative abundance within the specific class.

are different. In particular, two main distribution areas are present. In the first one, present for all three ionization techniques, nC = 10−25 and DBE = 5−14. This distribution is likely related to heteroaromatic compounds, since DBE values higher than 4 can be conceivably related to aromatic molecules.43 The other distribution area, characterized by a DBE = 3−12 and nC = 24−30, is present only in the APCI and APPI graphs. A similar situation is reported for the O2N2 class (Figure 6, right), where two relatively important, partially superimposed mass distributions (one with DBE = 7−17 and nC = 16−35 and the second with DBE = 2−10 and nC = 25−35) are present in the APCI and APPI graphs, whereas in the ESI graph only the first one is present. Independent of the specific ionization

technique employed, these distributions are shifted to higher DBE and nC values than those observed for the O2N1 class. This behavior is probably due to the presence of the additional nitrogen atom included in unsaturated hydrocarbon structures. Remarkably, a third distribution appears in the APPI plot, characterized by a relatively high nC ranging between 34 and 44 and low DBE values centered at 2. We consider that such a pattern could be generated by gas-phase dimerization of the fatty acid amides (belonging to the O1N1 class) present in the bio-oil, each of them being described by 1−2 double bonds and 16−22 carbon atoms. The molecular masses of such compounds, as they appear in the HR-FTMS spectra, agree with eq 3: 2024


Energy & Fuels Madduct = MX + MY + M H+

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bio-oil by weight,11 are preferentially ionized in negative ion mode. Figure 8 shows the weighted elemental distributions given by ESI, APCI, and APPI in comparison with the bulk elemental

(3)

where MH+ is the mass of the proton (1.007321 amu) and MX and MY are the accurate molecular weights of the two fatty acid amides making up the cluster, which are also present in the HRFTMS spectra in the O1N1 class. The calculated distribution of these molecules for all of the fatty acid amides is reported for comparison in Figure S5 in the Supporting Information. The dimerization was also confirmed by tandem mass spectrometry and ion source fragmentation experiments (data not shown). A very interesting observation comes from a comparison of the elemental compositions of the bio-oil obtained by the three mass spectrometric ionization techniques (see Materials and Methods) and that obtained by bulk elemental analysis.27,44 The elemental compositions from mass spectrometric data were calculated both as “nonweighted elemental distributions” (i.e., not considering the intensities of the mass peaks)27 and as “weighted elemental distributions”. In Figure 7 the three

Figure 8. Calculated weighted elemental distributions obtained by the ESI, APCI, and APPI methods and the bulk elemental analysis data (wt %) of the bio-oil.

composition. Once more, the best match was obtained for all the elements by APPI, which gives the following elemental distribution (wt %): C 78.44, H 9.79, N 6.01, and O 5.76. Remarkably, except for the O content, the results obtained here by considering the relative abundances of the identified molecules are closer to the bulk elemental composition than the results obtained with the nonweighted data analysis. Despite the fact that each ionization method is more efficient for specific compounds and a part of the sample was not detected at all by FTICR measurements, a good match between the calculated and bulk elemental compositions was obtained. This might be due to two possible reasons: FTICR is able to detect the most abundant compounds, and the compounds not detected by FTICR have a similar elemental composition to the detected ones.

Figure 7. Calculated nonweighted elemental distributions obtained using the ESI, APCI, and APPI methods and the bulk elemental analysis data (wt %) of the bio-oil.

nonweighted elemental distributions derived from the ESI, APCI, and APPI mass spectra and the bulk elemental composition are reported in a comparative way. Independent of the specific ionization technique employed, the calculated elemental composition is quite similar to the bulk elemental analysis of the bio-oil (wt %): C 76.90, H 9.70, N 5.20, S 0.15. The O content of 7.31% was calculated by difference. In the case of C, all three ionization methods overestimate its content, with the APPI technique giving the closest value (78.45%). An opposite trend was observed for H, with the ESI technique giving the best match. It could be concluded that all three ionization techniques slightly favor structures with higher aromaticity, since the C/H ratio obtained is higher than that of the bulk composition. Concerning the N content, while APCI and APPI give values of 6.82 and 6.61%, respectively (5.20% is the bulk elemental value), the ESI data overestimate its content as 7.87%, probably as a consequence of the relatively high proton affinities of nitrogen-containing compounds.27 Concerning O, the ESI technique gives the lowest value (3.85%), while APCI (5.11%) and APPI (5.93%) seem to be more reliable methods (7.31% is the bulk value). Most likely, the underestimation of the O content may be due to the fact that we considered the positive ion mode only. Indeed, fatty acids (belonging to the O2 class), which represent about 10% of the

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CONCLUSIONS Chemical characterization of a complex mixture is a hard issue, and a single analytical approach cannot give a complete description of the sample. In this work, three different mass spectrometric ionization techniques, namely, ESI, APCI, and APPI, in combination with a high-resolution FTICR mass analyzer were used in a comparative way to characterize a waste biomass HTL bio-oil. In terms of the number of assigned molecular formulas, the three ionization techniques gave comparable results, but with different distributions of the molecular classes. In particular, the APPI and APCI results were found to be very similar to each other, while ESI was revealed to be much more sensitive to molecules with more polar features, characterized by higher N/ C ratios. Remarkably, the integration of the results derived from the ESI, APPI, and APCI analyses was able to cover a wider range of molecular species. APPI in particular was found to be the ionization technique that best fits the actual elemental composition of the bio-oil sample. As to the reliability of the mass spectrometric data, in fact, it can be considered that APPI, in comparison with the other ionization techniques, is only 2025


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(15) D’Andrilli, J.; Dittmar, T.; Koch, B. P.; Purcell, J. M.; Marshall, A. G.; Cooper, W. T. Rapid Commun. Mass Spectrom. 2010, 24, 643− 650. (16) Smith, E. S.; Lee, Y. J. Energy Fuels 2010, 24, 5190−5198. (17) Klein, G. C.; Angström, A.; Rodgers, R. P.; Marshall, A. G. Energy Fuels 2006, 20, 668−672. (18) Chiaberge, S.; Guglielmetti, G.; Montanari, L.; Salvalaggio, M.; Santolini, L.; Spera, S.; Cesti, P. Energy Fuels 2009, 23, 4486−4495. (19) Purcell, J. M.; Hendrickson, C. L.; Rodgers, R. P.; Marshall, A. G. Anal. Chem. 2006, 78, 5906−5912. (20) Hughey, C. A.; Rodgers, R. P.; Marshall, A. G. Anal. Chem. 2002, 74, 4145−4149. (21) Shaub, T. M.; Hendrickson, C. L.; Horning, S.; Quinn, J. P.; Senko, M. W.; Marshall, A. G. Anal. Chem. 2008, 80, 3985−3990. (22) Kujawinski, E. B.; Hatcher, P. G.; Freitas, M. A. Anal. Chem. 2002, 74, 413−419. (23) Hsu, C. S.; Hendrickson, C. L.; Rodgers, R. P.; McKenna, A. M.; Marshall, A. G. J. Mass. Spectrom. 2011, 46, 337−343. (24) Savory, J. J.; Kaiser, N. K.; McKenna, A. M.; Xian, F.; Blakney, G. T.; Rodgers, R. P.; Hendrickson, C. L.; Marshall, A. G. Anal. Chem. 2011, 83, 1732−1736. (25) Cunico, R. L.; Sheu, E. Y.; Mullins, O. C. Pet. Sci. Technol. 2004, 22, 787−798. (26) Purcell, J. M.; Mendrignac, I.; Rodgers, R. P.; Marshall, A. G.; Gauthier, T.; Guibard, I. Energy Fuels 2010, 24, 2257−2265. (27) Gaspar, A.; Zellermann, E.; Lababidi, S.; Reece, J.; Schrader, W. Anal. Chem. 2012, 84, 5257−5267. (28) Sipilä, K.; Kuoppala, E.; Fogernäs, L.; Oasmaa, A. Biomass Bioenergy 1998, 14, 103−113. (29) Bertero, M.; de la Puente, G.; Sedran, U. Fuel 2012, 95, 263− 271. (30) Bertero, M.; Gorostegni, H. A.; Orrabalis, C. J.; Guzmán, C. A.; Calandri, E. L.; Sedran, U. Fuel 2014, 116, 409−414. (31) Bianchi, D.; Bosetti, A.; Franzosi, G.; Ricci, M. (Eni SpA). World Intellectual Property Organization WO 2011/030196 A1, 2011. (32) Robb, D. B.; Covey, T. R.; Bruins, A. P. Anal. Chem. 2000, 72, 3653−3659. (33) Kendrick, E. Anal. Chem. 1963, 35, 2146−2154. (34) Barrow, M. P.; Headley, J. V.; Peru, K. M.; Derrick, P. J. Energy Fuels 2009, 23, 2592−2599. (35) Stanford, L. A.; Kim, S.; Klein, G. C.; Smith, D. F.; Rodgers, R. P.; Marshall, A. G. Environ. Sci. Technol. 2007, 41, 2696−2702. (36) Fu, J.; Klein, G. C.; Smith, D. F.; Kim, S.; Rodgers, R. P.; Hendrickson, C. L.; Marshall, A. G. Energy Fuels 2006, 20, 1235−1241. (37) Stanford, L. A.; Kim, S.; Rodgers, R. P.; Marshall, A. G. Energy Fuels 2006, 20, 1664−1673. (38) Panda, S. K.; Andersson, J. T.; Schrader, W. Anal. Bioanal. Chem. 2007, 389, 1329−1339. (39) Purcell, J. M.; Juyal, P.; Kim, D. G.; Rodgers, R. P.; Hendrickson, C. L.; Marshall, A. G. Energy Fuels 2007, 21, 2869−2874. (40) Chiaberge, S.; Leonardis, I.; Fiorani, T.; Bianchi, G.; Cesti, P.; Bosetti, A.; Crucianelli, M.; Reale, S.; De Angelis, F. Energy Fuels 2013, 27, 5287−5297. (41) Kim, S.; Kramer, R. W.; Hatcher, P. G. Anal. Chem. 2003, 75, 5336−5344. (42) Wu, Z.; Rodgers, R. P.; Marshall, A. G. Anal. Chem. 2004, 76, 2511−2516. (43) Shaub, T. M.; Hendrickson, C. L.; Quinn, J. P.; Rodgers, R. P.; Marshall, A. G. Anal. Chem. 2005, 77, 1317−1324. (44) Kovács, K.; Gáspár, A.; Sajgó, Cs.; Schmitt-Kopplin, Ph.; Tombácz, E. Eur. J. Mass Spectrom. 2010, 16, 625−630.

slightly affected by a nonlinear correlation between the mass peak intensity and the relevant abundance of the neutral precursors in the sample. Finally, our results obtained by the integration of the three mass spectrometric ionization techniques offer the opportunity to characterize the heteroaromatic component of the bio-oil at the molecular level. This is extremely important in view of the upgrading process of the bio-oil into a valuable fuel, where the overall content of both heteroatoms and aromatic molecules must be drastically reduced by the employment of a suitable catalytic hydrotreatment.

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ASSOCIATED CONTENT

* Supporting Information S

Product separation procedure for the HTL process; relative abundances, DBEs, and AMWs of the main molecular classes identified in the bio-oil sample; unique assigned molecular formulas for ESI, APCI, and APPI; common N2 and N3 assigned molecular formulas for ESI, APCI, and APPI; Van Krevelen plot of H/C vs N/C obtained from ESI, APCI, and APPI; plot of DBE vs nC for O2N2 compounds; and population-based class distributions for the three ionization techniques. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected] (S.C.). *E-mail: [email protected] (S.R.). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Financial support by Eni S.p.A. (Contracts 3500004922 and 3500029048) is gratefully acknowledged by the University of L’Aquila authors.

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REFERENCES

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