Review pubs.acs.org/EF
Overview of Analytical Methods Used for Chemical Characterization of Pyrolysis Bio-oil Martin Staš,*,† David Kubička,† Josef Chudoba,‡ and Milan Pospíšil§ †
Research Institute of Inorganic Chemistry, UniCRE, Záluží 1, 436 70 Litvínov, Czech Republic Central Laboratories, Institute of Chemical Technology Prague, Technická 5, 166 28 Prague 6, Czech Republic § Department of Petroleum Technology and Alternative Fuels, Institute of Chemical Technology Prague, Technická 5, 166 28 Prague 6, Czech Republic ‡
ABSTRACT: Biomass is a renewable energy source with great potential. One of the promising ways for the conversion of biomass into more suitable forms of energy is its pyrolysis. Liquid products of the biomass pyrolysispyrolysis oils (or bio-oils) could be used in the future as biofuel or as feedstock for valuable chemicals. Detailed knowledge about their chemical composition is crucial, as it can facilitate the design of processes for the necessary upgrading of bio-oils. This paper outlines current knowledge about the composition of bio-oils and presents an overview of the commonly used analytical methods and procedures for the characterization of the liquid pyrolysis products of biomass. The capabilities and limitations of these methods are discussed as well.
1. INTRODUCTION Lignocellulosic biomass is a plentiful and renewable energy resource commonly used throughout the history of mankind. Due to the ever-increasing energy demands, biomass was almost completely replaced by coal, later by crude oil and natural gas as the primary energy resource in the last two centuries.1 Apart from the gradual depletion of these fossil resources, the concentration of CO2 in the atmosphere has increased since the industrial revolution from ∼280 to ∼380 ppm in 2004,2 resulting in global warming (however, this has not been scientifically proved yet). In addition, other adverse environmental effects have been attributed to the consumption (combustion) of fossil fuels including ozone layer depletion and air pollution by volatile organic compounds (VOC), soot (particulate matter), etc. To mitigate these drawbacks of fossil fuels, a more widespread use of biomass as an energy resource has been suggested.3,4 Direct utilization of the energy stored in lignocellulosic biomass is possible only by its combustion and is thus suitable only for stationary applications, such as power plants. Mobile applications, for example, transportation fuels, rely, however, exclusively on liquid or gaseous fuels and lignocellulosic biomass needs to be converted to such fuels in order to be used as transportation fuel or a source of chemicals. Typically, thermo-chemical processes, such as gasification and pyrolysis, have been suggested as promising routes.5 Gasification of biomass, as well as of any carbon-containing feedstock, affords a so-called synthesis gas, that is, a mixture of carbon monoxide and hydrogen that can be used (after thorough cleaning to remove any impurities (catalytic poisons) and adjustment of the CO/H2 ratio) to produce a wide range of chemicals and fuels (e.g., via methanol or Fischer−Tropsch syntheses). The inherent disadvantage of this approach is that all molecules are broken down to C1 products (CO, CO2); that is, no carbon−carbon bonds are preserved, which is energy inefficient. Moreover, gasification and cleaning of synthesis gas are large scale technologies. This fact imposes severe logistic © 2013 American Chemical Society
issues for (sustainably) securing the needed supply of lignocellulosic biomass.5 Pyrolysis, on the other hand, is a process of the thermal decomposition of the feedstock in the absence of oxygen, leading to the cleavage of some bonds and thus to the formation of gaseous, liquid, and solid products.5 The distribution of products among these three main groups is determined by the pyrolysis conditions, particularly temperature and residence time. From the end-use point of view, the liquid product (bio-oil) has a higher energy content (lower heating value) than feedstock and contains a wide spectrum of compounds, some of which are promising raw materials for the production of chemicals.5,6 Slow, fast and flash pyrolysis are the three main types of biomass pyrolysis distinguished in literature.6−10 The main product of the slow pyrolysis (temperature up to 500 °C, heating rate ∼10 °C/s and residence time ∼5−30 min) is the char. In the process of the fast pyrolysis (temperature 500−650 °C, heating rate ∼100 °C/s, residence time 0.5−5 s), mainly gaseous products, hydrocarbon vapors, liquid product (bio-oil), and solid char are formed. The main product of the flash pyrolysis (700− 1000 °C, heating rate ∼10000 °C/s, residence time less than 0.1 s) is bio-oil which can be after appropriate treatment used as fuel or feedstock for valuable chemicals.6−10 The lignocellulosic biomass is composed of three major componentscellulose, hemicellulose, and ligninand other minor components including organic extractives (fats, waxes, terpenes, resins, etc.) and inorganic minerals.6,11,12 Cellulose is a linear polysaccharide that consist of ∼5000−10000 of β-(1→ 4)-D-glucopyranose units.6 Hemicellulose is a heterogeneous polysaccharide consisting of various polymerized monosaccharides (glucose, mannose, galactose, xylose, arabinose, 4-O-methylglucuronic acid, and galacturonic acid residues) with a degree of polymerization Received: October 11, 2013 Revised: December 19, 2013 Published: December 20, 2013 385
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Figure 1. (A) Chemical structure of cellulose. (B) Main monomers of hemicellulose. (C) Lignin precursors.
of ∼150 and lignin is an amorphous, three-dimensional, highly branched polyphenolic substance consisting of an irregular array of hydroxy- and methoxy-substituted phenylpropane units6 (see Figure 1). Bio-oils are very complex mixtures of hundreds of organic oxygen-containing components (carboxylic acids, phenols, alcohols, aldehydes, ketones, ethers, esters, furans, sugars, and water) derived from the decomposition of major biomass components.6,9,10,13−15 The percentage of cellulose, hemicellulose, and lignin varies in different biomass species16 (see Table 1) and the type of biomass therefore influences the
very difficult due to the presence of the pyrolytic lignin and components with a wide distribution of boiling points and molecular weights and with a different polarity and solubility. Moreover, many of the bio-oil compounds are present in very low concentrations. Therefore, detailed analysis requires the combination of several analytical methods.10,13,20 The aim of this work is to review and summarize the current possibilities for the chemical characterization of bio-oils with the focus mainly on the sample preparation and chromatographic and spectroscopic methods and to provide a fast overview of the analytical procedures and methods used for this purpose. Moreover, the analytical methods for the specific characterization of the individual classes of the oxygenated biooil compounds (phenols, low-molecular carbonyl compounds, volatile organic acids, and sugars) are presented in a separate section. The methods for the determination of the water content of bio-oils (Karl Fischer titration) or their bulk elemental composition and methods for the characterization of physical properties of bio-oils are not presented in this paper, as these were discussed in detail elsewhere.21−24
Table 1. Typical Lignocellulose Content of Selected Biomass Species (wt % Dry and Extractive-free Basis)16 lignocellulose content (wt %) biomass
cellulose
hemicellulose
lignin
spruce wood softwood (avg.) beech wood hardwood (avg.) hazelnut shell corncob
50.8 45.8 45.8 45.2 25.9 52.0
21.2 24.4 31.8 31.3 29.9 32.0
27.5 28.0 21.9 21.7 42.5 15.0
2. CHEMICAL CHARACTERIZATION OF PYROLYSIS BIO-OILS The chemical analysis of the biomass-derived pyrolysis liquids included typically different chromatographic and spectroscopic methods. The major analytical techniques used for the chemical characterization of bio-oils were conventional gas chromatography (GC),24−37 comprehensive two-dimensional gas chromatography (GC × GC),38−44 liquid chromatography (LC),20,45−52 high-resolution mass spectrometry (HRMS),30,53−56 nuclear magnetic resonance (NMR),51,57−59 and Fourier transform infrared spectroscopy (FTIR).37,45,60−66 Due to the huge complexity of bio-oils, the sample pretreatment was often applied in order to achieve more detailed information about their chemical composition. 2.1. Sample Preparation. The sample treatment included typically solvent and solvent-free methods. From the solvent techniques, the dissolution of the bio-oil sample in an appropriate solvent (acetone, methanol) without any further treatment (except filtration) was applied.27,35 Some other biooil sample pretreatment methods, such as fractionation by the
chemical composition of resulting pyrolysis liquids.17,18 Besides the biomass origin, the chemical composition of bio-oils depends upon other factors such as feedstock pretreatment (particle size and shape, moisture, and ash content), conditions of the pyrolysis process (temperature, heating rate, residence time, and pressure), vapor filtration, and condensation (filter type, condensing method and medium, cooling rate). The properties of bio-oils produced from different feedstock and at different conditions can therefore differ greatly.10,13 According to Meier,19 bio-oils contain about 20 wt % of water, approximately 40 wt % of components, which are detectable by gas chromatography, 15 wt % of nonvolatile components detectable by liquid chromatography and approximately 15 wt % of high-molecular (residual) components that cannot be detected by chromatographic methods. In comparison to crude oil and conventional fuels, bio-oils contain usually less nitrogen and practically no sulfur or metals.17 Bio-oil characterization is 386
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Figure 2. Scheme of solvent fractionation of bio-oil based on the water extraction35,67
(i) adsorption chromatography (LSC), (ii) solvent extraction− liquid−liquid extraction (LLE) and supercritical fluid extraction (SFE), (iii) gel permeation chromatography (GPC), (iv) sample derivatization, and (v) solid phase extraction (SPE) were also used in order to overcome the huge complexity of bio-oils, or to improve the chromatographic separation and obtain more precise analytical information. However, these techniques are usually time-consuming and the loss of the analyte is possible. The solvent-free pretreatment techniques for the bio-oil characterization involved (vi) solid phase microextraction (SPME) and (vii) molecular distillation. (i). Adsorption Chromatography. The LSC separation is typically applied to the bio-oil sample before the subsequent GC analysis in order to obtain fractions of different polarity. The compounds retained on a column are eluted using solvents of increasing polarity. The LSC separation was usually applied to the n-pentane soluble fraction, obtained by the extraction of the bio-oil sample with n-pentane. The retained compounds were gradually eluted with n-pentane, toluene, ether, and methanol to obtain aliphatic, aromatic, ester, and polar fractions, which were further analyzed by GC-MS or GC-FID.45,46 (ii). Solvent Extraction. The separation of the desired compounds is achieved by selecting appropriate solvents for extraction. The method can be divided into liquid−liquid extraction (LLE) and supercritical fluid extraction (SFE). The most commonly used LLE method for the bio-oil fractionation is based on the water addition (see Figure 2).35,67 After adding water, the bio-oil sample is divided into watersolubles and water-insolubles (pyrolytic lignin). The water insoluble fraction is further separated by dichloromethane extraction into CH2Cl2 soluble (low-molecular mass lignin material) and CH2Cl2 insoluble fractions (high-molecular mass lignin material). The water-soluble fraction is subsequently extracted with diethylether and dichloromethane. In the ether soluble fraction, aldehydes, ketones, and lignin monomers can be detected. The ether-insolubles consist of sugars and C < 10 aliphatic hydroxy acids.35,67 Table 2 presents the concentrations of major bio-oil compound classes determined by the presented solvent fractionation scheme, followed by GC-MS.21,67,68 Other fractionation approaches were also applied for bio-oil characterization, but it is not the goal of this paper to discuss in detail the
Table 2. Typical Abundance of Major Compound Classes of Bio-oils21,67,68 major components
abundance wt %
aldehydes alcohols carboxylic acids furans ketones phenolic monomers phenolic oligomers sugars water
10−20 2−5 4−15 1−4 1−5 2−5 15−30 20−35 20−30
bio-oil solvent separation strategies, because this information can be found elsewhere.6 SFE uses dissolving abilities of the supercritical solvents (held at or above their critical pressure and critical temperature). The most widely used supercritical solvent is carbon dioxide (critical pressure 73.8 atm and critical temperature 31.1 °C). This method is suitable for thermally unstable compounds (separation occurs at low temperatures) and offers improved yields comparable with those obtained by conventional LLE methods based on organic solvents. In addition, unlike the organic solvents, carbon dioxide is nontoxic, nonflammable, cheap, and easily available.69−71 The supercritical CO2 extraction was applied also for bio-oil separation and enabled the selective extraction of the low-polar compounds (aldehydes, ketones, phenols), on the other hand, acids and water remained in the residue phase.69−71 (iii). Gel Permeation Chromatography. This method separates the molecules of the analyzed sample based on their sizes. GPC was applied (as a sample pretreatment method) for the separation of the high-molecular-mass lignins from the phenolic fraction before the LC-LC characterization of bio-oil phenols.72 (iv). Sample Derivatization. In the bio-oil analytics, the derivatization reactions were usually applied before GC and HPLC methods. The derivation reactions used in the gas chromatography transform an analyte in order to be detectable in GC or to improve the chromatographic behavior of the components with poor elution properties. Derivatization increases or decreases the volatility of target compounds; it also reduces the adsorption of the analyte and improves detector response, 387
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peak separations, and peak symmetry.73 In general, compounds containing polar functional groups with active hydrogen (−OH, −NH, −COOH, etc.) are modified, due to the tendency of these compounds to form intermolecular hydrogen bonds, which affect the volatility of compounds, their thermal stability, and their tendency to interact with column packing materials.73 In the bio-oil characterization, acetylation was applied for the analysis of phenols,34 trimethylsilylation was used for the determination of anhydrosugars,74,75 the derivatization with o-(2,3,4,5,6-pentafluorobenzyl) hydroxylamine hydrochloride (PFBHA)47 and catalytic methanololysis74 were used for the characterization of aldehydes and the alkylation into benzylesters was used for the analysis of volatile (C1−C6) carboxylic acids24,35 (see also Section 2.7). HPLC-UV analysis of carbonyl compounds in bio-oils is usually based on the derivatization of the sample with 2,4dinitrophenylhydrazine (2,4-DNPH). This derivatization reagent reacts selectively with aldehydes and ketones, resulting in hydrazone-DNPH derivatives and these can be selectively detected by HPLC-UV.47,48 (v). Solid Phase Extraction. SPE is a separation technique that uses solid particles in cartridges or disc devices for the isolation and preconcentration of the selected analyte and for the purification of the sample.76 The method is simple, fast, and enables selective extraction of the selected analyte.76 The sample is passed through the sorbent, where (i) the analyte is selectively retained and then eluted with an appropriate solvent or (ii) the impurities are captured and a clean sample is collected. In the bio-oil analyses, SPE was applied for sample cleanup.77 (vi). Solid Phase Microextraction. SPME includes sorption and desorption steps. First, a fused silica fiber coated with a stationary phase is exposed to the sample until equilibrium is established between the analyte in the sample and on the fiber and then the analyte is desorbed from the fiber in the heated injector of the gas chromatograph and subsequently analyzed by GC.78 SPME enables rapid and simple analysis of volatile and semivolatile compounds in different matrices without requiring the use of an organic solvent. In the bio-oil analytics, SPME was used for the analysis of the volatile low-molecular compounds of bio-oils.24,28,74 This method can also be combined with the bio-oil derivatization and headspace extraction and be used for the selective characterization of volatile aldehydes.47 (vii). Molecular Distillation. The molecular distillation is a rapid and economically feasible process that is widely used in the industry (including petrochemical industry) for the separation and purification of thermally unstable chemicals, for which the conventional distillation techniques are not applicable. It is also suitable for viscose, high-molecular weight, and high-boiling point compounds.71,79−81 The separation relies on the various mean free paths of the separated substances. The process occurs in the high vacuum. The distance between the evaporation and condensation surfaces is less than or equal to the mean free path of a light molecule but greater than that of a heavy molecule. The light molecules escaped from the evaporation surface can easily reach the condensation surface and subsequently condense without the collision with other molecules. They are therefore continuously released from the liquid phase. On the other hand, the heavy molecules return to the liquid phase.71,81 Recently, the molecular distillation was applied also to separate the bio-oil prior a GC-MS analysis.71,79−81
2.2. Gas Chromatography. 2.2.1. Conventional (Onedimensional) Gas Chromatography (GC). Gas chromatography, especially with a mass spectrometric detector (GC-MS) or flame ionization detector (GC-FID), was used for decades for the analysis of the chemical composition of bio-oils and provided valuable information about the chemical structure of compounds present in pyrolysis liquids.24−37 The main challenges of the gas chromatographic characterization of such complex mixtures as bio-oils include insufficient chromatographic resolution, peak coelution, unavailability of mass spectra of some bio-oil components in MS libraries and the lack of analytical standards (unknown response factors for the quantification). Gas chromatography is also unable to characterize the nonvolatile compounds present (sugar and lignin oligomers).6 Qualitative and quantitative bio-oil analysis is thus a very difficult task, and GC-MS analysis provides only partial information about the chemical composition of bio-oils. Despite the mentioned difficulties, GC-MS is still one of the most commonly used methods for the tentative analysis of liquid pyrolysis products, especially due to its acceptable price and wide availability. In the analyses of the biomass pyrolysis products, nonpolar,25,28,31,32,37 medium polar,26,27,32,36 and polar30,33 columns were applied. Over 300 different bio-oil components were detected so far using GC-MS (EI +, 70 eV) and GC-FID.24−37 Typical bio-oil compounds identified by these methods are listed in Table 3. Pyrolysis Gas Chromatograph−Mass Spectrometry (PyGC-MS). One of the possibilities for studying the process of biomass pyrolysis and its products in the lab-scale is the pyrolysis gas chromatograph−mass spectrometry.28,31,32,36 This method comprises a thermal decomposition of the raw biomass under the defined conditions and the gas chromatographic analysis of the resulting degradation products. Online and offline pyrolyses are the two commonly used configurations. In the online pyrolysis, the pyrolysis products are directly introduced into the injector of the gas chromatograph where they are analyzed. The main drawback of this configuration is the discrimination of high-molecular components (caused by their condensation in the transfer line between the pyrolyzer and gas chromatograph), poor chromatographic behavior of polar compounds, and deterioration of GC columns due to the presence of nonvolatile components.28,82 In the off-line configuration, the pyrolysis products are captured on the sorbent and subsequently eluted with the solvent, which eliminates the memory effects and column deterioration and enables further sample treatment (e.g., derivatization) prior to the analysis.28,82 However, a long sampling time is needed for the analysis of high-molecular compounds.28 2.2.2. Comprehensive Two-dimensional Gas Chromatography (GC × GC). The separation on two capillary columns (usually nonpolar and medium polar or polar) connected with a modulator is characteristic for the comprehensive two-dimensional gas chromatography. Typically, the separation starts on the long nonpolar column, where components are separated according to their boiling points. The separation process on the nonpolar column is interrupted in regular intervals by the modulator. The modulator collects and focuses the effluent and reinjects it into a heated very short medium polar or polar column, where components are rapidly separated at isothermal conditions based on their polarities.83 The utilization of two columns having different selectivity and refocusing the eluted components during the modulation period in GC × GC allows achieving an extended theoretical peak capacity, enhanced 388
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Table 3. Typical Bio-oil Compounds Detected by GC and GC × GC Methods M [g/mol]
32 46 58 60 62 68 72 78 86 92 104 106 128 130 142 152 212 46 60 72 74 76 86 86 88 88 98 100 102 104 104 116 116 116 118 120 174 216 230 30 44 56 58 58 60 70 72 72 74 74 84 84 86 86 90 100
compound
Table 3. continued M [g/mol]
reference 102 102 128
Nonaromatic Compounds Alcohols methanol 25, 28, 29, 36 ethanol 25 2-propenol (allylalcohol) 27, 30, 42 1,2-ethenediol 36 1,2-ethanediol 36 isobutanol 27 2-buten-1-ol 37 2,3-dimethyl-2-pentanol 33 cyclopentanol 30 propanetriol (glycerol) 43 1,3-dioxolane-2-methanol 28 2,2-dimethoxyethanol 37 2,3-dimethylcyclohexanol 37 1-octanol 33 4-isopropylcyclohexanol 37 pentane-1,2,3,4,5-pentol (xylitol) 37 3-isopropyl-4-methyl-1-decen-4-ol 42 Carboxylic Acids formic acid 25, 27, 29, 30, 36, 37 acetic acid 24−31, 35−37, 42 2-propenoic (acrylic) acid 26, 27, 29 propionic acid 25−27, 29, 30, 37, 42 1-hydroxyacetic (glycolic) acid 29, 35 cis-2-butenoic (isocrotonic) acid 25 trans-2-butenoic (crotonic) acid 26, 29 butyric acid 26, 29, 30, 42 2-methylpropanoic (isobutyric) acid 25 2,4-pentadienoic acid 42 3-methyl-3-butenoic acid 26 pentanoic (valeric) acid 25, 29, 37 2-hydroxybutyric acid 25 4-hydroxybutyric acid 37 4-oxopentanoic (levulinic) acid 26 hexanoic acid 26, 29 4-methylpentanoic acid 25 acetoxyacetic acid 31 2,4-dihydroxybutyric acid 35 3-propylpentanedioic acid 25 3-hydroxydodecanoic acid 37 3-hydroxytridecanoic acid 37 Nonaromatic Aldehydes formaldehyde 28, 36, 37 acetaldehyde 25, 28, 29, 31, 36, 37, 42 2-propenal 36 propanal 26 ethanedial (glyoxal) 28 glycolaldehyde 26−29, 31, 36, 37, 42 2-butenal (crotonaldehyde) 25, 26, 31, 36 butanal 26, 36 2-oxopropanal (methylglyoxal) 28, 36 2-hydroxypropanal 26, 27, 29, 36 3-hydroxypropanal 26, 36 2-methyl-2-butenal 26 2-pentenal 26 pentanal (valeraldehyde) 37, 43 1,4-butanedial (succinaldehyde) 27, 28, 31, 36, 40 2,3-dihydroxypropanal (glyceralde42 hyde) 1,5-pentanedial (glutaraldehyde) 31, 42
58 70 72 74 82 84 84 84 84 86 86 86 86 88 90 96 96 96 96 96 98 98 98 98 98 98 100 100 100 102 102 110 110 110 110 110 112 112 112 112 112 112 112 114 114 116 116 124 124 124 126 126 128 389
compound
reference
Nonaromatic Aldehydes 2-hydroxy-1,4-butanedial 36 2-hydroxy-3-oxobutanal 36 octanal 25 Nonaromatic Ketones acetone 25, 28−30, 42 methyl(vinyl)ketone 30 2-butanone 25−27, 29, 36 1-hydroxy-2-propanone (acetol) 24, 26−31, 35−37, 42 2-cyclopenten-1-one 25, 26, 29, 30, 35, 40, 43 3-penten-2-one 25, 26, 31 3-methyl-3-buten-2-one 26, 27 cyclopentanone 25−27, 29, 30, 42 3-butenal-2-one 36 2-pentanone 25−27 3-pentanone 26−28 2,3-butanedione 29, 31, 36 3-methyl-2-butanone 26 1-hydroxy-2-butanone 26, 27, 29, 30, 35, 36, 40, 42, 43 1,3-dihydroxy-2-propanone 37 2-cyclopenten-1,4-dione 26, 31 4-cyclopenten-1,3-dione 36 2-methyl-2-cyclopenten-1-one 25−27, 29−31, 35, 42, 43 3-methyl-2-cyclopenten-1-one 25, 26, 30, 43 2-cyclohexen-1-one 25, 26 2-methylcyclopentanone 25, 28 1,2-cyclopentanedione 28, 31 1-cyclohexanone 25, 27 4-pentenal-2-one 35 3-methyl-3-penten-2-one 26 2-hydroxy-2-cyclopenten-1-one 26, 42 2,3-pentanedione 25−27, 29 2-methyl-3-pentanone 26 3-hexanone 26 4-hydroxy-3-methyl-2-butanone 25 1-hydroxy-2-pentanone 25 2,3-dimethyl-2-cyclopenten-1-one 26, 27, 30, 42 2,4-dimethyl-2-cyclopenten-1-one 25 3,4-dimethyl-2-cyclopenten-1-one 25, 42 2-cyclohexene-1,4-dione 31 3-ethyl-2-cyclopenten-1-one 26, 27 dimethylcyclopentanone 29 2-hydroxy-1-methyl-1-cyclopenten26 3-one 2-hydroxy-3-methyl-2-cyclopenten25−31, 35−37, 43 1-one 2-methyl-1,3-cyclopentanedione 26 3-methyl-1,2-cyclopentanedione 43 1-methylcyclohexanone 25 3-methylcyclohexanone 26 3-hydroxycyclohexanone 37 2,5-hexanedione 27 1-acetyloxy-2-propanone 26, 27, 29, 30, 35, 36, 42 4-hydroxy-4-methyl-2-pentanone 25, 26 trimethylcyclopenten-1-one 26, 29 2,3,4-trimethyl-2-cyclopenten-1-one 26 4,5-dimethyl-2-cyclohexen-1-one 26 3,4-dimethyl-2-hydroxy-2-cyclopent43 en-1-one 3-ethyl-2-hydroxy-2-cyclopenten-125−28, 30, 31, 42, 43 one 2-methyl-4-heptanone 25 dx.doi.org/10.1021/ef402047y | Energy Fuels 2014, 28, 385−402
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Table 3. continued M [g/mol] 130 130 60 74 74 86 86 88 88 100 102 102 102 102 102 104 114 114 114 116 128 130 130 142 144 158 160 82 84 96 102 103 104 104 110 112 116 142 157 180 182 196
68 82 82 84 84 84 86 94 96 96 96 98 98
Table 3. continued compound
M [g/mol]
reference
Nonaromatic Ketones 2,3-dihydroxy-1-hexene-4-one 36 1-(acetyloxy)-2-butanone 25, 26, 42 Nonaromatic Esters methyl formate 29 methyl acetate 24, 25, 27, 29−31, 37 ethyl formate 37 methyl 2-propenoate (methyl acryl36 ate) vinylacetate 25 ethyl acetate 27, 30, 27 methyl propanoate 27 methyl 2-butenoate 26 2-propenyl acetate 25, 37 methyl 2-oxopropanoate 26, 31 ethyl propanoate 40 methyl butanoate 25 acetanhydride 25, 36 ethyl 2-hydroxyacetate 37, 43 methyl 2-pentenoate 36 methyl 3-pentenoate 36 vinyl butanoate 25 1-hydroxy-2-propanone acetate 29 2-propenyl butanoate 25 4-hydroxy-2-butanone acetate 29 2-oxobutyl acetate 37 allyl acetylacetate 25 isobutyl isobutanoate 25 methyl octanoate 33 ethyl methyl succinate 37 Others 2-methyl-1,4-pentadiene 25 2-hexene 25 dimethylcyclopentene 29 1-ethoxybutane 25 sec-butylnitrite 25 1-ethoxy-1-methoxyethane 40 1-ethoxy-2-methylpropane 25 1-(1-methylethyl)cyclopentene 25 cyclooctane 25 2-ethoxypentane 25 decan 25 dibutylformamide 25 1-methylfluorene 25 fluorenol 25 ditolylmethane 25 Heterocyclic Compounds Furans furan 27, 29, 31, 36 2-methylfuran 29, 31, 36 3-methylfuran 25, 36 2(5H)-furanone (γ-crotonolacton) 26−31, 35, 37, 40, 42, 43 2(3H)-furanone 26, 36 3(2H)-furanone 36 dihydrofuran-2(3H)-one 26, 29, 30, 36, 42, 43 (γ-butyrolacton) vinylfuran 31 2,5-dimethylfuran 31 2-furaldehyde (furfural) 24−31,35 −37, 42, 43 3-furaldehyde 26, 36, 40 2-furanmethanol (2-furfurylalcohol) 25−29, 31, 36 3-furanmethanol 26, 36
98 98 98 98 98 100 102 102 102 110 110 110 110 112 112 114 116 126 126 128 132 144 184 84 96 112 114 126 142
79 93 93 95 95 109 126 132 132 144 144 144 162 162 162 162 162 164 210 342 342
78 92 104 106 390
compound
reference
Furans 5-methyl-2(3H)-furanone 26, 27, 29, 31, 35, 36 5-methyl-2(5H)-furanone 26, 27, 30, 31, 42 4-methyl-2(5H)-furanone 26, 31, 37, 40, 42, 43 3-methyl-2(5H)-furanone 26, 29, 30, 36, 42, 43 2,5-furandione (maleic anhydride) 30 valerolactone 29, 35 3-hydroxydihydro-2(3H)-furanone 26, 42, 43 4-hydroxydihydro-2(3H)-furanone 31, 42 2-methoxytetrahydrofuran 30 2,3,5-trimethylfuran 31 2-propylfuran 31, 36 1-(2-furanyl)-ethanon (2-acetylfuran) 25−27, 30, 31, 35, 36, 42, 43 5-methyl-2-furaldehyde 25−31, 35, 36, 42 2,5-dihydro-3,5-dimethyl-2-furanone 26 3-methyl-2,5-furandione 30, 42 3-methyl-2,4-(3H, 5H)-furandione 31 2-ethoxytetrahydrofuran 30 5-(hydroxymethyl)furfural 25−31, 35, 36, 40, 42, 43 methyl furoate 36 28 2,5-dimethyl-4-hydroxy-2H-furan3-one 2,5-dimethoxytetrahydrofuran 27, 37 5-hydroxymethyl-2(2H)-furanone 30 γ-heptylbutyrolactone 25 Pyrans 2,3-dihydropyran 25 4H-pyran-4-one 43 2H-pyran-2,6(3H)-dione 31 4-hydroxy-5,6-dihydro-2H-pyran36 2-one 3-hydroxy-2-methyl-4H-pyran-4-one 26, 29, 31, 35, 36, 42 3,5-dihydroxy-2-methyl-4H-pyran31 4-one Pyridines, Pyrrols pyridine 30 2-methylpyridine 30 3-methylpyridine 30 3-pyridinol 30, 43 2-formylpyrrol 25 2-acetylpyrrol 25 Carbohydrates levoglucosenone 31 1,5-anhydro-arabinofuranose 36 36 1,5-anhydro-β-D-xylofuranose 1,4-3,6-dianhydro-mannofuranose 36 27, 28, 30, 31, 42, 43 1,4-3,6-dianhydro-α-D-glucopyranose 26, 43 2,3-anhydro-D-mannosan levoglucosan 25−27, 29, 31, 37, 40, 43 36 1,6-anhydro-α-D-galactopyranose 36 1,6-anhydro-β-D-mannopyranose 36 1,6-anhydro-β-D-glucofuranose 36 1,6-anhydro-α-D-galactofuranose 43 1,5-anhydro-D-mannitol D-glycero-L-glucoheptose 37 melibiose 37 lactose 25, 37 Aromatic Compounds Aromatic Compounds (Nonoxygenated) benzene 26, 27 toluene 26, 31, 32 styrene 27, 31, 32 ethylbenzene 26, 31, 32 dx.doi.org/10.1021/ef402047y | Energy Fuels 2014, 28, 385−402
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Table 3. continued M [g/mol] 106 106 106 116 116 120 128 130 142 142 144 156 108 118 120 120 120 122 122 124 132 132 134 134 136 136 136 138 144 146 148 150 152 158 162 164 168 168 170 172 174 178 182 202 255
106 120 122 122 122 132 136 136 138 180
Table 3. continued compound
M [g/mol]
reference
Aromatic Compounds (Nonoxygenated) 1,2-dimethylbenzene (o-xylene) 26, 32 1,3-dimethylbenzene (m-xylene) 32 1,4-dimethylbenzene (p-xylene) 26, 32 propynylbenzene 31 indene 26 3-ethyltoluene 31 naphtalene 26 methyl-1H-indene 26 1-methylnaphtalene 26, 31 2-methylnaphtalene 26 ethyl-1H-indene 26 dimethylnaphtalene 26 Aromatic Compounds (Oxygenated) benzylalcohol 32 2,3-benzofuran 26 ethenyloxybenzene 33 2,3-dihydrobenzofuran 31 acetophenone 27, 32 1-methoxy-4-methylbenzene 26 benzoic acid 29 3,5-dihydroxytoluene 42 2,3-dihydro-1H-inden-1-one 26, 42 7-methylbenzofuran 26 2,3-dihydro-1H-inden-5-ol 26 phenyl-2-propanone 25 1-methoxy-2,3-dimethylbenzene 26 1-(2-hydroxyphenyl)ethanone 33 1-(3-hydroxyphenyl)ethanone 33 4-hydroxybenzoic acid 32 2-naphtol 26, 33 dimethylbenzofuran 26 7-methoxybenzofuran 31 4-(3-hydroxypropenyl)phenol 29, 37 1,2-dimethoxy-3-methylbenzene 26 2-methyl-1-naphtol 33 6-methoxy-3-methylbenzofuran 31 3-(2-hydroxyphenyl)-2-propenoic 33 acid 1,2,4-trimethoxybenzene 25 1,2,5-trimethoxybenzene 25 (1,1-biphenyl)-3-ol 33 2-naphtoic acid 37 3-methoxy-2-naphtol 26 2-(2-isopropylphenyl)-1-propanol 37 1,2,3-trimethoxy-5-methylbenzene 27 3-methoxy-2-naphtoic acid 37 3-methoxy-5-methyl-4-nitrophtalic 37 acid Aromatic Aldehydes benzaldehyde 26, 27, 32 2-methylbenzaldehyde 37 2-hydroxybenzaldehyde 26, 42 3-hydroxybenzaldehyde 26, 42 4-hydroxybenzaldehyde 32 3-phenyl-2-propenal (cinnamalde25 hyde) 4-hydroxy-3-methylbenzaldehyde 26, 42 2-hydroxy-6-methylbenzaldehyde 42 2,3-dihydroxybenzaldehyde 42 4-ethoxy3-methoxybenzaldehyde 25
108 122 122 136 136 94 108 108 108 120 122 122 122 122 122 122 122 122 122 134 134 134 136 136 136 136 136 136 136 136 136 136 148 220 368
110 110 110 124 124 124 124 138 138 138 138 140 140 152 124 124 124 138 138 138 138 138 391
compound
reference
Anisols 1-methoxybenzene (anisol) 32 3-methylanisol 32 4-methylanisol 32 2,4-dimethylanisol 32 2,5-dimethylanisol 32 Phenols phenol 24−35, 42, 43 2-methylphenol (o-cresol) 26−29, 31−35, 42, 43 3-methylphenol (m-cresol) 27, 29−35, 37, 43 4-methylphenol (p-cresol) 27−35, 42 4-vinylphenol 26, 28, 29, 32 2-ethylphenol 26, 42 3-ethylphenol 26, 27, 32, 42 4-ethylphenol 26, 28, 30−32, 34, 37 2,3-dimethylphenol 26, 32, 33 2,4-dimethylphenol 26, 27, 32, 33, 35, 42 2,5-dimethylphenol 26, 27, 32, 33, 35 2,6-dimethylphenol 26, 27, 32, 33 3,4-dimethylphenol 27, 37 3,5-dimethylphenol 32, 33 4-allylphenol 32, 34 4-(1-propenyl)phenol (cis, trans) 26, 32 4-(2-propenyl)phenol 42 2-propylphenol 32 4-propylphenol 32 2-ethyl-6-methylphenol 42 3-ethyl-5-methylphenol 42 4-ethyl-3-methylphenol 37 2,3,4-trimethylphenol 26 2,3,5-trimethylphenol 26 2,3,6-trimethylphenol 33 2,4,5-trimethylphenol 42 2,4,6-trimethylphenol 26, 27, 33 2-allyl-4-methylphenol 42 2,6-di-tert-butyl-4-methylphenol 25 2,2′-methylenebis(6-tert-butyl-4-ethyl- 25 phenol) Benzenediols 1,2-benzenediol (catechol) 26−32, 34, 35, 37, 40, 42, 43 1,3-benzenediol (resorcinol) 29, 34 1,4-benzenediol (hydroquinone) 27, 29, 34, 42, 43 3-methylcatechol 27, 32, 34, 35, 37, 42, 43 4-methylcatechol 26, 27, 31, 32, 34, 37, 42, 43 3-methylresorcinol 34 2-methylhydroquinone 25, 43 4-ethylcatechol 25, 35, 37, 42 4-ethylresorcinol 25, 42 4,5-dimethylresorcinol 42 2,5-dimethylhydroquinone 25, 42 3-methoxycatechol 27, 28, 32, 34, 35, 43 5-methoxyresorcinol 34 3,4-dihydroxyacetophenone 25 Metoxy-, Dimetoxyphenol Derivatives 2-methoxyphenol (guaiacol) 24−26, 28−31, 34, 42, 43 3-methoxyphenol 27 4-methoxyphenol 37, 40 3-methylguaiacol 27, 32 4-methylguaiacol 24, 26, 28−32, 34, 40, 42, 43 5-methylguaiacol 26 6-methylguaiacol 37 5-methyl-3-methoxyphenol 26 dx.doi.org/10.1021/ef402047y | Energy Fuels 2014, 28, 385−402
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Table 3. continued M [g/mol] 150 152 152 152 152 154 164 164 164 164 166 166 166 168 168 168 178 180 180 180 180 180 180 182 182 182 182 194 194 194 196 196 208 210 210 212
compound
during pyrolysis and for the analysis of polycyclic aromatic hydrocarbons (PAHs) and nitrogenated compounds. Approximately 30 PAHs were detected and quantified in studied oils and ∼140 nitrogen compounds (nitriles, pyridines, amides, amines, and polyaromatic nitrogenated compounds) were also identified in the sewage sludge bio-oil.38 Marsman et al.39 used GC × GC-FID and GC-MS for the characterization of crude beech bio-oil and hydrodeoxygenated (HDO) beech bio-oil. The identification especially of the volatile compounds (elution temperature up to 100 °C) in GCMS was very complicated due to the strong peak coelution. However, several phenolic components (e.g., guaiacols and syringols) were identified with high probabilities. On the other hand, clearly better chromatographic resolution was achieved using GC × GC-FID. Overall, 72 model components belonging to 10 various chemical classes (acids, aldehydes, and ketones, alkylbenzenes, hydrocarbons, phenones, guaiacols and syringols, alcohols, furans, phenolics, sugars) were used for the determination of their 1D and 2D retention times in the GC × GC-FID system and creation of retention time fields. Relative response factors for all model components were determined for quantification purposes. The obtained retention time fields were used for the identification and classification of the bio-oil compounds (i.e., assigning components into a certain chemical class). The group-type quantification was performed by the correction of the areas of all peaks within a chemical class using relative detector response and a summary of corrected peak areas. Significantly lower content of sugars and acids in HDO oil in comparison to crude beech oil was determined using this approach. On the other hand, the concentration of hydrocarbons, alcohols, alkylbenzenes, and guaiacols/syringols increased considerably after hydrodeoxygenation.39 Marsman et al.40 studied the chemical composition of the crude beech bio-oil and two HDO bio-oils. GC × GC-TOF-MS was used for the identification and group-type classification of the main components describing approximately 75% of the total peak area in crude and HDO oils. The major component classes of bio-oil were aldehydes, esters, and mono- and dimethoxyphenols. In HDO oils, a significant amount of cyclic hydrocarbons and alkylated phenols was detected. Some identified compounds (e.g., isomers of tetrahydronaphtalenes) were previously not detected in bio-oils.40 Stefsas et al.41 analyzed the chemical composition of three different bio-oils. Two-dimensional gas chromatography with time-of-flight mass spectrometric detection (GC × GC-TOFMS) was used to perform qualitative and quantitative analysis. Almost 300 components (approximately 80−95% of the peak area) were detected and identified, 11 selected components (cyclopentanone, hydroxypropanone, 2-cyclopenten-1-one, acetic acid, 2-furaldehyde, 3-methyl-2-cyclopenten-1-one, 2-acetyl5-methylfuran, 2(5H)-furanone, guaiacol, syringol, and levoglucosan) were quantified using a multiple-point external calibration curve. Afterward, each bio-oil sample was extracted with water and aqueous phases were analyzed by GC-FID. The same aqueous phases were further extracted with dichloromethane and dichloromethane phases were then quantified by GC × GC-TOF-MS. The results obtained by both methods were in agreement, showing the potential of GC-FID for rapid bio-oil screening.41 Djokic et al.42 studied the chemical composition of crude and HDO bio-oils obtained by the fast pyrolysis of pine wood. GC × GC-TOF-MS allowed separation of approximately 1000 and 500 compounds in HDO and crude oils, respectively.
reference
Metoxy-, Dimetoxyphenol Derivatives 4-vinylguaiacol 25−29, 31, 32 3-ethylguaiacol 26, 27, 32 4-ethylguaiacol 24, 26, 28−32, 34, 43 5-ethylguaiacol 26 vanillin 25−32, 34, 35, 37, 42, 43 2,6-dimethoxyphenol (syringol) 24, 25, 28−32, 34, 43 4-allylguaiacol (eugenol) 24−32, 35, 37, 43 cis-isoeugenol 24, 26−29, 31, 37 trans-isoeugenol 24, 26−29, 31, 37 5-(1-propenyl)guaiacol 42 4-propylguaiacol 26, 27, 31, 32, 34, 35 homovanillin 26, 28 acetoguaiacon 25, 27, 29−32, 35 4-methylsyringol 24, 27, 29, 32, 34 vanilic acid 25, 32, 35 isovanilic acid 32 coniferaldehyde 26, 28, 29, 32, 35, 37 cis-coniferol 26, 29, 32, 35 trans-coniferol 29, 32, 35 4-vinylsyringol 29, 32, 35 guaiacyl acetone 25, 26, 28, 30−32 propioguaiacone 27−29, 32, 34 3,4-dimethoxyacetophenone 25 syringaldehyde 27−29, 32, 34 4-(3-hydroxy-1-propyl)guaiacol 32 3-ethylsyringol 32 4-ethylsyringol 29, 32, 34 4-allylsyringol 27, 29, 32, 34, 35 4-propenylsyringol (cis, trans) 25, 27, 29, 32, 34, 35 ferulic acid 29 4-hydroxy-3,5-dimethoxyacetophe25, 27, 29, 32 none 4-propylsyringol 32 sinapaldehyde 29, 32, 35 syringyl acetone 27, 28, 32, 35 sinapylalcohol (cis, trans) 29, 32 homosyringic acid 25
chromatographic resolution (suppression of peak tailing and coelution), and subsequently a significantly higher number of detected components in comparison to the conventional GCMS technique. On the other hand, the high purchase price and limitation to the compounds with boiling points up to ∼400 °C are the main disadvantages of this method.38,83 In the analyses of the biomass pyrolysis liquids, nonpolar × medium polar39−44 and nonpolar × polar38 column sets were utilized. Fullana et al.38 demonstrated the great potential of the multidimensional gas chromatography-time-of-flight-mass spectrometry (GC × GC-TOF-MS) in the analysis of pyrolysis products. Products of primary and secondary pyrolysis of cellulose, lignin, and sewage sludge were studied, and the capabilities of onedimensional GC-MS and multidimensional GC-MS for the characterization of the pyrolysis products were compared. Different approaches for the GC × GC data processing were discussed as well. The utilization of GC × GC resulted in a significant increase of the peak number. Using the multidimensional GC-MS, more than 70% of the total peak area was identified due to an increased resolution, but only 47%, at best, using conventional GC-MS. Multidimensional GC-MS showed potential for the studying of reaction mechanisms 392
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A detailed quantitative bio-oil analysis was performed for the first time in this work using GC × GC-FID. More than 150 components (approximately 80% of the total peak volume) of the crude and HDO oils were annotated with names and quantified.42 Tessarolo et al.43 performed the chemical characterization of two bio-oil samples prepared in the process of the flash pyrolysis of empty palm fruit bunch and pine wood chips using GC × GC-TOF-MS (631 and 857 detected peaks) and GC-MS (∼166 and 129 detected peaks). Ketones, cyclopentanones, furanones, furans, phenols, benzenediols, methoxy- and dimethoxyphenols, and sugars were the major compound classes (>0.5% relative area) detected by GC × GC-TOF-MS in both bio-oil samples. Additionally, esters, aldehydes, and pyridines were identified in the empty palm fruit bunch biooil sample, whereas alcohols and cyclopentanediones were detected in the pine wood chips bio-oil. The results obtained by GC × GC-TOF-MS indicated the suitability of the both bio-oil samples for the production of valuable chemicals. Moreover, the empty palm fruit bunch bio-oil contained a significant amount of phenol (5.11% of the relative area), which could after separation provide an alternative to the phenol from petroleum.43 Moraes et al.44 analyzed rice husk and peach pit bio-oils by GC × GC-TOF-MS in order to obtain detailed qualitative information about the composition of the bio-oils. The great performance of this method allowed the detection of 503 components for the rice husk and 705 for the peach pit pyrolysis oil. The number of tentatively detected components was 106 and 223, respectively, in the same matrices.44 2.3. Liquid Chromatography (LC). The separation ability of the liquid chromatography is much worse than that of the gas chromatography. This drawback of LC is compensated by its ability to analyze besides volatile components detectable by gas chromatography also nonvolatile substances, which make up to around 15 wt %19 of bio-oils. Hence, liquid chromatography is a powerful tool and new methods for bio-oil characterization are still being developed. However, some heavy fractions of bio-oil (especially polymers or oligomers from the partial decomposition of cellulose or lignin) may not be amenable for LC either60 (this might be caused by an irreversible adsorption of some heavy polar compounds). The most commonly used LC methods for bio-oil characterization are (i) adsorption chromatography (LSC), (ii) gel permeation chromatography (GPC), and (iii) high performance liquid chromatography with UV or refractive index detection (HPLC-UV and HPLC-RID, respectively). (i) Adsorption chromatography was usually used to separate bio-oil into fractions having different polarities.45,46 In the LSC separation, the compounds are separated according to their different affinities to the used stationary phase. The polar compounds are effectively adsorbed on a polar stationary phase, while weakly polar are not. In the bio-oil separations, silica gel columns were usually applied as a stationary phase and different solvents for the elution of various classes of bio-oil compounds (e.g., pentane or hexane for hydrocarbons, benzene or toluene for aromatics, methanol for polar compounds, etc.).71 (ii) GPC separates the molecules of the sample based on their size (the chemical or physical interaction is undesirable). The stationary phase is a porous gel. The smaller analytes enter the pores more easily and spend
more time within the pores (longer retention times) than the larger analytes.84 It is a versatile method that can be used for the pretreatment of the bio-oil sample,72 or for the determination of the molecular weight distribution of the entire bio-oil or bio-oil fractions. 20,49−52 Tetrahydrofuran (THF) was commonly used as a mobile phase and styrene-divinylbenzene columns as a stationary phase in GPC separations. Polystyrene standards, having various molecular weights, were usually used for calibration and the differential refractometer or UVphotometer for detection.20,49−52 However, some highmolecular compounds are only partially soluble or totally insoluble in THF. The more polar dimethyl-formamide (DMF), which is able to dissolve heavy polar compounds, poly sugars and lignins over a wide range of molar masses, can be used as an alternative eluent. Nonetheless, when using DMF as an eluent and styrenedivinylbenzene columns for the separation, the adsorption of nonpolar components may occur due to the high polarity of DMF.20 (iii) HPLC was utilized for the characterization of volatile acids, aldehydes and sugars in pyrolysis liquids. The characterization of volatile acids was based on the extraction of the analyzed sample with trifluoroacetic acid and subsequent quantification of volatile acids in the aqueous phase using HPLC with UV detection.48 HPLC-UV was used also for quantification of bio-oil aldehydes. The biooil samples were analyzed after derivatization with 2,4DNPH (see Section 2.1).47,48 The HPLC characterization of bio-oil sugars was based on the hydrolysis of the sample and subsequent HPLC-RID quantification.85 Garcia-Perez et al.20 used GPC to determine the molecular weight distribution of softwood bark and hardwood bio-oil fractions. Styrene-divinylbenzene copolymer gel columns were utilized for the separation. Two solvents (THF and DMF) were used as eluents and monodisperse polystyrene, polyethylene glycol standards and other components present in bio-oil (e.g., benzene, naphthalene, phenol, cellobiose, etc.) were used for the calibration. The calibration curve obtained using THF was independent of compound polarity; on the other hand, while using DMF, the calibration curve showed dependence on the polarity of the calibration standard used, which was caused by the adsorption effect. Polystyrene standards could therefore not be used for calibration purposes when using DMF as an eluent in styrene-divinylbenzene copolymer gel columns. Polyethylene glycol standards were used instead. The molecular weights of prepared fractions determined by GPC varied in the range ∼100−2000 Da. The results were in agreement with the data from thermo-gravimetric analyses.20 Scholze et al.49 studied different water insoluble fractions of bio-oils (pyrolytic lignins) obtained by adding water into bio-oil samples. Average molecular weights of pyrolytic lignins studied were estimated by GPC to be in the range ∼650−1300 Da.49 Bayerbach et al.50 studied molar mass characteristics of different pyrolytic lignins by GPC, matrix assisted laser desorption ionization-time-of-flight-mass spectrometry (MALDITOF-MS), laser desorption ionization-time-of-flight-mass spectrometry (LDI-TOF-MS) and pyrolysis field ionization-mass spectrometry (Py-FI-MS). Using these methods, average molecular weights of studied pyrolytic lignins were determined to be 560−840 Da. The molecular weight of pyrolytic lignin 393
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methods is necessary to obtain more detailed information about the composition of bio-oils. The efficiency of the ionization between the compounds present (and subsequently ionization yields) may also be different. Hence, other methods are required for the quantitation. In the bio-oil analyses, the negative ion laser-desorption ionization (LDI) and electrospray ionization (ESI) were applied.30,53−56 The utilization of other ionization methods, such as atmospheric pressure chemical ionization (APCI) and atmospheric pressure photoionization (APPI), is being tested.53 The capabilities and limitations of these ionization methods are briefly described in following subparagraphs. More details about these and other ionization techniques used in HRMS can be found in the specialized literature.89,90 (i) ESI, in general, is applicable for compounds with polarities from medium to high polar and with the mass range up to m/z > 100 000. This ionization method is also suitable for thermally unstable species. However, ESI is not applicable for nonpolar and aprotic compounds and the efficiency of the ionization is usually poor for less polar compounds as well.54,56 (ii) LDI is applicable for nonvolatile species with low to medium polarity that significantly absorb the light of the used laser (highly unsaturated or eventually including a significant number of heteroatoms).53,56 On the other hand, the in situ generation of the laser-induced aggregation products requiring the minimizing of the laser power was reported in LDI.53 (iii) APCI enables ionization of low polar to polar compounds. Unlike ESI, some volatility and thermal stability of the analyzed compounds is required. APCI is usually limited to the species with the molecular masses up to ∼1500 Da.89,90 (iv) APPI is optimized for nonpolar compounds that are not efficiently ionized by other ionization methods. The molecular mass range of the analyzed compounds is similar as for APCI.89,90 According to our best knowledge, only negative ion mode mass spectrometry was applied for the bio-oil characterization. This is likely caused by the fact that the bio-oil components can produce negative ions,30,54−56 and for such compounds, the mass spectrometry in the negative ion mode is more efficient, selective, and sensitive than positive ion mass spectrometry.91 Currently, different types of mass analyzers providing high resolution (the range of tens of thousands)TOF based analyzersand ultrahigh resolution (>100 000)orbitrap and FT-ICR analyzersare available. The properties of different high-resolution mass spectrometers are presented in Table 4. More details can be found in the specialized literature.89,90 Another limitations of high-resolution mass spectrometers are high purchase price and for some of them also large space requirements. High-resolution mass spectrometers are therefore not commonly available in nonspecialized laboratories.
monomers and dimers was 130−200 and 270−400 Da, respectively.50 Gellerstedt et al.51 analyzed two bio-oils obtained via pyrolysis of spruce sodium lignosulfonate and pyrolysis of the isolated lignin from steam-exploded birch wood in the presence of formic acid and alcohol in order to elucidate the degradation mechanisms in the pyrolysis process. GPC, as well as electrospray-mass spectrometry (ESI-MS), were used for the determination of molecular mass distributions. Average molecular weight ∼300 Da and molecular weight range ∼100−1100 Da were determined using these methods.51 2.4. High-Resolution Mass Spectrometry (HRMS). The characterization of mixtures by HRMS is based on the fact that each different elemental composition (CcHhNnOoSs) has a different exact mass, which can be measured by high-resolution mass spectrometers with sufficiently high mass resolving power and accurate mass measurement.86 The mass resolving power refers to the ability of separating two narrow mass spectral peaks and the mass accuracy defines the m/z measurement error. The mass accuracy usually increases with increasing mass resolving power resulting in the increased confidence in which the peak assignments can be made upon the m/z.87 The required mass resolving power and accuracy of the mass measurement depend on the complexity of the analyzed sample. Marshall and Rodgers detected more than 20000 components in petroleum oils using the Fourier transform ion cyclotron resonance-mass spectrometry (FT-ICR-MS) and introduced a new methodology for analyzing and understanding of these complex mixtures at the molecular level known as “petroleomics”.86,88 The petroleomic analysis was also applied for the characterization of bio-oils and enabled the detection and identification of thousands of bio-oil components.30,53−56 The molecular mass and elemental composition were not the only information obtained by HRMS; the detected bio-oil components could usually be grouped into homologous series, which were characterized by a heteroatom number (NnOo), carbon number and double bond equivalents (DBE).30,53−56 The DBE parameter provides an information about the number of rings and double bonds (degree of unsaturation), and it can be calculated for a general molecule CcHhNnOoSs from eq 1.86 Besides the high number of detected components, high-resolution mass spectrometry is able to analyze also nonvolatile, high-molecular components, which are not detectable by gas chromatography. DBE = c − (h /2) + (n/2) + 1
(1)
One of the drawbacks of HRMS might be the discrimination of some compounds in the ionization process. Until now, there is no versatile ionization technique, which could be applicable for all compounds regardless of their molecular mass distribution or polarity. Hence, some compounds in complex mixtures, such as bio-oils, may not be ionized at all, using only one ionization method and combination of different ionization
Table 4. Overview of the Properties of the Selected High-Resolution Mass Spectrometers87 mass spectrometer
resolving power (fwhm)a
mass accuracy
mass range
linear dynamic range
TOF Orbitrap FT-ICR
up to 40 000 up to 150 000 up to 1 000 000
5 ppm 1−5 ppm 100 000 6000 >10 000
106 103−104 103−104
fwhm definition of the mass resolving power: resolving power = m/Δm50, m ... molecular mass, Δm50 ... mass spectral peak width at half-maximum peak height
a
394
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180−215 carbonyls 163−215
aromatics 103−163
carboxylic acids, esters ketones, aldehydes 165−180 180−215 aromatics, heteroaromatics aldehydes
4.4−6
3−4.4
6−8.5 9.5−10.1
95−165
6−8.25 8.25−11 11−12.5
aliphatic alcohols, nonconjugated alkenes, aromatic ethers aromatic and conjugated alkenes aldehydes and phenols carboxylic acids 4.2−6
70−103 55−95
alcohols, ethers,phenolicmethoxys, sugars aromatics, olefins
3−4.2
165−180
aromatics, heteroaromatics acids, esters, amides aldehydes, ketones 95−165
aliphatics alcohols, sugars 0−55 55−95
alkyl methoxy, hydroxy carbohydrate 1−54 54−70
aliphatics aliphatic protons, protons α-to carbonyls ethers and methoxy 0−2 2−3 short aliphatics long and branched aliphatics 0−28 28−55
alkanes aliphatics α-to heteroatom or unsaturation alcohols, methylenedibenzene methoxy, carbohydrates 0.5−1.5 1.5−3
chemical shift [ppm] H NMR assignments C NMR assignments
13
chemical shift [ppm] H NMR assignments
1
chemical shift [ppm]
Mullen et al.57
Table 5. Chemical Shifts in 1H and 13C NMR Spectra of Bio-oils57−59
chemical shift [ppm]
1
Joseph et al.59
chemical shift [ppm]
13 C NMR assignments
Strahan et al.58
Smith et al.53 performed the petroleomic analysis of the pyrolysis bio-oil for the first time. In this work, the bio-oil produced by a fast pyrolysis of a loblolly pine was studied using laser desorption ionization-linear ion trap−orbitrap mass spectrometry. The detection of 136 components with 3−6 oxygen atoms and DBE 9−17 was described. Few limitations were noticed; some interference was observed due to the laserinduced aggregation and some volatile components were not detected due to their evaporation in the ionization process. Moreover, using this approach, cellulose pyrolysis products could not be detected as well (due to the inability of nonaromatic pyrolysis products of cellulose to absorb laser power). It was shown that unlike petroleum oils, some major precursor ions such as m/z 272, 284, 296, and 308, which represent O4 compounds with DBE 9, 10, 11, and 12, could be isolated with no or only a negligible amount of interference and tandem mass spectrometry (MS-MS) of these ions can be performed.53 Smith et al.54 compared capabilities of high-resolution mass spectrometers with three different analyzersorbitrap, Fourier transform ion cyclotron resonance (FT-ICR), and quadrupoletime-of-flight (Q-TOF) in the petroleomic analysis of bio-oil using negative-ion electrospray ionization. Some minor differences in mass discrimination were observed. In FT-ICR-MS spectra, higher relative ion abundances for m/z 200 or more were observed, in comparison with orbitrap-MS and Q-TOF-MS. On the other hand, mass discrimination for ions m/z 131 and lower was observed in FT-ICR-MS. Negative-ion-electrosprayFT-ICR-mass spectrometry was used for the chemical analysis of bio-oil due to the highest mass resolving power. More than 800 bio-oil components with 2−12 oxygen atoms and DBE 1−14 were detected. Only 40 of them were previously identified by GC-MS. The limitation of this approach was an inability to detect aprotic and nonpolar components, as they were not ionized using the electrospray in the negative-ion mode.54 Jarvis et al.55 performed the analysis of oily and aqueous phases of pine pellet and peanut hull bio-oils using negative ion-electrospray-FT-ICR-mass spectrometry. More than 8000 peaks in the pine pellet oil and ∼16 000 peaks in the peanut hull bio-oil were detected. The molecular weight distribution of the detected components was 150−800 Da and the number of oxygen atoms and DBE was 2−14 and 0−20, respectively.55 Olcese et al.56 combined for the first time HRMS and GC × GC for the characterization of the raw bio-oil produced by the pyrolysis of lignin and hydrotreated bio-oils in order to assess the selectivity of the hydrotreatment catalysts. The negative-ion laser desorption ionization-FT-ICR mass spectrometry, negative-ion electrospray-FT-ICR mass spectrometry, and GC × GC were successfully applied for the characterization of the biooil compounds. More than 600 C8−C37 compounds were detected using both HRMS techniques. The molecular weight range of the components detected by these methods was ∼170−650 Da. GC × GC-MS-FID enabled the identification and quantification of over 170 C2−C14 components.56 Liu et al.30 analyzed red pine bio-oil and bio-oil fractions obtained by solvent fractionation of bio-oil (see Figure 2) using negative ion-electrospray-FT-ICR-mass spectrometry and GCMS. Components with 2−17 oxygen atoms, 4−39 carbon atoms, and DBE 1−22 were identified. N1Ox compounds with 6−30 carbon atoms and DBE 1−16 were also detected. The molecular weight range of components of bio-oil and bio-oil fractions was 150−700 Da.30 2.5. Nuclear Magnetic Resonance (NMR). The number of papers dealing with the utilization of NMR (especially 13C NMR)
13 C NMR assignments
Review
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Energy & Fuels
Review
applied for the characterization of carbonyls or other oxygencontaining functional groups.37,45,60−64 The absorption bands that can be found in FTIR spectra of bio-oil are presented in Table 6. Lievens et al.60 studied the distribution of carbonyl groups in different bio-oils prepared via pyrolysis of mallee wood, bark, and leaves using FTIR and GC-MS. The FTIR spectra in the range 1490−1850 cm−1 were deconvoluted with following 9 Gaussian bands: 1767 cm−1 (lactones), 1740 cm−1 (unconjugated alkyl aldehydes and alkyl esters), 1713 cm−1 (carboxylic (and fatty) acids), 1696 cm−1 (unsaturated aldehydes and ketones), 1654 cm−1 (hydroxy unsaturated aldehydes and ketones), 1606 cm−1, 1565 cm−1, 1517 cm−1, 1501 cm−1 (substituted aromatics), in order to obtain relative concentrations of different types of carbonyls. Using this approach, different concentrations of carbonyls were detected in studied bio-oils, which correlated well with the content of cellulose, hemicellulose, lignin, and extractives in the pyrolysis feedstock. The spectral deconvolution method was verified by the esterification of the bio-oil with methanol in the presence of an acid catalyst. As expected, a significant reduction of the carboxylic acid band (1713 cm−1), unsaturated aldehydes and ketones band (1696 cm−1) and an increase of the esters band (1740 cm−1) was observed.60 Scholze and Meier61 studied the composition of eight pyrolytic lignins obtained from bio-oils from different fast pyrolysis processes. FTIR was used to determine the present functional groups. It was observed, that a change in the oxygen content significantly affected the intensities of absorption bands of carbonyl species (1701 cm−1, 1652 cm−1, and 1600 cm−1) in FTIR spectra. On the other hand, the bands of other oxygencontaining functional groups, such as hydroxyl and methoxyl, were less affected. Hence, absorption bands of carbonyls were proposed to be used for studying the aging processes of biooils.61 Hilten and Das65 compared three methods based on the viscosity increase, formation of solids, and the onset of oxidation under heat for the assessment of the stability of pine pellet batch bio-oil, peanut hull pellet batch bio-oil and ethanol spray-condensed pine pellet bio-oil from continuous pyrolysis. Each method involved an accelerated aging procedure and determination of oil stability. The effect of the methanol stabilization of the oils was also studied. FTIR was used for the evaluation of the functional group changes during the aging process. The increase of the relative concentration of C−O (phenols, carboxylic acids, ethers, and esters) and CO (carbonyl) groups was observed in the aged samples. The methanol stabilization reduced viscosity and positively affected the stability of the bio-oil samples, which resulted in the reduction of the functional group changes in FTIR spectra.65 Tripathi et al.66 used a reflection−absorption based nearinfrared spectrometry for the prediction of the water content in bio-oils. The developed method enabled successful prediction of the water content in bio-oils in the range 16−36 wt % with a high coefficient of reliability (R2 > 0.85) and with error less than 2%, which indicated the possible utilization of this method for predicting the water content in bio-oils during bio-oil production.66 2.7. Specific Characterization of Individual Classes of Oxygenated Bio-oil Components. 2.7.1. Phenols. Phenols contained in bio-oils originate from the lignin decomposition. Some phenolic components can be used after separation and purification as food antioxidants, gasoline additives, as precursors
in the bio-oil analysis is increasing. One of the reasons is the ability of NMR to characterize almost the entire bio-oil sample including the high-molecular components (unlike chromatography).57,58 1H and 13C NMR were usually used in the bio-oil analytics to determine the percentage of different carbon and hydrogen atoms in the sample (aliphatic, olefinic, aromatic, methoxy/hydroxy, carbonyl, etc.).57,58 31P NMR can be used for the quantification of hydroxyl and carboxyl functional groups in the bio-oil samples after their derivatization with 2-chloro-4,4,5,5-tetramethyl-1,3,2-dioxaphospholane. 51 Nevertheless, the percentage of different hydrogen and carbon atoms in the functional groups and quantification of hydroxyl and carboxyl groups are not the only possible applications of NMR for bio-oil characterization. Mullen et al.57 used 1H, 13C a 13C DEPT (Distortionless Enhancement Polarization Transfer) NMR for the analysis of six bio-oil samples from different feedstock (switchgrass, alfalfa stems, guayule plant and bagasse, corn stover, chicken litter). The NMR spectra were recorded in the solution-state and acetone-d6 was used as the solvent. 1H and 13C NMR were used to quantify the functional groups containing hydrogen or carbon atoms, respectively. For this purpose, the obtained spectra were subdivided into several chemical shift regions corresponding to different functional groups present and spectral intensities of the individual regions were quantified. The regions classified in 1H and 13C experiments are presented in Table 5. The 13C DEPT experiment enabled the classification and quantification of the carbon atoms based on the number of attached protons and chemical environment. The NMR technique provided information about the concentrations of different functionalities in bio-oils and allowed estimating the alkyl chain length of the alkanes present, or the degree of their branching, which is very important information for the upgrading of bio-oil fuel properties. The results from these analyses make it possible to determine the feedstock and pyrolysis process conditions to produce bio-oils with the desired properties (degree of branching or saturation, aromatic or carbonyl content, etc.).57 Strahan et al.58 analyzed 15 bio-oils produced from different feedstock and two fossil fuel samples (gasoline and diesel) using 13C and DEPT NMR in order to characterize the composition of their functional groups in relation to their energy content. The obtained spectra were divided into five chemical shift regions (see Table 5) and percentages of the functional groups, as well as the carbon proton substitution numbers were determined. A principle component analysis (PCA) was further used to extract more information from the 13C NMR spectra. PCA enabled rapid discriminating between bio-oil composition based on the chemical functional groups and the resulting clusters were in good correlation with the biological origin and energy content of bio-oils examined.58 Joseph et al.59 proposed chemical shift ranges for the assignment of the NMR data. For this purpose, more than 50 model compounds were chosen (compounds previously detected in bio-oils and selected natural plant products), and their 1H and 13 C NMR spectra in DMSO-d6 were acquired. On the basis of obtained data, the library consisting of 352 proton and 383 carbon chemical shifts was created. The proposed chemical shift ranges are presented in Table 5.59 2.6. Fourier Transform Infrared Spectroscopy (FTIR). FTIR allows the characterization of the entire bio-oil sample, regardless of the volatility of the compounds present. Moreover, this method is simple, cheap and fast. FTIR was usually 396
dx.doi.org/10.1021/ef402047y | Energy Fuels 2014, 28, 385−402
Energy & Fuels
Review
Table 6. Absorption Bands in FTIR Spectra of Bio-oils.37,45 wavenumber [cm−1]
type of vibration
compound class
3600−3200 3100−3000 2980−2870 2350−2000 1850−1650 1650−1580 1550−1490 1470−1350 1300−950 915−650
O−H, N−H stretching C−H stretching C−H stretching CC stretching CO stretching CC stretching NO2 stretching, N−H bending, aromatic CC stretching C−H bending C−O stretching, O−H bending C−H in-plane bending
phenols, alcohols, water, carboxylic acids, amides, amines aromatics alkanes alkynes, cyanides aldehydes, ketones, carboxylic acids, esters alkenes nitrogenous compounds, aromatics alkanes alcohols, ethers aromatics
extraction and determination of phenols. The extraction of the sample was performed from the organic to the aqueous phase. The obtained extract was directly introduced to the LC-LC system. Diode array detector (DAD) and mass spectrometer (MS) were used for the detection. The possibility of using online capillary electrophoresis-mass spectrometry (CE-MS) instead of LC-LC was also tested. Both methods were applied for the determination of C6−C10 phenols in the bio-oil. The extraction efficiency of MMLE was ∼12% for most phenols. However, the extraction was reproducible and limits of detection were good (0.01−0.12 wt %) because of the large amount of the extracted sample (5 mL). MMLLE enabled simple, fast, and reliable pretreatment of the bio-oil sample. Using MMLELC-LC, good reliability and sensitivity was achieved. In comparison to CE-MS, a more reliable identification of the analytes was achieved using MMLE-LC-LC.92 Rover et al.93 evaluated the Fiolin-Ciocalteu (FC) method (used in the food industry for quantifying total phenols in wine) to determine total phenols in red oak bio-oil. The method is based on the reduction of the FC reagent (mixture of tungsten and molybdenum oxides) by phenolic compounds and subsequent UV−vis analysis (the products of the metal oxide reduction have a blue color that absorbs the light in the visible range). The accuracy relative to interferents using positive (phenol, 4-methylphenol, 3-ethylphenol, guaiacol, syringol, and eugenol) and negative controls (sugars, furfural, and acids) was investigated. For all positive and negative controls, potential interference with the quantification of total phenols by the presented method was calculated using data obtained when adding positive (contributor) and negative controls (interferent) into bio-oil using usual concentrations found in bio-oil. It was found that the influence of interferents (error in the prediction of phenolic content) was very small even for large concentrations of interferents. For usual concentrations of nonphenolic compounds in bio-oil, the error in the prediction was
