Article pubs.acs.org/EF
Toward Controlled Ionization Conditions for ESI-FT-ICR-MS Analysis of Bio-Oils from Lignocellulosic Material Jasmine Hertzog,† Vincent Carré,*,† Yann Le Brech,‡ Anthony Dufour,‡ and Frédéric Aubriet*,† †
LCP-A2MC, FR 2843 Institut Jean Barriol de Chimie et Physique Moléculaires et Biomoléculaires, FR 3624 Réseau National de Spectrométrie de Masse FT-ICR à très haut champ, Université de Lorraine, ICPM, 1 boulevard Arago, 57078 Metz Cedex 03, France ‡ LRGP, CNRS, Université de Lorraine, ENSIC, 1, Rue Grandville, 54000 Nancy, France S Supporting Information *
ABSTRACT: Pyrolysis or liquefaction processes can be applied to lignocellulosic biomass to produce a bio-oil which allows the access of green chemicals or sustainable energy. Among the different existing resources, this raw material has the advantage to come from nonfood feedstocks such as agricultural wastes (wood, grass, ...) or dedicated plantations. Whatever the considered bio-oil, the development of high performance analytical techniques is needed to achieve an exhaustive characterization. The use of Fourier transform ion cyclotron resonance mass spectrometry coupled to electrospray ionization (ESI-FT-ICR-MS) has the potential to chemically identify the components of bio-oil at the level of the molecular formula. In this work, we investigated the influence of the sample preparation (use and nature of dopant and ion detection mode) on the development of a robust methodology for lignocellulosic based bio-oil characterization. Commonly used ESI dopants have been studied to increase the ionization yield and the measurement repeatability. We highlighted the dramatic effect of the sample preparation on the global chemical description of the bio-oil, especially the disproportional contribution of the CxHyN1−5Oz species. Moreover, we demonstrated the ability of well-controlled ESI ionization conditions to attain, on the one hand, specific chemical information on the origin (cellulose, hemicellulose, or lignin) of the bio-oil constituents and, on the other hand, the simultaneous description of both its oily and aqueous compounds without a fractionation step.
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INTRODUCTION The dependence of fossil energies is a critical issue dealing with economy, environment, and geopolitics. Both the global population and the energy demand increase, whereas the total resources decrease. One key is the development of new sustainable and greener sources of energy. Among the renewable energy sources (solar, wind, ocean, ...), the use of the lignocellulosic biomass is an interesting method. The lignocellulosic biomass includes cellulose, hemicellulose (complex carbohydrates), and lignin. Generally, the biomass is used to produce a bio-oil which may be used as a biofuel or which may be refined in classical or specific devices to yield interesting components for the chemical industry.1 Depending on the raw material as well as the used transformation process, different biofuels are achieved. The first generation biofuels are produced from transesterification of vegetable oils (sunflower, rapeseed, oil palm) to yield biodiesel or from fermentation of sugar from sugar cane, sugar beet, wheat, or corn to yield bioethanol. Nevertheless, the production of these bio-oils impacts the foodstuff cultures for human and livestock farming. The third generation bio-oils are from algae or bacteria and are still on laboratory scale and required significant process developments before their use on an industrial stage. At the present time, second generation bio-oils are very promising. Nonfood feedstocks such as bark, grass, agricultural wastes, or wood are specifically used. Their production is based on the liquefaction2−4 or on the pyrolysis5 of biomass or organic wastes, and some semipilot devices already exist. The biomass fast pyrolysis yields more bio-oil than biomass liquefaction (75 and 35 wt %, respectively),6,7 but liquefaction © XXXX American Chemical Society
bio-oils are poorly oxygenated, which is better in terms of energy density.8 The elemental analysis of fast pyrolysis bio-oils demonstrates large amounts of oxygen and traces of nitrogen and sulfur (less than 1%).9 Nevertheless, the high oxygen content is an important hurdle for its use as biofuel due to its corrosive and reactive properties and its low energetic density.9 Bio-oils have to be catalytically upgraded in order to decrease their oxygen content and to increase their energetic density.10,11 Furthermore, bio-oils are composed of thousands of molecular species, and their composition highly depends on biomass composition and process conditions, namely the type of pyrolysis reactor, gas-phase residence time in the pyrolysis reactor, and type of catalysts used.12 Therefore, it is of tremendous importance to comprehensively assess the composition of bio-oils by complementary analytical methods.13−15 Gas chromatography (GC) or 2-dimensional GC are used to study volatile compounds.16−18 The less volatile and more polar compounds may be investigated by liquid chromatography.19,20 Both separation techniques are typically coupled to mass spectrometry and yield qualitative and quantitative information on a restricted number of compounds when a targeted analysis is performed. Nontargeted analysis, such as infrared21 or the NMR ( 1 H and 13 C) 22−24 spectroscopy allows global information on the functional groups to be distinguished. Nevertheless, no information is obtained from an individual Received: March 21, 2016 Revised: June 6, 2016
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DOI: 10.1021/acs.energyfuels.6b00655 Energy Fuels XXXX, XXX, XXX−XXX
Article
Energy & Fuels
line at 350 °C and condensed in three condensers, the first one set at 0 °C and 2 others at −60 °C (with glass beads in order to break aerosols). After weighing, the condensers were rinsed with 20 mL of methanol. This solution is called the “bio-oil solution”. The mass balance of the fast pyrolysis experiment was 31 wt % for char; 56 wt % for total condensable (“bio-oil”), and 9.0 wt % for permanent gas (total 96 wt % of sampled, quantified products). Unfortunately, it is not possible to conduct a CHNOS analysis on the investigated bio-oil. Indeed, it was recovered in methanol, and the elimination of the solvent may also induce the vaporization of highly volatile compounds. This may introduce a bias to the results. However, previous elemental analysis performed on raw Miscanthus bio-oil has shown high oxygen amounts but poor nitrogen ones. Preparation of the Samples. Methanol (99.9%, VWR−Prolabo), formic acid (99%, Biosolve), ammonium hydroxide (99%, VWR− Prolabo), ammonium acetate (99%, Sigma), and sodium acetate (99%, Prolabo) were used as received without further purification or distillation. The bio-oil solution was first diluted by 10 in methanol. The amounts of formic acid and ammonium hydroxide solutions were adjusted to a final concentration of 1% (v/v). The concentration of ammonium acetate was set to 1 mg·mL−1, whereas the sodium acetate concentration was kept to 0.1 mg·mL−1 in the infused bio-oil solution. The increase of sodium acetate concentration did not lead to an increase of the signal relative to the bio-oil component but induced the formation and the detection of [Nan(CH3COONa)m]n+ clusters, which significantly disturbed the analysis. ESI-FT-ICR-MS Analysis. Fourier Transform Ion Cyclotron Mass Spectrometer. ESI-FT-ICR-MS measurements were conducted in both positive and negative ion detection mode on an IonSpec HiRes FT-ICR-MS (Ion Spec, Lake Forest, CA) fitted with a 9.4-T shielded superconducting magnet and a Micromass Z-spray electrospray source. The used high voltage (HV) was ±3500 V. Nitrogen was used to assist solvent evaporation. The sample was infused in the ion source at a flow rate of 3 to 6 μL.min−1. The temperature of the source and probe were both kept to 80 °C. The sample cone voltage was kept constant to ±40 V and the extraction cone one to ±10 V. Extracted ions were accumulated in a RF-only hexapole for a 0.5 to 6 s period of time before to be transferred to the FT-ICR-MS cell. Both infusion flow rate and accumulation time were adjusted to obtain a mass spectrum presenting a constant total ion current (TIC). The ion guide was optimized for m/z 350 ion and allowed ions in the 160−600 m/z range to be efficiently transferred into FT-ICR cell. Ions were trapped in the FT-ICR-MS cell with a ± 0.5 V trapping potential. The trapped ions were then excited by the application of an arbitrary excitation wave function applied on excitation plates. The resulting image current was collected, amplified, digitized, apotized (Blackman) and Fourier transformed to produce a mass spectrum. The signal was sampled during 2 s with 4096 K data points. The average mass measurement accuracy was typically better than 1 ppm, and the mass resolution at m/z 315 close to 400 000. To increase the signal-to-noise ratio, 30 to 140 spectra were accumulated. Calibration, Data Treatment, and Ion Assignment. Before acquisition, the mass spectrometer was externally calibrated by considering well-known ions such as hydride gold cluster ions. After mass spectrum acquisition, internal calibration was performed with a specific and well-characterized CxHyO3,4,5± ion series. A peak list of signals with a S/N > 4.5 was generated, and the Composer software (Sierra Analytics, Modesto, CA) was used for ion assignment with the following search criteria: C1−100H1−100N0−5O1−30Na0−1+ (positive ion) or C1−100H1−100N0−2O1−30S0−1− (negative ion) general formula, 3 ppm tolerance error, and a double bound equivalent (DBE) ranged from −0.5 to 40 (a DBE = −0.5, corresponding to a saturated compound cationized by NH4+). The recalibration of the spectrum is then conducted with signals assigned with an error lower than 1 ppm by considering the following equation:
molecule viewpoint. As previously reported, the global characterization of the bio-oil is of major concern to adapt the operating conditions to the main part of the oxygenated molecules present in the considered bio-oil.15 The nontargeted petroleomic methodology demonstrated its ability to yield the “chemical-signature” of different oils (petroleum25,26 and biooil27,28). The petroleomic approach is generally conducted by ultrahigh resolution mass spectrometry with a Fourier transform ion cyclotron resonance (FT-ICR-MS)29,25 or orbitrap (FT−OrbitrapMS)30 mass spectrometer. The ultrahigh resolution and the accuracy of the mass measurement achieved by these instruments enable discrimination of the thousands of peaks of the mass spectra and assignment of one unique CcHhNnOoSs elemental formula to each detected signal. The combination of the results obtained with different ionization sources in positive and negative ion detection modes allows an exhaustive description of the investigated sample to be achieved.31 More specifically, Olcese et al.10 combined the results obtained by electrospray ionization (ESI) and laser desorption/ionization (LDI) on an FT-ICR-MS to compare raw and treated bio-oils produced by lignin pyrolysis. Chiaberge et al.32 used ESI, atmospheric pressure chemical ionization (APCI), and atmospheric pressure photoionization (APPI) to fully characterize a HTL bio-oil from urban wastes in terms of more or less polar compounds. The ESI appears to be a wellestablished ionization method to investigate the bio-oils whatever their nature or origin.28,33 To increase the efficiency of the analysis, some authors tried to use different dopants to improve the ionization efficiency by cationization or anionization phenomena. For example, Sudasinghe et al.3 added 0.1% formic acid or 0.1% ammonium hydroxide (in volume) to increase the ion signal in positive and negative modes, respectively, when they investigated HTL wood oil by ESIFT-ICR-MS. In that case, protonation and deprotonation were favored. Alternatively, Alsbou et al.31 used sodium chloride, formic acid, and ammonium chloride in positive ion mode and sodium hydroxide and ammonium chloride in negative ion mode. The deprotonated ions were the main detected negative ions, whereas sodiated species dominated the mass spectrum in positive detection mode when NaOH or NaCl were added to the investigated sample. The addition of ammonium chloride ensures the specific formation of ammonium (positive ion) or chloride (negative) adduct with carbohydrate-derived products such as levoglucosan, glucose, and cellobiose.31 In spite of the influence of the sample solution composition on the nature of ESI ions from bio-oil, no systematic study has been undertaken on its dramatic effects on the ionization selectivity of the bio-oil component. Consequently, the purpose of this work is to investigate the influence of the sample preparation (with or without dopant) on the detailed chemical description of one pyrolysis bio-oil by ESI-FT-ICR-MS in both positive and negative ion detection modes. This systematic study enables proposal of a robust and confident method for bio-oil analysis by ESI-MS.
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MATERIALS AND METHODS
Miscanthus Pyrolysis Bio-Oil. Miscanthus was characterized in detail.34 The pyrolysis was conducted in a microfluidized bed reactor set at 500 °C (temperature of the sand); more details on its design can be found in the following reference.35 Two grams of raw miscanthus was injected continuously during 12 min with a microfeeder developed at CNRS-Nancy. The gas-phase residence time in the hot zone was about 1.2 s. The as-produced vapors were sampled through a heated
m A B = + 2 z f f For the nonassigned signals or the assigned signals with an error greater than 1 ppm a manual assignment was conducted using Omega B
DOI: 10.1021/acs.energyfuels.6b00655 Energy Fuels XXXX, XXX, XXX−XXX
Article
Energy & Fuels
Table 1. Total Ion Current (TIC in arbitrary unit), Number of Identified Peaks (#), and Relative Distribution of Detected Species Observed in Positive and Negative ESI-FT-ICR-MS Experiments for the Different Investigated Ionization Conditions Dopant
TIC (a.u)
# Identified peaks
Without Ammonia HCOOH
4625.5 9757.5 588.2
529 920 532
Without
723.7a 8373.3b 2956.5 8397.4 3660.6 8037.9a 9049.7b
413 425 1500 920 1197 858 890
HCOOH AcONH4 Ammonia AcONa a
Oz Negative ions 98% 73% 66% Positive ions 96% 32% 15% 3% 11% 98% 98%
NOz
N2Oz
N3Oz
N4−5Oz
SOz
− 24% −
− 3% −
− − −
− − −
2% − 34%
4% 48% 52% 8% 27% 2% 2%
− 19% 29% 39% 42% − −
−
