Methods and Challenges in the Determination of Molecular Weight

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Methods and Challenges in the Determination of Molecular Weight Metrics of Bio-oils Anne E. Harman-Ware†,‡ and Jack R. Ferrell, III*,‡ Biosciences Center, National Renewable Energy Laboratory, ‡National Bioenergy Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States

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†

ABSTRACT: The analyses of thermochemically derived bio-oil properties and composition are challenging due to the diversity of compounds present and the reactivity of the oils. There are currently a variety of techniques used and no standard method established for the analysis of the molecular weight distribution, weight-average molecular weight (Mw), and other molecular weight metrics of bio-oils. This review focuses on the challenges and variation in methodologies employed for the analysis of bio-oils on the basis of molecular weight, particularly by gel permeation chromatography (GPC). GPC is the most practical means for determination of molecular weight metrics of bio-oils but needs to be refined using appropriate standards and/or detectors to ensure consistency and accurate quantification of molecular weight metrics. Future method development for a robust technique with accurate and comparable molecular weight data should focus on GPC with multiple detection methodology on whole bio-oils, verified relative to another technique such as mass spectrometry (MS). MS techniques, such as Fourier transform-ion cyclotron resonance (FT-ICR MS), have also been utilized for the determination of molecular weight distribution of bio-oils and are briefly addressed in this review. Many MS methods can provide extensive characterization and structural speciation of components in bio-oils, and while accurate molecular weight metrics can be obtained with the appropriate use of ionization techniques and optimized parameters to ensure an appropriate range of m/z and signals representative of abundance, MS is not a robust or an economically practical method for routine molecular weight analyses. Physical separation techniques such as preparative scale GPC, distillation, and liquid−liquid extraction methods are also briefly addressed in this review in the context of molecular weight analyses.

1. INTRODUCTION Fast pyrolysis, the rapid thermal degradation of a material in an oxygen-deficient environment, is used to convert biomass to liquid (bio-oil) products and can be performed in the presence of a catalyst to produce oil with a lower oxygen content; if a catalyst is used, the process is termed catalytic fast pyrolysis (CFP). Raw pyrolysis (also known as fast pyrolysis, FP) and CFP oils are reactive and contain many types of compounds, rendering them difficult to separate and analyze. The components of the bio-oils originate from the lignin and holocellulosic portions of lignocellulosic biomass and include phenolic and aromatic compounds, furans, anhydrosugars, and sugar-based oligomers, as well as low molecular weight acids and aldehydes.1−8 Bio-oils from microalgal feedstocks also consist of aromatics but also contain more nitrogen-containing compounds (from the protein in the feedstock) and lipidderived species such fatty acids and alcohols.9−11 Bio-oils age over time and with temperature fluctuations, and hence their compositions and properties (such molecular weight distributions) are subject to change.12−14 Other thermochemically derived bio-oils such as hydrotreated oils and hydrothermal liquefaction (HTL) oils share similar properties with pyrolysis oils but typically have a different composition such as lower oxygen content. Since lignocellulosoic-based bio-oils are obtained from the thermal decomposition of carbohydrate and lignin polymers, molecular weight metrics of the resulting products, and particularly the oligomers, needs to be obtained to inform accurate and reliable analyses of the properties of the oil, degree of deconstruction, and recalcitrant nature of the © XXXX American Chemical Society

feedstock as well as the upgrading and conversion process. Analysis of the molecular weight distribution of bio-oils is also particularly important for comparisons of raw versus upgraded oils and to elucidate aging behavior and stability. Currently, there is no accepted standard methodology for the quantification of molecular weight metrics for bio-oils. Though no standard method has been accepted by the community, many different techniques, methods, and sample preparations have been used to analyze the molecular weight distribution of various types of bio-oils. This body of work has produced varied results, which shows that further method development is needed to address this gap in standard bio-oil analysis. Based on the literature available and method development suggested here, a standard, accurate, and reproducible method for molecular weight analysis of bio-oils should be achievable. The goal of this manuscript is to review the available techniques and methods and associated limitations and advantages for the quantification of molecular weight metrics for thermochemically derived bio-oils including raw FP and CFP oils as well as hydrotreated and HTL bio-oils. Future method development for molecular weight determination of pyrolysis oils based on accuracy, robustness, and practicality for widespread standardization and implementation will be suggested. Particular focus in this manuscript has been placed on gel permeation chromatography (GPC) due to its Received: June 18, 2018 Revised: August 23, 2018 Published: August 27, 2018 A

DOI: 10.1021/acs.energyfuels.8b02113 Energy Fuels XXXX, XXX, XXX−XXX

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Table 1. GPC Capabilities Depend on the Analyte Type, the Detection Method Employed, and the Calibration Technique (Adapted from ref 78) GPC technique - detection

molecular weight

molecular size

information/relevance

refractive index, UV

relative to standards

no

viscometer

universal calibration necessary absolute

hydrodynamic radius

absolute

hydrodynamic radius and radius of gyration

light scattering triple (UV or RI− viscometery−LS)

radius of gyration

concentration, molecular weight signal intensity sensitive to distribution functional groups conformation, branching, works with copolymers conformation, branching, >200 g mol−1 aggregation comprehensive

weight fraction of polymers of varying molecular weights. The number-average molecular weight, Mn, is defined as

practicality for standardization and on Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) based on its accuracy, high mass resolution capabilities and the abundance of work available to review. Molecular weight metrics of various thermochemically derived oils have been briefly addressed in previous reviews that encompassed a wide variety of bio-oil properties, albeit they lacked thorough discussions of difficulties and limitations in the analyses of the oils.15,16 This manuscript is not meant to comprehensively cover all molecular weight metrics ever established for all thermochemically derived bio-oils ever generated, but it will elucidate the discrepancies in the analyses, the wide range of values that have been obtained, and also the challenges and benefits associated with the different methodologies that have been implemented.

Mn =

∑ NiM i ∑ Ni

The Mn of a polymer assumes that there are the same number of molecules on either side of a distribution of molecular weight values. The polydispersity index, PD, of a polymer is defined as Mw/Mn and measures the span of the molecular weight distribution of a polymer. For example, if all chains in a polymer are equal length, the PD = 1, whereas if the number of chains varies greatly in length, the PD is larger. Other size or molecular weight metrics such as the peak molecular weight, Mp, the viscosity average molecular weight, Mv, and the higher or size average molecular weights Mz and Mz+1 can also be determined for polymers using GPC. Mp is the maximum for peaks in a chromatogram and is useful for straightforward comparisons. Mz and Mz+1 measurements are sensitive to larger polymers and can be indicative of their mobility (i.e., diffusion) properties. A polymer with a chain of a specific volume will have a particular influence on the viscosity of the solvent solution, known as the intrinsic viscosity (dL/g). The polymer’s intrinsic viscosity is related to its molecular weight based on the Mark− Houwink equation:

2. PRINCIPLES OF GEL PERMEATION CHROMATOGRAPHY (GPC) Gel permeation chromatography (GPC) is a liquid chromatography technique that utilizes a column (or a series of columns) packed with a porous, gel material to separate polymers or other analytes based on hydrodynamic volume. Smaller analytes take longer to elute because they must diffuse through the broader network of smaller pores, whereas larger analytes elute sooner, as they do not spend as much time in the pores. Analyte detection is typically achieved by ultraviolet detectors (UV, UV/vis, diode array, being “DAD”), refractive index (RI or dRI being differential refractive index or RID) detectors, viscometers, light scattering detectors (low angle, LALS; dual angle, DALS; multiangle, MALS), or a combination, as outlined in Table 1. GPC, a form of size exclusion chromatography (SEC), is used to determine the absolute or relative molecular weight of a polymer and/or its molecular weight distribution. SEC typically refers to size separation in aqueous media with different types of columns, but the methods often operate under the same general set of principles. Molecular weight is typically described by metrics such as molecular weight distribution, weight-average and number-average molecular weights, and polydispersity. The weight-average molecular weight of a polymer, Mw, is defined as Mw =

limitations

[η] = KM α

where η is the intrinsic viscosity, M is the molecular weight, and K and α are the Mark−Houwink parameters. If the Mark− Houwink parameters of a set of different polymer types are known, then the molecular weight of one polymer can be used to calibrate for the molecular weight of a different type of polymer. A calibration is constructed using the retention or elution volume (or also in the case of GPC chromatograms, time) of a polymer (with known molecular weight or determined Mp) vs molecular weight, assuming the unknown polymer either has the same Mark−Houwink parameters or they are accounted for in the calculations to determine molecular weight metrics of the sample. However, for unknown samples in solvents where the Mark−Houwink parameters are not known, they must be estimated or assumed relative to a polymer standard, which could potentially result in errors for absolute molecular weight determinations. Traditionally, GPC has been used for analysis of polymers, as described above. However, GPC can also be used for bio-oils, which contain a wide variety of components derived from lignin and carbohydrate polymers. Depending on the type of detection, calibration, solvent, and columns used, and the degree of upgrading that the bio-oil has undergone, GPC analyses may yield a wide range of molecular weight metrics for thermochemically derived bio-oils. Therefore, a standardiza-

∑ NiM i2 ∑ NiM i

where Mi is the molecular weight of a polymer chain and Ni is the number of chains with molecular weight of Mi (where molecular weight is reported in g mol−1 or Da). The Mw of a polymer depends on the number of molecules and their corresponding molecular weights as it is calculated using the B


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Figure 1. GPC chromatograms from RID signals showing molecular weight distributions from untreated and treated pine (A) and straw (B) pyrolysis oils, also pyrolysis oils generated from feedstocks impregnated with alkali and alkaline earth metals (AAEMs). Reprinted from ref 23 with permission. Copyright 2016 Elsevier.

sample preparation methodology such as the solvent used, filtering, and steps for avoiding agglomeration or aggregation (which can also be measured), all of which can make the standardization of a GPC method for the analysis of bio-oils’ molecular weight more complicated. While the analysis of molecular weight distributions of polymers relative to standards using RI and/or UV detection is typically sufficient and is robust for comparison purposes, this analysis procedure may not provide an accurate representation of bio-oils or enable comparisons across samples with large functionality differences. The bio-oils are made up of a variety of “polymer types,” each potentially having a different set of Mark−Houwink parameters in certain solvents (with different solubilities) and response factors to the RI or UV detectors. For example, the difference in the relative abundance of aromatic (or other π electron containing) analytes between different samples would result in different responses from a UV/vis DAD detector.17,23 An investigation of the difficulties in the analysis of raw FP oils (and model oils) by GPC with RI detection (and to a lesser degree, UV detection) was conducted by Hoekstra et al.17 They compared the real molecular weight values of model oils to those predicted by SEC using polystyrene calibrants in THF and concluded that the hydrodynamic volume to molecular weight ratio will determine whether the calibrant will over- or underestimate the real molecular weight of the compounds being analyzed. They also demonstrated that the response factor not only is compound-dependent but can also vary with concentration for RI detection, which could also have implications on the type of solvent used. Additionally, if the resolution of particular analytes is not sufficient, the RI intensity measured at a given retention time may not be an accurate representation of the intensity of the separate analytes. Therefore, when using RI detection, the intensity of the y-axis in a GPC chromatogram cannot be used to calculate accurate molecular weight values, and bio-oils with differing degrees of upgrade cannot necessarily be compared assuming the same Mark−Houwink parameters. Hoekstra also used the ratio of UV to RID response to understand the relative aromaticity and double bond content for the bio-oils analyzed. The authors gave a Mw

tion protocol will be paramount toward obtaining accurate and reproducible molecular weight metrics for bio-oils.

3. GPC ANALYSIS OF BIO-OILS GPC has advantages and shortcomings, particularly in the analysis of complex mixtures such as thermochemically derived bio-oils. For example, GPC requires that the entire sample be soluble in a solvent such as THF or DMF, which is typically reasonable for raw FP and CFP oils. For aqueous fractions of bio-oil produced from thermochemical processes, aqueous SEC could be used. Only a small amount of sample is required, and the analysis is not typically performed at elevated temperatures that could cause reactions of the analytes. However, GPC has many disadvantages. First, GPC is not typically capable of high-resolution separation of polymers in comparison to other analytical techniques such as mass spectrometry. Additionally, with the wide variety of analyte types and functionalities present in raw FP and CFP oils (essentially representing different types of polymers), appropriate solvents, columns, and standards for calibration have not yet been established. The variation in analytes in raw pyrolysis or upgraded oils also makes the determination of molecular weight an issue with respect to detection, as different analytes will have different functional groups and responses to detectors and will contribute differently to the intrinsic viscosity of the solutions being analyzed.17 In order to accurately calculate molecular weights of analytes in solution relative to standards in certain solvents using GPC with traditional RI or UV detectors, the intrinsic viscosity and/or Mark−Houwink parameters of the oil and/or analytes in solvents need to be known or established. The use of such parameters is required or assumed for bio-oil samples, as they must be calibrated against an external polymer standard which is a different polymer type. The same basic GPC and spectroscopic/ detection issues that have plagued the molecular weight distribution analysis of asphalts and lignins is also likely going to be a problem for bio-oils as they contain polyaromatic or asphaltene-like components.18−22 For example, column− analyte interaction, solvent−analyte interaction and concentration, detection response variation for different analytes, and other properties and processes will need to be considered for a C


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Figure 2. GPC variable wavelength detector (UV 254 nm) signals showing molecular weight distributions of untreated (A) and treated (B) pine pyrolysis oils obtained from different reaction temperatures. Reprinted from ref 23 with permission. Copyright 2016 Elsevier.

of 302 g mol−1 for the analysis of raw FP oil, though they did not comment on errors in the analysis. Another study by Oudenhoven et al.23 (see Figures 1 and 2) compared the molecular weight distributions (MWD) of untreated and acid-leached pine and straw-derived raw FP oils (also with and without alkaline and alkaline earth metals (AAEMs) generated at different temperatures using both RI and UV (254 nm) detectors. Differences in the pyrolysis products and corresponding molecular weight distributions between the two feedstocks were more evident in the chromatograms obtained using a RID since it was able to detect more sugar-based pyrolysates (nonaromatic species), whereas the UV detector, according to the authors, “only detects aromatics and transitions involving π electrons.” The effect of acid leaching of the pine on the molecular weight distribution of the resulting bio-oil was apparent in the GPC RID signal, which indicated an increase in anhydrosugars of molecular weight 100−200 g mol−1 for the acid-leached feedstock. Overall, the MWD of the oils indicated the products were between 100−1000 g mol−1 with the majority being 100−200 g mol−1. Additionally, the variation in the properties of the components of the bio-oils may lead to different solubilities in solvents, and thus the analyses of bio-oils in different solvents may vary in the results reported. To demonstrate the importance of solvent as well as columns, Garcia-Perez et al.24 found that DMF was a better solvent than THF for the analysis of hard- and softwood raw FP oils, as well as fractions separated by solvent fractionation. They explained the SEC separation of analytes partly on their adsorption to the column (another important variable to consider) while in DMF, and polyethylene glycol polymers (as opposed to the typical polystyrene) were used as calibrants. GPC chromatogram peaks were deconvoluted and described on the basis of heavy and light compounds, their origin (lignin vs sugar), and their solubility in particular solvents. The heaviest compounds quantified were nonpolar (lignin-based, water and dichloromethane insoluble) and ranged from about 600−2000 g mol−1. The heavy polar compounds (water-soluble) were approximately 600 g mol−1. Lower molecular weight compounds were

described as being volatile (∼100 g mol−1), monolignols (∼200 g mol−1), and extractives and sugars (∼400 g mol−1). As pointed out in a study by Sholze et al., hydrogen bonding of the various functionalities present in pyrolysis oils with the solvent (THF in particular) may also change the solubility of the oils and impact their interactions with the columns and hence the chromatography of the sample, which would then be manifested in the molecular weight distributions calculated.25 Despite limitations in the determination of accurate molecular weight metrics, GPC coupled with single detection such as UV or RI may still be useful for comparisons as long as functionalities/polymer types are known to be similar and column−analyte interaction is minimized. The coupling of multiple detectors or the utilization of ratio integration signals (such as the ratio of UV/RID signals17) can provide additional useful information and more accurate molecular weight metrics for unknown and complicated mixtures, such as raw FP and CFP bio-oils. For example, Choi et al. used a number of analytical methods to try to obtain high mass balance closure for the full composition of bio-oil.4 They used GPC with RID and UV detection to help determine that a large portion of the bio-oil was pyrolytic lignin and dimers and oligomers of sugars/carbohydrates. On the basis of their GPC method, the pyrolysis bio-oil had a Mw of approximately 400 g mol−1. In a recent round robin study by Elliot et al., various feedstocks underwent fast pyrolysis at different institutions to demonstrate that raw pyrolysis oils from the same feed are not necessarily the same across different reactor configurations and conditions.26 The pyrolysis oils were all analyzed on one site for comparison using a GPC method that used DMSO/0.1 wt % LiBr as a mobile phase, polyethylene glycol standards, and UV detection (254 nm). The authors used GPC to elucidate a correlation between the molecular weights of the bio-oils (ranging from ∼600−1600 g mol−1 with the molecular weight of the corresponding isolated pyrolytic lignins (∼ 1000−2500 g mol−1), while there was not an observable correlation between wt % pyrolytic lignin and its corresponding molecular weight. Iisa et al. used GPC to compare CFP bio-oils (UV detection, polystyrene standards) generated from in situ and ex situ catalytic upgrading of pine pyrolysis vapors using different D


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Figure 3. GPC (UV detector) chromatograms showing molecular weight distributions of bottom (left) and top (right) catalytic fast pyrolysis oils obtained under different reaction conditions. Reprinted with permission from ref 27. Copyright 2016 American Chemical Society.

Figure 4. GPC chromatograms showing molecular weight distributions of pyrolysis oil feeds and corresponding HDO products under various reaction temperatures from (a) aqueous fraction after water addition (AFWA), (b) oil fraction after water addition (OFWA), and (c) whole pyrolysis oil. Reproduced from ref 28 with permission. Copyright 2011 Royal Society of Chemistry.

Da to ∼2000 g mol−1 (see Figure 3).27 Another important application of GPC analysis is related to the stability and aging of bio-oils. It has been found that pyrolysis bio-oils exhibit increases in viscosity and molecular weight upon aging, and first order reaction kinetics have been used to predict

catalysts. Chromatograms (normalize-scaled to largest peaks) showed that the “bottom” oils, which did not vary greatly overall in chemical properties if separated by top and bottom fractions, also did not vary drastically in molecular weight distribution and consisted of components ranging from ∼100 E


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Figure 5. GPC-RID chromatograms of pyrolysis oils and products obtained from liquefaction conditions in quaiacol (a) and chromatograms obtained of guaiacol-liquefaction bio-oil separated into heavy and middle molecular weight fractions using preparative scale GPC (b). Initial oil contains quaiacol from liquefaction conditions. Reprinted from ref 29 with permission. Copyright 2015 from Elsevier.

molecular weight changes in bio-oils as observed by GPC.12 Therefore, an accurate and reliable GPC method would help to track aging processes. Other feedstocks and types of thermochemically derived biooils have been studied and compared by GPC methods. Vardon et al. compared the molecular weights (Mw) of bio-oils generated from slow pyrolysis and HTL of two types of microalgae using GPC (RID, polystyrene standards).10 The pyrolysis oils had lower molecular weights than the HTL oils being ∼700−1000 g mol−1 and 1900−3300 g mol−1, respectively. The algal bio-oils also had PD values of 2−3, which are higher than typical pyrolysis bio-oils from lignocellulosic feedstocks (usually 1.5−2). De Miguel Mercader et al. used GPC to compare the molecular weights of raw and fractionated FP bio-oils against the corresponding hydrodeoxygenated (HDO) bio-oils (polystyrene calibrant, see Figure 4).28 The H/C and O/C ratios showed trends with molecular weight distributions, as can be expected if cracking and deoxygenation has occurred. However, if the functionalities changed during upgrading (hydrodeoxygenation), then the detector responses and distribution plots should be interpreted with caution, as described previously. Depending on the feedstock, MWD showed the majority of pyrolysis biooil and HDO products ranged from 100 to 1000, and some HDO bio-oils had significantly higher amounts of components ranging 1000−3000 g mol−1. Preparative scale GPC has also been performed as a means to separate and study the different molecular weight fractions of oils.29,30 For example, Castellvi ́ Barnés et al. analyzed middle and heavy molecular weight fractions of HTL bio-oils (obtained from guaiacol liquefaction) and raw FP bio-oil that were separated using preparative scale GPC (see Figure 5).29 Similar to results from Vardon et al.,10 the pyrolysis oil had a lower MWD at 100 Da given the parameters used to optimize for the analysis of heavier ions. Both ionization techniques indicated that polymerization occurred particularly among lignin-based phenolic compounds, though the phenolic compounds observed had slightly different chemical functionalities depending on which ionization technique was used. While the authors did not quote metrics such as Mw, they did indicate relative differences in heteroatom class abundances which would translate to differences in molecular weight measurements between the two ionization techniques. It should be noted that the authors reference the FT-ICR parameters used as having previously been reported as being optimized to the range of ions of interest to avoid mass or time-of-flight discrimination.45 Generally, the molecular weight range can be related to both the ionization technique and the ability to accurately represent the relative abundance of ions detected by the spectrometer. In order to account for instrumental artifacts and bias and to determine a flight time for the consistency of the FT-ICR data with other MS, Smith et al. compared the analysis of raw FP oil using Orbitrap, Q-TOF, and FT-ICR MS.45 (+) ESI, APPI, and APCI were all compared for the analysis of HTL oils derived from municipal solid waste in a study by Chiaberge et al.46 (see Figure 6). The ionization techniques gave different distributions of molecular classes but gave

similar numbers of assigned molecular formulas. The Mw value for ESI was 332.5 Da, 357 Da for APCI, and 341.6 Da using APPI. The authors suggested that APPI was the most appropriate ionization technique, as the spectral results more closely matched elemental composition data. (+) and (−) ESI were both used to analyze and compare FT-ICR MS data obtained from pyrolysis oil generated from three types of aquatic plants in a study by Santos et al.47 The majority of components detected ranged from about 150 to 550 m/z, centered around 200 m/z, and the different ionization techniques showed different relative abundances of heteroatom classes. A (−) DART FT-ICR MS was used to analyze “biotar” obtained from the pyrolysis of hardwood chips in a study by Lobodin et al.48 The authors reported parameters associated with trapping and transferring of ions generated and extensive references on their justifications. However, they were concerned that molecular weight distributions determined using a DART ionization technique would not accurately represent complex mixtures such as pyrolysis oils, due to the bias in low boiling compounds liberated whereas high boiling compounds would be underestimated. The resulting spectra, which revealed more than 6000 peaks between 150 and 900 m/z, showed similar results to (−) ESI FT-ICR analyses reported by Jarvis et al.49 LDI and (−) ESI FT-ICR methods were compared for the analysis of pyrolysis and hydrotreated oils (Organocell lignin being the feedstock) in a study by Olcese et al.50 LDI FT-ICR generated spectra from pyrolysis oils containing more than 600 peaks in a range covering 200− 650 m/z, whereas the (−) ESI method only generated about 300 peaks occurring mostly between 170 and 450 m/z. The LDI was also able to ionize more compounds of lower polarity, whereas the (−) ESI was biased toward ionization of polar compounds. This distinction in ionization techniques was particularly important when interpreting the data from the hydrotreated oil, as those components were less oxygenated (and less polar). As hydrotreated and HTL oils will have substantially different functionality from FP and CFP oils depending on the level of upgrade, it may be necessary to utilize multiple and/or different ionization techniques for FTICR analyses. As these authors also pointed out, the understanding of time-of-flight and mass discriminations must also be taken into account when analyzing data and optimized to provide the best quantitative representation of bio-oil components. HTL oils have been analyzed extensively by FT-ICR MS methods, particularly for relative distributions of the heteroatom classes for components present in the oils. FTICR using a (+) APPI ionization technique was used to analyze hydrotreated oils derived from municipal solid waste in a study by Leonardis et al.51 More than 2000 peaks were detected, most of which contained nitrogen and were compared based on heteroatom classes (O and N content) in plots of DBE vs nominal mass. While they varied depending on class, the compounds present in the oil were typically in the range of 200−500 m/z with DBE centered around 10. ESI (+ and -) FT-ICR MS was used to analyze hydrotreated, HTL, and raw FP bio-oils in a study by Sudasinghe et al.52 The FP oil showed significantly higher abundances of most heteroatom species than the HTL and hydrotreated oils, as was expected. The authors also mentioned the importance of adduct formation and accountability of all components, particularly with respect to (+) ESI as proton and sodium adducts could only account for a portion of oxygenated species present in the oils. In I


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Energy & Fuels another study by Sudasinghe at al.,9 HTL oil and the aqueous products from Nannochloropsis silina microalgae were analyzed by (+) and (−) ESI FT-ICR MS. The average molecular weight was 431 Da over approximately 4600 peaks for (+) ESI and 319 Da for (−) ESI that detected about 1700 peaks in the HTL oil. While the oils were characterized by NMR and other analyses, the FT-ICR MS was able to provide a more comprehensive profiling of the types and sizes of components present in the oils. It is common practice to subject bio-oils to liquid−liquid extraction or solvent fractionation schemes, in an effort to separate the oils based on chemical classes for more simplistic analyses and comparisons across different degrees of upgrade; although, most oils do not typically separate efficiently. Liu et al.53 used (−) ESI FT-ICR MS to analyze raw FP oil and its fractionation components based on fractions that were soluble/insoluble in either water, hexane, diethyl ether, dichloromethane, and methanol (see Figure 7). They found

They concluded that most species had a molecular weight in the range of 200−800 Da (indicating many are not analyzed by GC/MS due to volatility limitations) and O/C ratio of 0.4− 0.6. The authors did also indicate that the FT-ICR MS data were qualitative as the ion peak intensities were not necessarily related to abundance. FT-ICR analyses have also elucidated the presence and abundance of particular analytes in bio-oils that had not received attention previously. Furthermore, knowledge of these analytes is needed to close mass balance gaps in bio-oil analyses. For example, several different types of biomass feedstocks were used to generate pyrolysis oils characterized by (−) ESI FT-ICR MS in studies conducted by Jarvis et al.54 Their study elucidated the presence of typically neglected components such as nitrogenous species, boron-containing compounds, and resin acids (i.e., abietic acid) present in the pyrolysis oils. Tessarolo et al. used a combination of data obtained from GC × GC Time-of-Flight (TOF) MS and FTICR to provide a comprehensive characterization of pyrolysis oil species, spanning low molecular weight/low boiling compounds capable of GC analyses to larger compounds as seen using (−) ESI FT-ICR.55 FT-ICR MS provided a broader range of species in both carbon number and heteroatom classes in comparison to the GC × GC TOF MS.

7. OTHER MASS SPECTROMETRY TECHNIQUES FOR ANALYSIS OF BIO-OIL Other mass spectrometry techniques that have been used to analyze the molecular weight of components in thermochemically derived bio-oils include Orbitrap56−58 and time-of-flight (TOF)45,55 configurations that analyze samples introduced by either the ionization techniques discussed previously, or by gas chromatography (GC). Orbitrap mass spectrometers operate by trapping analyte ions in an electrostatic field, which then cycle around an electrode at a frequency dependent on the ions m/z. TOF mass spectrometry, unlike FT-ICR and Orbitrap, separates ions spatially. In TOF MS, a voltage applied to the analyte ions gives all ions the same kinetic energy (KE = 1/2 mv2 where m = mass and v = velocity). Having the same kinetic energy, ions of heavier mass travel slower than ions of lighter mass; ions are detected in order of increasing mass. TOF MS is particularly useful as it can analyze compounds with very high masses (m/z ∼ 106).59 It should be noted that while Orbitrap and TOF MS might have lower resolution than FT-ICR, these detectors may exhibit different mass discrimination than is seen from FT-ICR MS, which is particularly important for quantitative data and MWD analyses. For example, in a study by Bai et al. reporting on the analyses of lignin-derived pyrolysis oil comparing APPI FT-ICR MS with Orbitrap MS, the Orbitrap had a different bias for particular mass ranges than the FT-ICR.60 However, the spectral data from both MS techniques when taken together were comparable to the GPC chromatograms (showing Mw ∼ 300 Da) obtained from UV detection and were explained based on the presence of phenolic monomers and oligomers in the oil. On the other hand, instrumental parameters can be carefully adjusted to show relatively similar spectral patterns for bio-oil as analyzed by different MS detectors as shown by Smith et al. comparing FT-ICR, Orbitrap, and Q-TOF MS analyses of oakderived pyrolysis oil.45 Their report also includes a succint explanation on the inherent differences between the different spectrometers and associated data. Each mass spectrometry technique has advantages and limitations with respect to

Figure 7. (−) ESI FT-ICR mass spectra of bio-oil and subfractions (HS = hexane soluble, ES = ether soluble from water-soluble fraction, EIS = ether insoluble from water-soluble fraction, DS = dichloromethane soluble fraction from water insoluble fraction and MeS = methanol soluble fraction from water insoluble fraction). Reprinted with permission from ref 53. Copyright 2012 American Chemical Society.

that the bio-oil and its fractions ranged in average molecular weight of 330−430 Da, and compounds of molecular weight 150−700 Da were detected. The LLE fractionation did not clearly separate the bio-oil based on chemical species. Additionally, they reported that some fractions had larger molecular weight components than the starting material, indicating that some polymerization, condensation, or other agglomeration occurred during the fractionation process. Charon et al. plotted O/C vs molecular weight from FT-ICR (and GC/MS) data generated from a variety of pyrolysis oils and their fractions from a liquid−liquid extraction method.3 J


K

FT-ICR3,9,44−55

GPC-MALS21,22,31 preparative GPC29,30

GPC (RI and/or UV)4,10,12,13,17,23−28,75,79−85

methodology

Charon et al.3 Sudasinghe et al.9

Reyhanitash et al.85 Elliot et al.26

Kim et al.84

de Miguel Mercader et al.28 Agblevor et al.75 Oh et al.83

fractions of raw fast pyrolysis oil HTL oil and aqueous phase from Nannochloropsis salina microalage

raw oil, aged oil, waste-derived HTL oils, microalgae HTL oils

fast pyrolysis oil before and after hydrodeoxygenation hydrotreated fast pyrolysis oil with and without preesterification various raw fast pyrolysis oils, pyrolytic lignin; round robin study lignin, biopolymers and synthetic polymers raw fast pyrolysis and HTL oils

pyrolytic lignin before and after hydrodoxygenation

forest residue fast pyrolysis oil fractions after water treatment (organic and aqueous phases) and corresponding hydrodeoxygenation products catalytic fast pyrolysis oil from poplar

catalytic fast pyrolysis oils from pine

raw fast pyrolysis oil from pine (with and without acid pretreatment) hardwood and softwood bark raw fast pyrolysis oils, also fractionated by LLE pyrolytic lignin from raw fast pyrolysis oils

Oudenhoven et al.23 Garcia-Perez et al.24 Scholze et al.,25 Mullen et al.,79 Fortin et al.82 Iisa et al.27

Hoekstra et al.17

Haverly et al.13

raw fast pyrolysis oil and corresponding oil aged over various conditions raw fast pyrolysis oils collected as fractions from condensers and electrostatic precipitators at different temperatures and corresponding oils after “rapid aging” raw fast pyrolysis oils, hydrodeoxygenated oils, model compounds

pyrolytic lignin: Mw ∼ 1100 g mol−1, PD = 2.0; after hydrodeoxygenation (called “phenol polymer”): Mw ∼ 1100−1300 g mol−1, PD ∼ 3−4 raw FP oil: Mw = 500 g mol−1, PD = 2.7; light upgraded oil: Mw ∼ 200−300 g mol−1, PD: 1.2−1.6; heavy upgraded oil: Mw ∼ 400−700 g mol−1, PD: 1.8−2.9, reduction in O/C content and changes in functional groups with upgrading were noted majority of products below Mw ∼ 1100 g mol−1, esterification step may prevent self-polymerization and produced chromatograms with lower MWD, oils had similar O/C ratios but different H/C correlation between pyrolytic lignin molecular weight (∼1000−3000 g mol−1) and whole oil molecular weight (∼500−2000 g mol−1) attributed to UV detection favoring aromatic species, although no correlation between pyrolytic lignin content with molecular weight no studies on thermochemically derived bio-oils of varying levels of upgrade using GPC-MALS with comparison to other techniques partial oil fractionation (medium Mp ∼ 300 g mol−1 and heavy Mp ∼ 1000 g mol−1), removal of liquefaction solvent, different fractions had different chemical compositions; 29 low molecular weight phenolics separated using GPC on small scale for quantitative purposes30 the ionization technique and associated parameters affect the data and measured molecular weight distributions; depending on the degree of upgrade, a particular ionization technique may be necessary along with appropriate MS conditions and parameters to ensure quantitative data; typical Mw ∼ 200−1000 g mol−1, PD ∼ 1−2, depending on feed and process; other important metrics measured include heteroatom and chemical classes, DBE, carbon number, etc. plotted O/C as a function of molecular weight, most species between 200 and 800 g mol−1 used (+) and (−) ESI FT-ICR MS spectra for both aqueous and organic fractions, most compounds between 200 and 600 g mol−1, (+) ESI gave average Mw of 431 g mol−1, (−) ESI gave 319 g mol−1, more peaks detected in (+) ESI of oils and larger molecular weight abundances in comparison to aqueous phase

Mw ∼ 200 g mol−1

Mw ∼ 600−1300 g mol−1 for bottom oils, top oils had majority of signal appearing around 100 g mol−1, comparisons made on the basis of normalized UV detection majority of MWD between 100 and 1000 g mol−1, hydrodeoxygenation did not reduce apparent molecular weight substantially based on GPC chromatograms

errors in molecular weight predictions using UV detection and RI response factors and from assumptions relating to elution volume, detectors are dependent on functional groups and comparisons of raw and upgraded oils need to be made with caution, ratio of UV to RI response provides insight into conjugation and aromaticity compared RI to UV detection to see changes in oils aromatic content and relevance to MWD with changes in feedstock pretreatment and reaction conditions DMF and THF solvent differences, column differences, need different standards accordingly; Mw ∼ 400−500 g mol−1, “heavy” fractions ranged Mw ∼ 500−2000 g mol−1 ref 25: Mw ∼ 600−1300 g mol−1 with similar values from both RI and UV detectors; ref 79: RI showed most from ∼90−500 g mol−1, ∼20−30% > 1500 g mol−1; ref 82: RI, Mw ∼ 1400−2000 g mol−1 with PD ∼ 2−3

Mw ∼ 400 g mol−1, Mn ∼ 200 (PD ∼ 1.8) RI detector, HTL: Mw ∼ 2000−4000 g mol−1, Mn ∼ 700−1300, PD ∼ 2.5−3.2; pyrolysis oil: Mw ∼ 700−1000 g mol−1, Mn ∼ 300− 400, PD ∼ 2.2−2.8; Scenedesmus consistently higher MWD than Spirulina, defatted Scenedesmus generated heavier HTL oil but lighter pyrolysis oil Mw ∼ 500 g mol−1, aging increased up to Mw ∼ 900 g mol−1, aging also increased water content and viscosity, aging properties attributed to condensation reactions before aging: Mw ∼ 100−500 g mol−1, after aging: 50−250% increases in relative Mw, samples with high TAN had highest increases in Mw

findings degree of upgrade (functionality differences) will influence which type of detector (and associated parameters) needed as well as standards to be used for accurate analyses; typical Mw ∼ 200−1300 g mol−1, PD ∼ 1−3, depending on feed and process

bio-oil type raw, catalytic, and hydrotreated/hydrodeoxygenated fast and slow pyrolysis oils, aged fast pyrolysis oils, fractionated fast pyrolysis oils, pyrolytic lignin, HTL oils, microalgal HTL oil raw oak-derived fast pyrolysis oil slow pyrolysis oil and HTL oil from Scenedesmus (raw and defatted) and Spirulina microalgae

Czernik et al.12

Choi et al.4 Vardon et al.10

reference

Table 2. Summary of Select Literature References Pertaining to the Determination of Molecular Weight Metrics of Thermochemically-Derived Bio-oils

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L

distillation71−77

Orbitrap, TOF-MS45,55−58,60

methodology

Table 2. continued

Tessarolo et al.55 Bai et al.60

Sudasinghe et al.52 Jarvis et al.54

Olcese et al.

50

Jarvis et al.49

Lobodin et al.48

Chiaberge et al.,46 Leonardis et al.51 Santos et al.47

raw and hydrotreaded fast pyrolysis oils, pyrolytic lignins

raw fast pyrolysis oil from oak, conifers, scotch broom raw fast pyrolysis oils from empty palm fruit bunch and pine corn stover lignin raw pyrolysis products recovered by cold solvent trapping (fresh and aged) raw pine and empty palm fruit bunch-derived fast pyrolysis oil, hydrolytic lignin, petroleum products

HTL oil and aqueous fraction from pine

pyrolysis products from freshwater biomass, compared to crude oil (petroleum) hardwood “bio-tar” as well as petroleum-derived products fast pyrolysis oil and aqueous phases derived from pine and peanut hull raw and hydrotreated fast pyrolysis oil from lignin

pine fast pyrolysis oil and solvent fractionation products HTL oil from municipal solid waste

Liu et al.53

bio-oil type raw oak-derived fast pyrolysis oil and aged

reference

Smith et al.44

findings

like FT-ICR, Orbitrap, and TOF results are dependent on ionization technique and oil type, TOF could see lower molecular weight oxygenates better than FT-ICR, peaks ranged from 100 to 6500 m/z, depending on ionization technique, molecular weight distribution centers around 300 g mol−1 laser-induced aggregation observed, different spectrometers can be optimized to yield comparable results and quantitative data upgraded oils capable of distillation and molecular distillation without coking

compared APPI-FT-ICR and Orbitrap MS; GPC-DAD results indicated fresh oil was ∼300 g mol−1 and aged oil was ∼400 g mol−1

(−) ESI FT-ICR MS, most peaks between 150 and 700 m/z, distribution centered around 200

(−) ESI FT-ICR MS, boron-containing species detected, focus on DBE, carbon number and heteroatom classes

(−) ESI displayed anions ranging from 170 to 450 m/z, (−) LDI between 200 and 650 m/z, more peaks and higher DBE detected from LDI, differences based on ionization efficiency (+) and (−) ESI FT-ICR MS, also low resolution linear ion trap MS, >4k peaks between 100 and 750 m/z

(−) ESI FT-ICR showed differences in spectra and heteroatom class distributions based on feedstock and phase

(−) DART, peaks between 150 and 900 m/z

(+) and (−) ESI, peaks ranged from 150 to 550 m/z, centered around 200 m/z

differences in relative component distributions between (−) ESI and (+) APPI based on ionization efficiency; APPI saw no difference in heteroatom class distribution, showed aged oil had higher oxygen content in higher molecular weight components, (−) ESI distinguished differences in phenolic and sugar-derived compounds based on DBE, majority of peaks detected between 100 and 500 m/z, oligomerization of phenolics occurs with aging (−) ESI, majority of peaks between 150 and 700 m/z with relative distribution differences in different solvent fractions, mean m/z of the fractions ranged from about 330 to 430 (+) ESI, (+) APCI, (+) APPI FT-ICR showed average Mw of 333, 357, 342 g mol−1, respectively, lower than those obtained using lowresolution linear ion trap MS, APPI spectral data best fit elemental composition data

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et al. separated pyrolysis oil by molecular distillation into heavy and light fractions, and the pyrolytic lignins that were isolated in each fraction were found to have weight-average molecular weights of 847 and 647 Da for the heavy and light fractions, respectively.74 Upgraded oils such as CFP and hydrotreated bio-oils have proven to be more easily distillable. For example, fractional CFP oil was distilled without leaving behind a high molecular weight residue as reported in a study by Agblevor et al.75 Additionally, hydrotreated pyrolysis oils have been successfully distilled to produce “light, gasoline, diesel, jet, and naphtha” fractions for analytical characterization purposes (although molecular weight metrics were not obtained).76,77 While physical separation techniques such as preparative scale GPC, distillation and liquid−liquid extraction may help in future upgrading strategies, scrupulous mass balance, and analytical characterization efforts, they are not always necessary for molecular weight analyses. Furthermore, they should only be implemented and interpreted with caution as these processes may induce changes to the properties and composition of bio-oils. Like other analytical methods and processing conditions, the degree of upgrade of the bio-oils under study will dictate the parameters necessary for physical separations based on molecular weight, whether they are based on solvent extraction, distillation, or some other process. However, at the very least, physical separation and solvent solubility research may still provide insight on the properties of bio-oils with respect to solvent−analyte interactions, condensation and aggregation, and other characteristics that may be unique or indicative of the bio-oil’s potential for use as a fuel or feedstock and could inform the parameters and procedures needed for accurate analyses.

analyzing complicated mixtures like pyrolysis oils. The resolution of the mass spectrometer, its ability to accurately and quantitatively analyze a particular molecular weight range, and how the sample is introduced (the ionization technique used) will all have consequences on the results of the analysis. Additionally, mass spectrometers are expensive, and the data analysis is not always straightforward for high throughput analyses, or amenable to multiuser settings. While highresolution mass spectrometers may provide unparalleled accuracy, the resolving power may not be necessary for routine or standardized analysis. Should a standardized method for the analysis of molecular weight distribution and profiling of bio-oil components be implemented by mass spectrometry, it would likely need to encompass analysis by multiple ionization techniques with appropriate parameters associated with ion transfer, trapping, ejection, etc. to be sure the bio-oil mixture is accurately represented.57

8. PHYSICAL SEPARATION OF BIO-OILS BASED ON MOLECULAR WEIGHT The ability to analyze a mixture of bio-oil components for accurate molecular weight distributions using chromatographic and mass spectrometry can be aided, as well as complicated, by the physical separation of the components prior to analysis. Preparative scale GPC has been implemented to separate biooils based on molecular weight, as described previously,29,30 and centrifugation can be used to aid the separation of bio-oils into layers of aqueous and organic light, middle, and heavy fractions containing varying concentrations of particulates.61 However, depending on the upgrading conditions, catalytic fast pyrolysis oils and hydrothermal liquefaction oils may more easily separate in to light, heavy, organic, and aqueous fractions upon production and collection. Significant efforts have been devoted toward liquid−liquid extraction (LLE) and fractionation methods as a means to separate the components of thermochemically derived bio-oils. Particularly, studies have focused on the separation of high molecular weight “pyrolytic lignin” from low molecular weight phenolics and other compounds using water, dichloromethane, and/or chloroform.3,7,8,14−16,24,53,62−67 In addition to separating various molecular weight fractions, LLE studies can also aid in the discovery of appropriate solvents and methodology used to analyze MWD of bio-oils, particularly for GPC analyses, although numerous types of analytical methods and results can be impacted by solvent selection.67 However, whether LLE methods induce condensations, agglomerations/aggregates, or changes in molecular weight distributions has not been sufficiently investigated and would also benefit from the use of a standardized and accurate MWD analysis. A standard method for LLE of bio-oil components based on molecular weight has not been established and may need to be optimized depending on the degree of upgrade of the oil. The traditional distillation of raw pyrolysis oils to separate fractions based on boiling points (and hence, molecular weight to some degree) is known to cause condensation reactions that leave behind up to 50% residue, rendering the process inefficient, and has been covered in several studies and reviews.5,68−70 However, molecular distillation techniques, which enable separation based on evaporation with condensation distances shorter than molecular mean free paths, have reportedly been used to successfully separate raw pyrolysis oils into light, middle, and heavy fractions without reports of coking or polymerization.71−74 For example, Wang

9. OUTLOOK AND CONCLUSIONS On the basis of a review of the literature, there is no consistent or standardized method, or consistent results obtained across different methods, for the determination of molecular weight metrics such as weight-average molecular weight (Mw) or molecular weight distribution (MWD) of thermochemically derived bio-oils. Most methods and instruments have various advantages and disadvantages, hence the widespread use of multiple techniques and instrumentation for the analysis of molecular weight metrics of bio-oils. A summary of select literature for each technique reviewed is provided in Table 2. GPC would be the most practical technique for the development of a standard method for the determination of molecular weight metrics of thermochemically derived bio-oils such as raw FP, CFP, hydrotreated, and HTL oils. GPC would incorporate the most economical, user-friendly, and robust instrumentation and methodology. However, high resolution separation and thorough characterization and speciation may not be possible or necessary using basic GPC techniques. GPC methods would need to be optimized from their current configurations to maintain a standard analysis that is accurate and applicable or translatable across a wide range of oil types, particularly of varying degrees of upgrade (i.e., raw FP, CFP, HTL oils). For example, the proper columns, solvents, standards, and detectors used for particular oil types need to be elucidated. Method development from a double or triple detection system (MALS detector coupled with RI detector and potentially viscometry detector) would be ideal and should be compared to or validated with a number of GPC standards, as well as analysis from another method such as FT-ICR or other MS. Previous research and literature comparisons M


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GC/MS, gas chromatography mass spectrometry; GPC, gel permeation chromatography; HPLC, high performance liquid chromatography; HTL, hydrothermal liquefaction; LDI, laser desorption ionization; LLE, liquid−liquid extraction; MALDI, matrix assisted laser desorption ionization; MAL(L)S, multiangle (laser) light scattering; MS, mass spectrometry; Mn, number-average molecular weight; Mw, weight-average molecular weight; Mp, peak molecular weight; MWD, molecular weight distribution; PD, polydispersity; RI(D), refractive index (detector); SEC, size exclusion chromatography; TAN, total acid number; TOF-MS, time-of-flight mass spectrometry; UV/ DAD, ultraviolet diode array detector

suggests some similarities between the molecular weight metrics obtained from FT-ICR MS and GPC of bio-oils, but no thorough and direct study has been conducted. While bio-oil analyses by FT-ICR MS techniques provide unparalleled characterization detail regarding the functionality, chemical composition, molecular formula, and identity of species present in the oils, a straightforward analysis for basic molecular weight distribution is lacking. The ability to accurately and quantitatively represent the full range of molecular weight components and functionalities, especially lower molecular weight/low boiling compounds, is not trivial and highly dependent on the type of oil (its degree of upgrade) coupled with the type of ionization technique used to introduce the oil to the spectrometer and the parameters used to transfer ions to and trap the ions in the ICR cell. FTICR has incredible resolving and characterization power, but given the complexity of pyrolysis oils, such high resolution for exact component speciation is not always practical, helpful, or necessary. When necessary, FT-ICR or other MS can be used to support molecular weight distribution data obtained from a more robust method, such as GPC. Overall, GPC methodology with multiple detectors, particularly MALS for higher molecular weight ranges, has been recommended to pursue the development of a standard protocol for the determination of accurate molecular weight metrics of thermochemically derived bio-oils.

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REFERENCES

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Anne E. Harman-Ware: 0000-0002-7927-9424 Jack R. Ferrell, III: 0000-0003-3041-8742 Author Contributions

The manuscript was written through equal contributions of all authors. All authors have given approval to the final version of the manuscript. Funding

This research was supported by the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office (BETO), under Award No. DE-AC36-08GO28308 with the National Renewable Energy Laboratory. Funding was also provided by the DOE Office of Science, Office of Biological and Environmental Research through the Center for Bioenergy Innovation (CBI), a DOE Bioenergy Research Center. The views expressed in the article do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors would like to thank Earl Christensen for his technical insight and editing of this manuscript. ABBREVIATIONS APCI, atmospheric pressure chemical ionization; APPI, atmospheric pressure photoionization; CFP, catalytic fast pyrolysis; DART, direct analysis real time; DBE, double bond equivalents; ESI, electrospray ionization; FT-ICR MS, Fourier-transform-ion cyclotron resonance mass spectrometry; N


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