Petroleomic Characterization of Pyrolysis Bio-oils: A Review - Energy

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Petroleomic Characterization of Pyrolysis Bio-oils: A Review Martin Staš, Josef Chudoba, David Kubicka, Jozef Blazek, and Milan Pospíšil Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b00826 • Publication Date (Web): 14 Sep 2017 Downloaded from http://pubs.acs.org on September 15, 2017

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Petroleomic Characterization of Pyrolysis Bio-oils: A Review

Martin Staš,1* Josef Chudoba,2 David Kubička,1 Josef Blažek,1 and Milan Pospíšil1

1

Department of Petroleum Technology and Alternative Fuels, University of Chemistry and

Technology Prague, Technická 5, 166 28 Prague 6, Czech Republic 2

Central Laboratories – Laboratory of Mass Spectrometry, University of Chemistry and

Technology Prague, Technická 5, 166 28 Prague 6, Czech Republic

ABSTRACT. Pyrolysis bio-oils could be used in the future as biofuels or as a source of valuable oxygen-containing chemicals. To facilitate efficient exploitation of bio-oils, a detailed understanding of their structure is necessary. Over the last decade, petroleomic analysis has been widely applied to characterize pyrolysis bio-oils from the lignocellulosic biomass. Typically, a petroleomic analysis has been performed using high-resolution mass spectrometry (HRMS). HRMS has enabled the researchers to determine the molecular weights and molecular formulas of thousands of less volatile and nonvolatile, high-molecular-weight bio-oil compounds to obtain structural information that cannot be obtained using any other method. Here, we discuss the theoretical principles of HRMS and present an overview of the investigations regarding the petroleomic characterization of pyrolysis bio-oils and their key findings. In addition, this review outlines the current knowledge of the structure of bio-oil compounds detectable by HRMS. This could help to understand the chemical composition of bio-oils in more detail and facilitate the design of processes for bio-oil upgrading and further utilization.

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1.

INTRODUCTION Pyrolysis bio-oils are liquids produced by the pyrolysis of a biomass. They represent a

renewable and environmentally friendly feedstock containing many valuable oxygen-containing compounds, see Table 1. In addition, the energy density of bio-oils per volumetric unit is about 2– 10 times higher in comparison with a raw biomass.1 Thus, bio-oils have the promising potential to be used as a source of valuable chemicals or as biofuels. On the other hand, bio-oils typically have high contents of water and oxygen.2 This causes some undesirable properties such as poor chemical and thermo-oxidative stability, phase instability, strongly acidic nature (corrosive properties) and immiscibility with conventional petroleum fuels that limit their more widespread use.2 Thus, an appropriate upgrading is required for the efficient use of bio-oils. To optimize any upgrading process, detailed knowledge of the chemical composition of bio-oils is necessary. Table 1:Typical composition of pyrolysis bio-oils 3 The chemical characterization of pyrolysis bio-oils is very difficult, as bio-oils may contain thousands of different biomass decomposition products with a wide distribution of molecular weights, boiling points, different polarity, solubility, etc. Lignocellulosic biomass contains three major building blocks – cellulose, hemicellulose (together designated as holocellulose) and lignin – and also some minor components including organic extractives (fats, waxes, resins, terpenes, etc.) and inorganic minerals. Cellulose is a linear polysaccharide that consists of about 5 000–10 000 of D-glucose units linked by (14)--glycosidic bonds, see Figure 1. It has a crystalline structure that is resistant to hydrolysis. Thermal decomposition of the cellulose leads to the formation of different compounds via two main competing pathways: depolymerization and ring scission. Depolymerization of cellulose mostly produces levoglucosan and other anhydrosugars (typically 1,4:3,6-dianhydro-α-D-glucopyranose, 2,3-anhydro-d2 ACS Paragon Plus Environment

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mannose, 3,4-altrosan, 1,6-anhydro--D-glucofuranose, etc.) and oligomeric saccharides. Ring scission mostly produces light volatiles (aldehydes, ketones, furans, etc.). Among them, hydroxyacetaldehyde,

methylglyoxal,

acetol,

glyceraldehyde,

furfural

and

5-

(hydroxymethyl)furfural are the most typical representatives. 4-5 Hemicellulose is a heterogeneous polysaccharide that consists of various polymerized monosaccharides (glucose, mannose, galactose, xylose, rhamnose) and their acidified forms (glucuronic and galacturonic acid). In comparison with cellulose, hemicellulose has a much lower degree of polymerization (150), an amorphous structure with little strength and no resistance to hydrolysis. Similarly to the pyrolysis of cellulose, small oxygenates (including alcohols, acids, ketones, furans, anhydrosugars, etc.) are mostly formed.6 Lignin is an amorphous, three-dimensional and highly branched polyphenolic substance that consists of an irregular array of methoxy- and hydroxysubstituted phenylpropane units. Thus, decomposition of lignin during the pyrolysis process leads to the formation of phenolic compounds, e.g., phenols, benzenediols, methoxy- and dimethoxyphenols, etc.3,6 Figure 1: (A) Chemical structure of cellulose, (B) Levoglucosan, (C) Main monomers of hemicellulose, (D) Lignin precursors Conventional GC, GC  GC, GPC, HPLC, FTIR and NMR methods have been used for the chemical characterization of bio-oils and the obtained structural information have been summarized in recent reviews.3,7-9 Overall, the structure of hundreds of volatile and semivolatile bio-oil compounds is well known, as these can be successfully analyzed using GC-MS or GC  GC-MS. On the other hand, there was only very limited information about the structure of nonvolatile, high-molecular-weight bio-oil compounds throughout the previous decades. At that time, these compounds were analyzed solely using bulk analytical methods, such as FTIR, NMR,


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etc.10-11 These methods brought valuable information on bulk properties of bio-oils, but not about individual compounds. In 2010, the petroleomic analysis of bio-oils using high-resolution mass spectrometry (HRMS) was reported for the first time and this brought the first insights in the structure of some heavier bio-oil compounds.12 In the recent years, HRMS became one of the most important analytical methods for the pyrolysis bio-oil characterization and enabled researchers to determine the molecular weights and elemental compositions of thousands of bio-oil compounds.1235

Among them, not only major oxygen-containing compounds were detected, but minor nitrogen-

, sulfur- and even boron-containing compounds were detected also.12-35 The majority of these compounds were not previously detected by any other analytical method. The aim of this review is to present a comprehensive overview of the studies regarding the HRMS characterization of pyrolysis bio-oils and their main outcomes. In addition, we also present the principles of HRMS and discuss its possible applications. This review may further improve the understanding of results of the HRMS characterization of bio-oils, which could help to obtain more detailed knowledge of the chemical composition of pyrolysis bio-oils and facilitate their further use. 2.

THEORETICAL

PRINCIPLES

OF

HRMS

CHARACTERIZATION

OF

PYROLYSIS BIO-OILS In the last decade, HRMS has been applied as the main analytical tool in petroleomics. Petroleomics studies the relationship between the chemical composition and physical and chemical properties of petroleum fuels or biofuels. It is based on the assumption that a sufficiently complete characterization of complex mixtures at the molecular level may help to correlate and predict the properties and behavior of such mixtures during their further processing.36-37


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In general, the HRMS characterization of complex mixtures is based on the ability of powerful high-resolution mass spectrometers to resolve and detect (tens of) thousands of compounds of a complex mixture in a single analysis. The compounds are detected based on their exact molecular masses, which allows for the determination of their elemental compositions, see Figure 2, as each elemental composition (e.g., CcHhNnOoSs) has its unique exact mass. The obtained elemental compositions (compounds) are subsequently categorized into different classes based upon the type and number of heteroatoms per molecule (i.e., NnOoSs); these compounds can further be described by their DBE value (double bond equivalents, see Eq. 1) and the numbers of carbon atoms per molecule in order to obtain a sample fingerprint. In the petroleum analytics, such knowledge has already been used to distinguish petroleum and its products according to their geochemical origin, the distillation cut, the extraction method, the catalytic processing, the study of molecular mass distribution, the identification of acidic and basic species, the characterization of heteroatom-containing compounds (NnOoSs) to study the efficiency of catalytic hydroprocessing, etc.36-38 DBE (CcHhNnOoSs) = c - (h/2) + (n/2) + 1

(1)

Typically, HRMS makes it possible to characterize less volatile and nonvolatile, highmolecular-weight compounds, most of which are not detectable by conventional GC methods. In addition, the molecular mass and elemental composition are not the only information that can be obtained by HRMS for each detected compound. The DBE value, calculated from an elemental composition, gives information about the number of rings and double bonds and may provide partial information about the structure of each of the detected compounds. Thus, HRMS has become one of the most important analytical methods for complex mixtures such as petroleum or biofuels. 5 ACS Paragon Plus Environment

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Figure 2: A broadband 12 T negative-ion ESI FT-ICR mass spectrum of birch wood (top) and a mass-scale expanded segment at m/z 309 of the same spectrum (bottom). Adapted from ref.23. © 2014 American Chemical Society. 2.1

High-resolution and Accurate Mass Measurement Petroleum, biofuels, or other complex mixtures typically contain tens of compounds per a

single nominal mass that differ in their exact masses. The high mass resolution and mass accuracy of powerful mass spectrometers are required to resolve such compounds and assign the correct molecular formulas to them. Mass resolution and mass accuracy are closely related to each other, as achievable mass accuracy strongly depends upon the level of peak resolution.39 2.1.1

Mass Resolution Mass resolution is defined as the ratio of the mass of interest (m) to the difference in mass

(m), see Eq. 2.39-40 Mass resolution influences the peak sharpness and refers to the ability to resolve (separate) the narrow mass spectral peaks, see Figure 3.39-40 Thus, mass spectrometers with a sufficiently high mass resolution produce sharp peaks that are well separated from each other. The ability of an instrument to separate peaks is called the mass resolving power.39 There are two different definitions of mass resolution/resolving power: the valley definition and peak width definition, see Figure 4. These two definitions differ in the way how m is measured. 𝑅 = 𝑚/∆𝑚

(2)

(R … mass resolution, m ... molecular mass, Δm ... see Figure 4) Figure 3: Illustration of low-resolution vs. high-resolution mass measurement


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In the valley definition, Δm is defined as the mass difference of two peaks of equal intensity such that the valley (lowest value of signal) between them is less than the specified fraction of the peak height, typically 10 %, see Figure 4.39-41 In the peak width definition, Δm is defined as the width of the measured peak at a specified peak height, i.e. 5 %, 10 %, 50 % etc. Typically, the peak width is measured at 50 %; such a Δm corresponds to the full width at the half maximum peak height and is usually designated as Δm50 and this particular definition is called the FWHM definition.39-41 Figure 4: Different definitions of mass resolution: the FWHM definition (left) and the 10 % valley definition (right). 

Mass Resolution Requirements in Petroleomics One of the challenges of the HRMS characterization of petroleum samples is the

resolution of two molecules whose elemental compositions differ by 12C3 versus 32SH4 (3.4 mDa) or 12C4 versus 13CSH3 (1.1 mDa), respectively.42 The latter doublet can be present when using an ionization technique that produces both radical and even-electron ions.42 Theoretically, these doublets can be present in pyrolysis bio-oils also, although the probability of their occurrence is lower in comparison with heavy petroleum fractions, as bio-oils typically have a very low content of sulfur. Table 2 presents the mass resolving power requirements for these and other selected doublets possible for bio-oils considering the fact that the doublets are composed of peaks of equal intensities (heights). It has to be taken into account that when the doublets would be composed of peaks of unequal height, even higher mass resolution would be needed.22 Recently, boroncontaining compounds were detected in bio-oils and this brought new requirements for the mass resolving power to analyze such samples.16 For instance, there is a 35 Da mass split corresponding to 12C7B1 vs. N2O4H3 that cannot be resolved even using the best FT-ICR mass spectrometers,16 see 7 ACS Paragon Plus Environment

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subchapter 2.1.3. This demonstrates the need for an ultra-high resolution to completely analyze bio-oils and complex mixtures in general and the need for further development of analytical instrumentation for these kind of analyses. Table 2: Selected mass splits and doublets in petroleomics* *A more comprehensive overview of the common mass splits and doublets in petroleomics is presented elsewhere38.

2.1.2

Mass Accuracy Mass accuracy is defined as the difference between the measured accurate mass and the

calculated exact mass. Thus, mass accuracy specifies the m/z measurement error, see Eq. 3.39 This parameter is very important to correctly assign the elemental compositions to the peaks in a spectrum, as the number of possible elemental compositions for a certain m/z increases exponentially with the increasing m/z and the number of chemical elements taken into account, see Figure 5.40,43 Mass accuracy usually increases with the increasing mass resolving power of mass spectrometers, because good mass accuracy can be obtained from sufficiently sharp and evenly shaped signals that are sufficiently separated from each other.39-40 Thus, high-resolution mass spectrometers enable the determination of the m/z with an accuracy of up to several decimal places, see Table 3.

𝑀𝑎𝑠𝑠 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 (𝑝𝑝𝑚) =

𝑚/𝑧 (𝑡𝑟𝑢𝑒) − 𝑚/𝑧 (𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑) . 106 𝑚/𝑧 (𝑡𝑟𝑢𝑒)

(3)

Figure 5: The number of possible elemental compositions in dependence on the obtainable mass accuracy for three different m/z values (200, 350 and 500) under the following constraints: C0-35H0-60N0-5O0-20 (A), C0-35H0-60N0-5O0-20S0-1 (B). Negative-ion APCI-MS analysis of a bio-oil from fast pyrolysis of wood.


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Table 3:

The obtainable absolute mass accuracy at different m/z values in dependence on the obtainable relative mass accuracy: 50 ppm (A), 10 ppm (B), 3 ppm (C), 1 ppm (D), 0.5 ppm (E)

2.1.3

High-resolution Mass Spectrometers for Bio-oil Characterization For bio-oil characterization, three different types of mass spectrometers have been

applied: Fourier transform ion cyclotron resonance (FT-ICR), orbitrap and time-of-flight (TOF).1235

The operation principles of these mass spectrometers are quite different from each other which

results in differences especially in the provided mass resolving power (and mass accuracy) and mass discrimination. Considering the mass resolving power, modern FT-ICR mass spectrometers are clearly the most powerful followed by orbitrap and TOF.13 For all of these mass spectrometers, mass resolving power is a function of m/z.42,44-45 Thus, when presenting the mass resolving power of these mass spectrometers, the value of the mass resolving power should always be accompanied by the value of m/z for which the resolving power is considered. Considering the mass discrimination, higher sensitivity for higher mass ions (m/z  131) vs. lower mass ions was observed for FT-ICR when applied for bio-oils, which is an opposite trend than observed for orbitrap or TOF.13 FT-ICR mass spectrometers (FT-ICRs) consist of an ICR cell, where ions orbit in a static magnetic field with the frequency fc that is inversely proportional to m/z, see Eq. 4.46 The ions create an image current that produces a time domain transient (sin waves of ions passing the detection plates – dependent upon the frequency of ion), which can be fast Fourier transformed into the frequency spectrum, which is then calibrated and converted into the mass spectrum.46 FT-ICRs are limited by very high purchase and operating costs, as they require cryogenic cooling using liquid helium and liquid nitrogen to maintain the superconducting magnet.46 Thus, they are 9 ACS Paragon Plus Environment

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available only in highly specialized laboratories, which limits their use for routine applications. On the other hand, the researchers can find access to such instruments – e.g., through programs like at the National High Magnetic Field Laboratory in Tallahassee, Florida. FT-ICRs, as the most powerful mass spectrometers, are currently the most commonly used for the petroleomic characterization of petroleum or biofuels.

𝑓𝑐 =

𝑧∙𝐵

(4)

2𝜋𝑚

(fc … cyclotron frequency, z … ion charge, B … magnetic field strength) An orbitrap is an ion trap mass analyzer that consists of one spindle-like inner electrode and two outer electrodes.42,45 Ions that are injected into the analyzer orbit in a static electric field around the inner electrode and oscillate along the z axis with angular frequency z proportional to (m/z)-1/2, see Eq. 5.46 These oscillations are detected using image current detection and are transformed into the mass spectra using fast Fourier transform.45 Overall, orbitraps are less powerful in terms of the provided mass resolving power and mass accuracy in comparison with FT-ICRs, but they are much more available in research laboratories than FT-ICRs are due to the lower purchase and operating costs.46

𝜔𝑧 = √

𝑘

(5)

𝑚/𝑧

(z… angular frequency, k … constant) A TOF mass analyzer consists of a simple chamber, in which ions are dispersed in time during their flight along a free path of a known length. The ions start their flight at the same time and the lighter ions arrive at the detector earlier than the heavier ones; i.e., the time of the flight of 10 ACS Paragon Plus Environment

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the ions depends upon their m/z, see Eq. 6. Novel TOF mass spectrometers are capable of achieving a mass resolving power up to 100 000 (at m/z 400), which is comparable to some older versions of the orbitrap (Orbitrap Velos).44 𝑡 = 𝑘 ∙ √𝑚/𝑧

(6)

(t… time, k … constant) Table 4:

Overview of the properties of selected high-resolution mass spectrometers (data obtained from ref.44,47-48 and specification sheets obtained from Bruker, Czech Republic and Thermo, Czech Republic) Overall, HRMS results may differ when different types of mass spectrometers are applied

for analysis; this is especially due to the differences in the mass discrimination and mass resolution of the different mass analyzers.13-14,46 Considering the possible mass discrimination of mass spectrometers, careful optimization of experimental conditions should be performed to minimize such mass discrimination for the mass range of interest.13 Overall, FT-ICRs are the most powerful mass spectrometers currently available, as they provide the highest mass resolving power and mass accuracy which is not obtainable by the other mass spectrometers, see Table 4, and they should always be the first choice for a comprehensive analysis of the most complex samples such as heavy petroleum fractions and biofuels. For bio-oils that are much less complex than heavy petroleum fractions, other high-resolution mass spectrometers (orbitrap, TOF) were used also, as these are typically more cost-effective (and more readily available) and robust in comparison with FT-ICRs and thus more convenient for routine applications.12,17-19,24 However, the limitations of these mass spectrometers have to be carefully taken into account. For instance, the results obtained using some older versions of orbitrap (Orbitrap Velos and Orbitrap Discovery) and TOF mass spectrometers 11 ACS Paragon Plus Environment

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indicate that these mass spectrometers can only be used to characterize major bio-oil compounds and they definitely cannot fully substitute more sophisticated mass spectrometers for the comprehensive characterization of bio-oils, as they are typically not able to detect many of the less abundant high-molecular-weight compounds that could be of interest for the development of the processes for bio-oil upgrading. 12,17-19,24 Conversely, the newer versions of orbitraps (e.g., Orbitrap Elite or Orbitrap Fusion Lumos) could be a good alternative to FT-ICRs especially for bio-oil characterization. For instance, the Orbitrap Elite was reported to be able to baseline resolve the 3.4 mDa and 1.1 mDa doublets, see subchapter 2.1.1, in petroleum samples up to m/z 640 and m/z 300, respectively.42 This suggests that this mass spectrometer could provide mass spectra of biooils of similar quality as FT-ICRs when an ionization technique solely producing radical ions or only even-electron ions is applied. In addition, this mass spectrometer should provide a satisfactory analysis of the overall composition of bio-oils even when using ionization techniques that produce even-electron and radical ions at the same time. This is a consequence of the very low content of sulfur in bio-oils due to which no significant occurrence of the 1.1 mDa doublets or doublets with lower mass difference is expected. 2.2

Elemental Composition Assignments from Accurate Mass Measurement The number of possible elemental compositions for a certain m/z increases exponentially

with an increasing mass and the number of chemical elements taken into account. Thus, especially for samples containing higher amounts of different heteroatoms, the assignments of each measured molecular mass to a unique elemental composition cannot always be performed in the whole mass range of interest based upon the mass measurement alone even using powerful mass spectrometers. To enhance the correctness of the molecular formula assignments in the whole mass range of


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interest, usually the Kendrick mass defect analysis has been applied for the HRMS characterization of petroleum fuels and biofuels.36-37 Kendrick49 proposed a Kendrick mass scale, in which the SI mass is converted into the Kendrick mass according to Eq. 7a. As the SI mass of CH2 is 14.01565 Da, the Kendrick mass of CH2 is 14.00000, see Eq. 7b. Kendrick also defined the Kendrick mass defect (KMD) as the difference of the Kendrick nominal mass and the Kendrick mass, see Eq. 7c. It can be deduced that all successive members of an alkylation series (i.e., with the same heteroatom class and DBE, but varying carbon number) will differ by 14.00000 in Kendrick mass and have thus the same KMD which allows for the sorting of such compounds into the Kendrick homologous (alkylation) series, see Figure 6. This is used in the KMD analysis, in which the measured values of m/z are converted into the Kendrick mass and the Kendrick homologous series are identified. For the lower mass members of such a series, the elemental compositions can be assigned with high confidence and the assignments can be then extended towards the higher mass members by extrapolation. Thus, the KMD analysis generally ensures much more reliable assignments of elemental compositions.3637

In addition, Kendrick mass plots enable one to readily identify outlier data (e.g., in-situ

contaminants, noise spikes), as they typically fall outside normal patterns.38 Kendrick mass = SI mass  (14.00000/14.01565)

(7a)

Kendrick mass of CH2 = 14.01565  (14.00000/14.01565) = 14.00000

(7b)

Kendrick mass defect = nominal Kendrick mass – Kendrick mass

(7c)

(the nominal Kendrick mass is the Kendrick mass rounded to the next integer)

2.3

Presentation of Petroleomic Data In the petroleomic analysis of the bio-oils or petroleum samples, typically thousands of

different elemental compositions can be resolved and identified.12-37 An appropriate graphical form 13 ACS Paragon Plus Environment

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is thus necessary to present this data. The presentation of petroleomic data was discussed in detail elsewhere36-38,50. Therefore, only a brief overview of the possibilities for the petroleomic data presentation is given here. Typically, Kendrick mass plots, van Krevelen diagrams, heteroatom class and DBE distributions and carbon number vs. DBE plots are used for the presentation of petroleomic data. In Kendrick mass plots, van Krevelen diagrams and carbon number vs. DBE plots, the detected elemental compositions are presented as dots, i.e., each dot presents a single elemental composition detected. Typically, color-coding is used to highlight the relative abundances of the compounds in the mass spectrum (not shown in Figures 6 and 8). All of these graphical forms allow for the presentation of thousands of the detected elemental compositions in a space-saving form and provide also some other possibilities that will be briefly discussed for each graphical form separately. Conversely, heteroatom class and DBE distributions are presented as bar charts; the relative abundance of compounds of a given heteroatom class is obtained as the summed abundances of all members of a given class divided by the summed abundances of all classes. In the same manner, the relative abundance of compounds with a given DBE are obtained. 2.3.1

Kendrick Mass Plots A Kendrick mass plot presents KMD vs. Kendrick nominal mass for each detected

elemental composition. The compounds of the same class (number of heteroatoms) and type (DBE) but different number of carbon atoms (CH2) form a single horizontal line, in which the members differ by 14 Kendrick mass units and have the same Kendrick mass defect, see Figure 6 and also subchapter 2.2. The neighboring vertical series differ by a DBE value of 1 (the same class and different type) and a KMD of two hydrogen atoms which is 0.013. The importance of Kendrick mass plots was discussed in the subchapter 2.2.38 14 ACS Paragon Plus Environment

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Figure 6: An illustrative Kendrick mass defect diagram of bio-oil from fast pyrolysis of wood, full (A) and “extracted”, O4 bio-oil compounds only (B). 2.3.2

Van Krevelen Diagrams The van Krevelen diagrams were introduced in 1950 to analyze the bulk elemental

abundance of coal samples; e.g., molar ratio of hydrogen to carbon (H/C) vs. oxygen to carbon (O/C).38 Based on the data distribution on these plots, samples of different origin can be distinguished.38 In petroleomics, the van Krevelen diagrams typically present the H/C ratio vs. O/C or N/C or S/C ratio for each detected elemental composition simultaneously.38 The O/C (or N/C or S/C) ratio distinguishes classes differing in the number of a given heteroatom (O or N or S) and such classes are thus separated horizontally. Alternatively, the H/C ratio distinguishes vertically compounds with differences in their DBE; as the H/C ratio increases, the DBE decreases. A homologous alkylation series composed of compounds with the same DBE and number of heteroatoms, but with a different number of CH2 groups will show up as diagonals. Thus, compounds with similar chemical properties tend to cluster in specific regions, see Table 5 and Figure 7.38 Van Krevelen diagrams can be used to estimate the major components observed in complex mass spectra.50 In addition, van Krevelen plots can be used to evaluate (i) the abundance of compounds of different classes, (ii) the correlations between the different compounds classes (methylation-demethylation, hydrogenation-dehydrogenation) and (iii) to compare the abundances of compounds containing different numbers of the same heteroatoms.8 Table 5:

Typical regions of bio-oil compounds in van Krevelen diagrams31,51


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Figure 7: Van Krevelen diagrams of oily and aqueous phases of slow pyrolysis bio-oil from pine wood with highlighted regions of the main bio-oil compounds. Reprinted with permission from ref.25. © 2015 American Chemical Society. 2.3.3

Heteroatom Class and DBE Distribution and Carbon Number vs. DBE Plots From an elemental composition, three main independent properties of a molecule can be

obtained: the heteroatom class (NnOoSs), the DBE and carbon number.36-37 These properties can be used to present the petroleomic data. The heteroatom class distribution groups compounds with the same heteroatom class but with a varying DBE and carbon number. It presents an overview of all heteroatom classes present and their relative abundances.38 Similarly, the DBE distribution groups compounds with the same DBE but with a varying heteroatom class and carbon number. It informs one about the degree of unsaturation of the detected compounds.38 Another important graphical presentation of the petroleomic data are DBE vs. carbon number plots, see Figure 8. The DBE vs. carbon number plots are typically presented for each detected heteroatom class separately (shown here for the O4–O6 classes only) and give information about the degree of unsaturation and alkylation of the representatives of a given heteroatom class.38 Figure 8: DBE vs. carbon number plots for the O4–O6 bio-oil compounds, negative-ion ESI-MS and negative-ion APCI-MS analysis of the bio-oil from fast pyrolysis of wood. Blue: ESI-MS only data, Green: ESI-MS + APCI-MS data, Red: APCI-MS only data. Adapted with permission from ref.17. © 2015 American Chemical Society. 2.4

Ionization Techniques for the HRMS Characterization of Pyrolysis Bio-oils In an HRMS characterization of complex samples, soft ionization techniques are typically

applied. Using soft ionization, only a little energy is imparted onto the subject molecule, which


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results in the formation of a radical molecular ion and/or an even-electron ion and no or only a little fragmentation occurs.52 The type of the formed ions is dependent on the ionization technique and the ionization mode applied. In the next paragraphs, there is a brief overview of soft ionization techniques typically applied for the HRMS characterization of bio-oils. In general, each ionization technique can be operated in a positive or negative ionization mode. The positive-ion mode is mostly suitable for basic compounds that can be readily protonated, whereas the negative-ion mode is mostly suitable for acidic compounds that can be easily deprotonated.33 Electrospray ionization (ESI) is the softest ionization technique suitable for medium polar to polar compounds of a wide molecular mass range, but not for some less polar or nonpolar compounds, see Figure 9.53 This ionization technique is suitable for thermally stable and also unstable species. Typically, it produces even-electron ions and odd-electron molecular ions are present in negligible amounts only.42 Negative-ion ESI is the most commonly applied ionization technique in the bio-oil analytics, as the majority of bio-oil compounds (holocellulose and lignin decomposition products) are polar and can be readily deprotonated to produce [M – H]– ions. Due to the presence of evenelectron ions only, the mass spectra of bio-oils obtained by negative-ion ESI are relatively less complicated in comparison with the mass spectra obtained by the other ionization techniques producing both even-electron and radical ions simultaneously. Besides oxygen-containing compounds, negative-ion ESI enabled the detection of some minor nitrogen-,15-16,18,20,22-23,30-33,35 sulfur-16,20,23,32,35 and even boron-containing16 compounds also. The bio-oil compounds detected by this ionization technique were typically more polar, less unsaturated and with lower carbon numbers and an m/z range than those obtained by other ionization techniques.17-18,27,35 A dependence of the obtained results on pH was reported.13 17 ACS Paragon Plus Environment

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Positive-ion ESI mostly produces even-electron protonated ions [M + H]+ and different adduct ions can be observed also due to the presence of trace amounts of impurities in the common solvents applied for the sample treatment, i.e., [M + Na]+, [M + K]+, [M + NH4]+; dimeric or multiply charged ions can be observed as well.52,54 The adduct formation can be used to detect acidic species and highlight species of interest. Positive-ion ESI is typically the technique of choice to study nitrogen-containing species in bio-oils, as this ionization technique predominantly ionizes basic nitrogen-containing species.32-33. When applied for bio-oils, the relative abundances observed for nitrogen-containing species were relatively high (sometimes even more than 80 %) especially considering the fact that bio-oils typically contain low amounts of nitrogen.18,20,32-33 Laser-desorption ionization (LDI) is applicable for nonvolatile, low to medium polar compounds that absorb the applied laser light, i.e., highly unsaturated compounds or compounds containing heteroatoms. Several limitations were reported when LDI was applied for bio-oil characterization: (i) some interference was observed due to the laser-induced aggregation, (ii) some volatile components were not detected due to their evaporation during the ionization process and (iii) cellulose pyrolysis products could not be detected due to the inability of nonaromatic pyrolysis products of cellulose to absorb the applied laser light.12 Negative-ion LDI was reported to be able to detect bio-oil compounds with a broader m/z range than negative-ion ESI, as higher m/z peaks are associated mostly with more unsaturated and oxygenated compounds that are typically less polar and thus more efficiently ionized by negative-ion LDI than by negative-ion ESI.27 Atmospheric pressure chemical ionization (APCI) enables the ionization of medium polar to polar compounds with molecular masses up to 1500 Da. For the analyzed compounds, thermal stability and some volatility is required.3 APCI may produce both even-electron and oddelectron radical ions and both lignin and holocellulose decomposition products can be analyzed.1718

In comparison with negative-ion ESI, negative-ion APCI was reported to be able to detect some 18 ACS Paragon Plus Environment

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more unsaturated, less polar bio-oil compounds with higher carbon numbers and m/z range not detectable by negative-ion ESI.17-18 Atmospheric pressure photo-ionization (APPI) has a similar application range as APCI, but is slightly softer and more successful in the ionization of the relatively less polar species.53 APPI is known to preferentially ionize aromatic compounds, for which both even-electron ions and radical ions may be observed.26,42 In the bio-oil analytics, positive-ion APPI allowed for the detection of mostly phenolic compounds which are mostly the decomposition products of lignin.19,26,33 The bio-oil compounds detectable by positive-ion APPI were typically heavier (m/z range shifted towards higher masses) and less polar than those detectable by negative-ion ESI and some hydrocarbons were detected also.26,35 Figure 9: Application range of the selected soft ionization techniques 17,53 Overall, HRMS results may differ when different ionization techniques are applied for analysis.17-18,26-27,32 Although different ionization techniques provide wide possibilities, the main drawback of an HRMS characterization of complex samples is the lack of a single versatile ionization technique. An ideal ionization technique should (i) be soft enough to avoid any fragmentation, (ii) ionize all of the compounds present regardless of their physical and chemical properties and (iii) eliminate the discrimination of some compounds in the ionization process, which decreases the ionization yields of these compounds.3,17 This results in several conclusions that should be considered in the HRMS study of bio-oils and other complex samples. First, even using soft ionization, a careful optimization of the process conditions has to be performed to avoid undesirable fragmentation. Second, all bio-oil compounds are not detectable by a single ionization technique, as bio-oils consist of thousands of compounds with different properties and the application range of the ionization techniques currently used depends upon the physical and 19 ACS Paragon Plus Environment

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chemical properties of the analyzed compounds.18,53 This means that not all bio-oil compounds may be detected when a single ionization technique is applied and an appropriate combination of ionization techniques is generally required when a comprehensive analysis is performed. Third, HRMS cannot be used for the quantitative analysis of bio-oils, as even structurally similar bio-oil compounds can significantly differ in their ionization yields as demonstrated in the study of Staš et al.18 However, it can be assumed that HRMS results can be used for a semi-quantitative comparison between similar samples (e.g., from the same pyrolysis process) when the analyses are performed under the same experimental conditions.18 

Optimization of Ionization Efficiency In the literature, there were reported some studies, in which the authors tried to increase

the efficiency of ionization using different solution phase modifiers (dopants) to favor cationization or anionization.14,16,20,23,32,55 In general, protonation can be favored using acidic dopants, whereas deprotonation is promoted using basic dopants.20 In the bio-oil analytics, formic acid18,20,32 and ammonium hydroxide14,16,23,32 have mostly been used as dopants; the first was reported in combination with positive-ion ESI to favor the formation of [M + H]+ ions, whereas the latter was reported in combination with negative-ion ESI to favor the formation of [M – H]– ions. Recently, Hertzog et al.20 studied the influence of sample preparation on the results of the HRMS characterization of pyrolysis bio-oils and demonstrated the great importance of wellcontrolled composition of the sample solution upon sensitivity and repeatability of the measurement and also a dramatic effect of the common ESI dopants upon the overall chemical description of bio-oils. In the negative-ion ESI, the analysis without dopant allowed for the detection of Ox compounds in the relative abundance of about 98 %. The addition of 1 % ammonia promoted the ionization of NOx compounds (24 %), whereas the addition of 1 % formic acid 20 ACS Paragon Plus Environment

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resulted in the promoted ionization of SOx compounds (34 %). In the positive-ion ESI, a poor measurement repeatability was observed when no dopant was used. The addition of 1 % formic acid, 1 % ammonium hydroxide and 1 % ammonium acetate resulted in the detection of mostly nitrogen-containing species, whereas the addition of 0.01 % sodium acetate led to a significant increase of both TIC and measurement repeatability and the detection of mostly O x compounds (98 %).

3.

PETROLEOMIC CHARACTERIZATION OF PYROLYSIS BIO-OILS The application of HRMS for pyrolysis bio-oils included a study of (i) the composition of

bio-oils of a different origin,12-35 (ii) bio-oil aging,26 (iii) bio-oil upgrading processes,27-29 (iv) selectivity and efficiency of bio-oil separation,30-31 etc. The results obtained from these analyses are summarized in Table 6. Table 6: 3.1

Overview of the results obtained by the HRMS studies of different pyrolysis bio-oils Study of the Composition of Bio-oils of a Different Origin HRMS allowed for the detection of thousands of less polar and nonpolar bio-oil

compounds originating from cellulose, hemicellulose, lignin and extractives and this brought the first insights in the structure of some heavier bio-oil compounds. Also, HRMS revealed the presence of some less polar anhydrosugars that were not detected by GC-MS previously.13 Overall, the obtained HRMS results confirm the widely accepted assumptions that (i) the chemical composition of pyrolysis bio-oils from a lignocellulosic biomass strongly depends upon the pyrolysis process conditions and pyrolysis feedstock (the type of biomass used for the pyrolysis) and (ii) bio-oils are much less complex in terms of the chemical composition than petroleum or heavy petroleum fractions (narrower m/z range, lower content of nitrogen, sulfur, metals, etc.). 21 ACS Paragon Plus Environment

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Typically, bio-oil compounds detected by HRMS had molecular masses in the range of 100–800 Da (peaks centered typically at about m/z 200–300), a DBE of 0–30 and contained up to 55 carbon atoms per molecule. HRMS measurements revealed that the majority of these compounds were oxygen-containing species with 1 up to 18 oxygen atoms per molecule (centered typically at about O3–O6). Typically, the lower oxygen heteroatom classes (O1–O6) mostly had lower DBE values of 1–6, whereas the higher oxygen classes (O7) mostly had higher DBE values of 6–12. An increase in the carbon number was typically accompanied by an increase in the DBE and oxygen content. In bio-oils, minor amounts of nitrogen-containing species and sulfur- and boron-containing compounds were detected also. Moreover, HRMS allowed for the identification of many fatty and resin acids. The majority of the bio-oil compounds come from the decomposition of holocellulose and lignin. Smith et al.13 used the DBE and the number of oxygen atoms per molecule to tentatively distinguish sugaric compounds (from the decomposition of holocellulose) and phenolic lignin compounds in the HRMS spectra. Compounds with a DBE  4 and O  5 were considered to be lignin decomposition products, while compounds with a DBE  4 and O  6 were considered to be sugaric compounds. However, such an approach has its limitations, as an overlap is possible, e.g., for O  6 compounds with a DBE of 4.13 Smith et al.26 used the DBE values to estimate the degree of polymerization for lignin decomposition products; the estimation was based on the fact that each lignin monomeric unit has a minimum DBE value of four (three double bonds and one ring) and an average DBE value of five (plus carbonyl or vinyl side chain) and the degree of polymerization is calculated by dividing the DBE values by five. HRMS was applied to study the chemical composition of single-phase bio-oils and as well as two-phase bio-oils containing oily (organic) and aqueous phases. Overall, aqueous phases


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typically contained compounds with a broader range of oxygen-atoms per molecule and lower DBE values than oily phases, which indicates that the solubility of bio-oil compounds in water increases with the number of oxygen atoms per molecule. Unsaturated long-chain fatty acids, resin acids and higher phenolic oligomers were mostly enriched in oily phases and saturated fatty acids, lower phenolic oligomers and holocellulose decomposition products (anhydrosugars and volatile acids) were mostly enriched in aqueous phases.14,22,24-25 

Nitrogen-containing Compounds in Bio-oils Nitrogen in biomass-derived pyrolysis bio-oils comes mostly from nitrogen-containing

amino acids and alkaloids that are present in plants.16 The detection of nitrogen-containing species in bio-oils using HRMS was reported in several publications.15-16,18,20,22-24,30-35 These studies revealed that nitrogen-containing species of bio-oil have typically a general formula of N1-3O1-16 and positive-ion ESI is the most suitable ionization technique to characterize such compounds. Tessarolo et al.15 detected a minor amount (1 %) of nitrogen-containing compounds with a general formula of N1O5-9 in an empty palm fruit bio-oil using negative-ion ESI and they assumed that these compounds were carbohydrates coupled to some nitrogen-containing secondary compounds of biomass. Jarvis et al.16 detected N1O5–11 compounds with the relative abundances 3–8 % in mixed conifer salvage bio-oils using negative-ion ESI. Hertzog et al.20 detected nitrogen-containing compounds in a miscanthus bio-oil using positive-ion and negative ion ESI. The authors highlighted a dramatic effect of sample preparation on the obtained results. Jarvis et al.22 detected nitrogen-containing compounds in a peanut hull bio-oil using negative-ion ESI. These compounds were enriched mostly in the aqueous phase of the bio-oil, but they were present in a lower amount in the oily phase also. Kekäläinen et al.23 detected N1O4-10 compounds in birch pyrolysis bio-oils obtained at 300 and 380 °C using negative-ion ESI. The relative abundances of the N1O4-5 23 ACS Paragon Plus Environment

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compounds increased as the pyrolysis temperature rose, whereas an opposite trend was observed for the other classes. Overall, the relative abundances of the nitrogen-containing species decreased with the increasing temperature from 5 % to 2 %.23 Santos et al.32 analyzed three bio-oils from freshwater plants using positive-ion and negative-ion ESI. The positive-ion ESI spectra were dominated by N1-3O0-3 compounds with the relative abundances in the range of about 80–85 % and the N2 class was the most abundant (24–31 %). Nitrogen-containing compounds (N1-3O2-3) were detected in the negative-ion ESI spectra as well, but their relative abundances were significantly lower (30–45 %).32 Cole et al.33 used orbitrap mass spectrometry to analyze nitrogen-containing species in fast pyrolysis bio-oils. The analyzed bio-oil samples originated from switchgrass collected at different harvest times throughout the year. In the study, the following ionization techniques were applied: positive-ion and negative-ion ESI and positive-ion APPI. The positive-ion ESI spectrum was dominated by nitrogen-containing compounds, whereas the other two spectra were dominated by oxygen-containing compounds. There were no detectable nitrogen compounds in the negativeion ESI, but the relative abundances of nitrogen-containing compounds were significantly higher using the other two ionization techniques: 95 % of TIC by ESI (+) and 24 % by APPI (+). As a result, almost 300 different nitrogen-containing species were detected and N2 was the most abundant heteroatom class followed by NO, N2O, NO2 and N1 (in the positive-ion ESI). Based on the obtained results, pyridine was assigned as a major structural motif of the N1 and NO classes and imidazole as a structural motif of the N2 class. Moreover, the authors observed that nitrogencontaining species (especially N2 compounds) dominated in the bio-oil spectra in early summer, but decreased significantly in later harvest times. This was caused according to the authors due to the decomposition of proteins as the senescence of perennial plants proceeds. The results indicate


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that late harvest switchgrass should be used for pyrolysis in order to reduce the nitrogen content of the final products.33 

Sulfur- and Boron-containing Compounds in Bio-oils The presence of sulfur- and boron-containing compounds in bio-oils from lignocellulosic

biomass is not unexpected, as both of these elements are present in plants. Sulfur is primarily assimilated via inorganic sulfate forms and then transported to stems and leaves of plants, where sulfates are reduced to sulfides that react with organic molecules to synthetize cysteine – amino acid distributed in most proteins.56 Also, sulfur is present in small quantities in softwood lignin, typically in the form of sulfonic acid.25 The fate of sulfur in biomass during its pyrolysis is discussed elsewhere.56 Boron is an essential element for normal growth of higher plants.57 Kekäläinen et al.23 detected some S1O3-4 compounds in minor amounts (1 %) in a birch wood bio-oil using negative-ion ESI. They assumed that these compounds were likely sulfonic acids and their derivatives. Santos et al.32 detected some N2S compounds in bio-oils from freshwater plants using positive-ion ESI. Miettinen et al.25 detected sulfur-containing compounds in the oily and aqueous phase of an unbarked pine slow pyrolysis bio-oil using negative-ion ESI. These compounds were enriched mostly in the oily phase. In another study, Miettinen et al.35 detected sulfur-containing compounds in a bio-oil from short-rotation willow using negative-ion ESI and positive-ion APPI. Jarvis et al.16 analyzed the chemical composition of oak, mixed conifer, scotch broom, and mixed conifer (fire and beetle kill salvage) bio-oils. They, as the first, detected boron-containing compounds (B1O4-16) in bio-oils. Especially, in the oak and mixed conifer fire salvage and mixed conifer beetle kill salvage bio-oils, B1Ox compounds were observed in relatively higher abundances


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in the HRMS spectra (15 %, 20 % and 30 %, respectively). B1O4-5 compounds were the most abundant boron-containing compounds in the oak bio-oil, whereas B1O5-9 and B1O6-8 compounds were the most abundant boron-containing compounds in the mixed conifer fire salvage and mixed conifer beetle kill salvage bio-oils. According to the authors, boron was likely bonded in boronpolysaccharides in the form of a tetravalent boratediol complex.16 

Fatty and Resin Acids in Bio-oils In several studies, different resin acids and saturated and unsaturated long-chain fatty

acids were detected and identified.14-16,23,25,31-32,35 These acids are listed in Table 7. Table 7:

A list of selected fatty and resin acids detected in pyrolysis bio-oils using HRMS Tessarolo et al.15 identified five O2 fatty acids in a palm-fruit bio-oil and palmitic and

stearic acids were the most abundant among them. Jarvis et al.16 revealed that the O2 class in oak, mixed conifer and scotch broom bio-oils was dominated by C12–C30 saturated fatty acids with a DBE of 1. They assumed that the most abundant O2 compounds in the oak and scotch broom biooils corresponded likely to palmitic and stearic acids, whereas the most abundant compounds of the O2 class in the mixed conifer bio-oil corresponded likely to resin acids with C20 and a DBE 6– 7 and/or their isomers. The mixed conifer bio-oil contained also diacids of a low DBE and carbon numbers 30 in high relative abundances.16 Kekäläinen et al.23 analyzed birch wood pyrolysis biooils obtained at 300 and 380 °C using negative-ion ESI. The most abundant representatives of the O2–O4 classes in both oils had DBE values of 2–4 and corresponded likely to lipid-derived fatty acids (O2 class), hydroxy- and epoxy-fatty acids (O3 class) and diacids and/or dihydroxy fatty acids (O4 class and a low-DBE region of the O5 class). Interestingly, the Oil-380 consisted almost exclusively of saturated long-chain fatty acids (mostly stearic acid, 18:0), whereas the Oil-300 26 ACS Paragon Plus Environment

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contained different saturated as well as unsaturated acids (i.e. 16:0, 18:1, 18:2, 18:3, 20:0, 22:0, etc.) within the O2 and O3 classes. This indicated the tendency for the hydrogenation of unsaturated acids to fully saturated acids when the pyrolysis temperature raised from 300 to 380 °C.23 Miettinen et al.25 detected and identified different resin acids and long-chain saturated and unsaturated fatty acids in the oily and aqueous phases of a slow pyrolysis bio-oil from unbarked pine. Overall, resin acids and O2 unsaturated fatty acids were mostly enriched in the oily phase, whereas saturated acids were dominant in the aqueous phase. In the oily phase, the O3 class was comprised of various saturated and unsaturated C18−C24 hydroxy/epoxy fatty acids and the O4 class was comprised of various diacids and/or hydroxyl fatty acids with a minimum DBE of 2.25 In another study, Miettinen et al.35 detected some O2 saturated and unsaturated fatty acids with 14– 28 carbon atoms and a DBE of 1–4; the O3 class was represented by C14-26 hydroxy fatty acids and the O4 class by C16-22 epoxy fatty acids and/or diacids (DBE 2). 3.2

Study of Bio-oil Aging Bio-oil aging is a significant problem especially during a prolonged storage when higher

molecular-weight compounds are formed due to the high reactivity of bio-oils. This increases the overall viscosity of bio-oils which is highly undesirable especially for fuel applications.58 Hence, a better understanding of the changes at the molecular level is needed to stabilize bio-oils and slow down their aging. Smith et al.26 applied HRMS to study the associated molecular changes during bio-oil aging. A red-oak fast pyrolysis bio-oil was subjected to an accelerated aging procedure by heating to 90 °C for 0, 8, 16 and 24 hours to obtain four samples (T0, T1, T2 and T3) that were analyzed using positive-ion APPI and negative-ion ESI FT-ICR mass spectrometry. In both the positive-ion APPI and negative-ion ESI spectra, the ion abundances of the aged samples (T1–T3) decreased in 27 ACS Paragon Plus Environment

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low oxygen compounds (O2–O6) and increased in high oxygen compounds (O8). Similarly, the positive-ion APPI data suggested the decrease of smaller lignin oligomers (dimers and trimers) and the increase of higher oligomers (tetramers and higher). This was supported also by the observed m/z distributions, where the signals of the aged samples were shifted towards higher m/z values in comparison with the non-aged sample. This indicates the presence of higher-molecular weight compounds (with the higher oxygen content) in the aged samples. Based on the comparison of the DBE distributions of the aged and non-aged samples, the authors concluded that bio-oil aging resulted from the oligomeration of phenolic lignin compounds and cellulose and hemicellulose compounds had a negligible effect on the aging.26 3.3

Study of Bio-oil Upgrading Processes Despite a great potential, further use of bio-oils is still limited by their negative properties

that arise especially from a high oxygen and water content. Thus, upgrading of bio-oil properties is essential especially for fuel applications. Hydrotreatment or hydrodeoxygenation (HDO) has been widely applied to reduce acidity and oxygen content of bio-oils. In HDO, bio-oils are treated with hydrogen in the presence of a catalyst and oxygen is removed as water. An in-depth knowledge of bio-oil composition is needed to design the HDO process conditions to obtain product of a required quality. Olcese et al.27 studied the effect of iron catalysts on the catalytic hydrotreatment of a lignin bio-oil. The study was based on the comparison of the chemical composition of the samples prior and after the upgrading. The relative abundances of the O2 and O3 were higher in the hydrotreated bio-oils in comparison with the non-hydrotreated bio-oil. Conversely, the relative abundances of the O4-6 classes decreased after the hydrotreatment and the O7-8 classes were no more detected in the hydrotreated products.27 28 ACS Paragon Plus Environment

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Bi et al.28 studied compositional changes of pyrolysis bio-oil from forestry residue during hydrodexygenation using different analytical methods including negative-ion ESI FT-ICR mass spectrometry. The bio-oil was hydrotreated at four different reaction temperatures (150, 210, 300 and 360 °C, respectively) and four products were obtained. The petroleomic study revealed that the raw (non-hydrotreated) oil contained O2–O18 species with the DBE distribution of 0–22, whereas the hydrotreated oils contained compounds with a DBE and number of oxygen atoms per molecule shifted towards lower values and an increase in the H/C ratio and a decrease in the O/C ratio were observed. During the HDO, carbohydrates with molecular compositions of (CH2O)n were dehydrated to final products of carbon (H/C = 0) and water. Lignins generated phenols by demetoxylation and then produced benzene (H/C = 1) and propylbenzene (H/C = 1.31). Unsaturated hydrocarbons containing carbonyl groups were saturated by hydrogenation, followed by decarboxylation and decarbonylation to produce aliphatic hydrocarbons (H/C = 2). It was observed that HDO at 150 °C promoted the breakdown of ether linkage and HDO at 210 and 300 °C facilitated dehydratation and hydrodeoxygenation (or dehydratation-hydrogenation) reactions and generated carbonyl-aromatic structures. The obtained results indicate that decarboxylation and decarbonylation need higher temperatures than 360 °C.28 Tessarolo et al.29 analyzed bio-oils from pine wood and sugarcane bagasse obtained by thermal and catalytic pyrolysis using negative-ion ESI FT-ICR mass spectrometry. They observed that the use of the ZSM-5 catalyst in the catalytic pyrolysis resulted in the selective decomposition of the lignin-derived compounds; decrease in the abundances of higher classes (O5) and a subsequent increase in the abundances of lower O2–O4 classes was observed.29


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Study of Selectivity and Efficiency of Bio-oil Separation Liu et al.30 analyzed a red pine fast pyrolysis bio-oil and its n-hexane soluble (HS), ether

soluble (ES), ether insoluble (EIS), dichloromethane soluble (DS) and methanol soluble (MeS) fractions using negative-ion ESI FT-ICR mass spectrometry. In the bio-oil, O2–O17 and N1O3–14 compounds with a DBE of 1–22 were detected. The HS fraction had the narrowest molecular mass distribution and consisted mostly of acids, alcohols and lignin monomers. The ES fraction consisted of volatile, low-molecular-weight bio-oil compounds. The EIS fraction consisted mostly of carbohydrates and the DS fraction consisted likely of lignin monomers and dimers. The MeS fraction had the widest molecular mass, heteroatom class and DBE distributions and consisted mostly of lignin dimers, trimers and tetramers.30 Cheng et al.31 performed a three-step supercritical CO2 extraction for the selective fractionation of a red pine fast pyrolysis bio-oil. The bio-oil and obtained fractions were analyzed using negative-ion ESI FT-ICR mass spectrometry. Between the fractions, a significant compositional difference was observed. With the increasing content of polar co-solvent, extraction pressure and time (fraction 1  fraction 3), the distribution of Ox and N1Ox compounds shifted from O2–O13 and N1O3–N1O12 to O4–O18 and N1O4–N1O16, respectively. HRMS enabled to determine the origin of compounds present in the obtained fractions: lipids and holocellulose compounds were enriched in fractions 1 and 2 and lignin compounds in fraction 3.31 4.

CONCLUSION Overall, HRMS allowed for the characterization of less volatile and nonvolatile, high-

molecular-weight bio-oil compounds most of which are not detectable by GC methods that are typically applied for bio-oil characterization. The application of HRMS for bio-oil characterization included a study of (i) the composition of bio-oils of different origin, (ii) bio-oil aging, (iii) bio-oil 30 ACS Paragon Plus Environment

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Energy & Fuels

upgrading processes, (iv) selectivity and efficiency of bio-oil separation, etc. More details about the structure of the major oxygen-containing compounds as well as minor fatty and resin acids, nitrogen-, sulfur- and boron-containing compounds were obtained. Although HRMS is a powerful tool to analyze complex mixtures providing molecular-level structural information that cannot be obtained using any other current analytical method, it has also some limitations, i.e., the inability to analyze volatile compounds and differentiate structural isomers and the inability to provide quantitative information and the exact structure of the detected compounds. A combination of HRMS with other analytical methods, i.e., ion-mobility mass spectrometry, tandem mass spectrometry, GC  GC, or some other separation techniques, etc., could be applied to achieve a more in-depth understanding of the bio-oil composition. Emphasis should be given to determine the exact structure of the abundant and also less abundant high-molecular-weight compounds which is crucial for the development of bio-oil upgrading processes as well as to obtain at least some quantitative information using the petroleomic approach. In terms of the development of biooil upgrading processes, special attention should also be given to nitrogen-, sulfur- and boroncontaining compounds. These compounds are typically present in bio-oils in minor amounts, but they are of great importance, as they can act as catalytic poisons. 

ABBREVIATIONS

APCI

atmospheric pressure chemical ionization

APPI

atmospheric pressure photo-ionization

Da

Dalton

DBE

double bond equivalents (the number of rings and double bonds, the degree of unsaturation)

ESI

electrospray ionization 31 ACS Paragon Plus Environment

Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

FT-ICR

Fourier transform ion cyclotron resonance

FTIR

Fourier transform infrared spectroscopy

FWHM

full width at half maximum

GC

gas chromatography

Page 32 of 46

GC  GC comprehensive two-dimensional gas chromatography GPC

gel permeation chromatography

HDO

hydrodeoxygenation

HPLC

high-performance liquid chromatography

HRMS

high-resolution mass spectrometry

LDI

laser desorption ionization

MS

mass spectrometry

NMR

nuclear magnetic resonance

TIC

total ion current

TOF

time-of-flight mass analyzer



AUTHOR INFORMATION

Corresponding author Telephone: +420220444238. Fax: +420220444321. E-mail: [email protected] 

ACKNOWLEDGEMENT

This research was funded from the institutional support for the long-term conceptual development of the research organization (CZ60461373) provided by the Ministry of Education, Youth and Sports, the Czech Republic and the “National Program of Sustainability” (NPU I LO1613, MSMT43760/2015). 32 ACS Paragon Plus Environment

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5.

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Table 1: Typical composition of pyrolysis bio-oils 3 Compound class

Content (wt %)

Aldehydes

10–20

Alcohols

2–5

Carboxylic acids

4–15

Furans

1–4

Ketones

1–5

Phenolic monomers

2–5

Phenolic oligomers

15–30

Sugars

20–35

Water

20–30

Table 2: Selected mass splits and doublets in petroleomics m [mDa]

Doublets

Required mass resolving power (FWHM) m/z 250 m/z 400 m/z 800

H3NS

vs

C313C

4.7

54 000

86 000

171 000

CH

vs

13

C

4.5

56 000

89 000

178 000

C2N

vs

H434S

3.9

65 000

103 000

206 000

H4S

vs

C3

3.4

74 000

118 000

236 000

O3S1

vs

O8

2.5

100 000

160 000

320 000

C2

vs

HNa

2.4

105 000

167 000

334 000

N13C

vs

O11B

2.2

114 000

182 000

364 000

C5

11

vs

HO3 B

1.9

132 000

211 000

422 000

CN C

vs

H3O3

1.8

139 000

223 000

445 000

C4

vs

13

CH3S

1.1

228 000

364 000

728 000

C711B

vs

N2O4H3

0.035

7 143 000

11 429 000

22 858 000

13


Page 39 of 46

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Table 3:

The obtainable absolute mass accuracy at different m/z values in dependence on the obtainable relative mass accuracy: 50 ppm (A), 10 ppm (B), 3 ppm (C), 1 ppm (D), 0.5 ppm (E)

m/z 100 250 500 800

A

B

C

D

E

m [mDa] 5.0 12.5 25.0 40.0

m [mDa] 1.0 2.5 5.0 8.0

m [mDa] 0.30 0.75 1.50 2.40

m [mDa] 0.10 0.25 0.50 0.80

m [mDa] 0.050 0.125 0.250 0.400

Table 4:

Overview of the properties of selected high-resolution mass spectrometers (data obtained from ref.44,47-48 and specification sheets obtained from Bruker, Czech Republic and Thermo, Czech Republic) Maximum mass resolving power (FWHM) at m/z 400

Mass accuracy using internal calibration (ppm)

Mass range of data acquisition

TOF (HRT)

100 000

1–3

100 000

Orbitrap Velos

100 000

1–3

50–2 000; 200–4 000

1

50–2 000; 200–4 000

1

50–2 000; 200–4 000

0.6

100–10 000

0.5

100–10 000

0.3

100–10 000

0.25

100–10 000

Mass spectrometer

240 000 Orbitrap Elite 480 000 * 500 000 (m/z 200) Orbitrap Fusion Lumos 1 000 000 * 295 000 (1 s transient) 7 T FT-ICR

10 000 000 390 000 (1 s transient)

9.4 T FT-ICR

10 000 000 500 000 (1 s transient)

12 T FT-ICR

10 000 000 600 000 (1 s transient)

15 T FT-ICR

10 000 000

*using a developer´s kit


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Table 5:

Page 40 of 46

Typical regions of bio-oil compounds in Van Krevelen diagrams31,51

Compounds

O/C

H/C

Lignin compounds

0.2–0.8

0.6–1.4

Condensed aromatics

0.0–0.6

0.3–0.8

Hollocellulose

0.7–1.1

1.0–1.8

Lipids

0.0–0.3

1.2–2.0

Table 6, see separate file. Table 7: A list of selected fatty and resin acids detected in pyrolysis bio-oils using HRMS Heteroatom and DBE class

Formula

Lipid numbers

Nominal mass (Da)

O2 DBE 1

C12H24O2

12:0

O2 DBE 1

C14H28O2

O2 DBE 1

Assignment Reference Compound class

Compound name

200

saturated fatty acids

lauric acid

14:0

228


myristic acid

15-16,35

C16H32O2

16:0

256


palmitic acid

14-16,23,32,35

O2 DBE 1

C18H36O2

18:0

284


stearic acid

O2 DBE 1

C20H40O2

20:0

312


arachidic acid

16,23,32,35

O2 DBE 1

C22H44O2

22:0

340


behenic acid

16,23,25,32,35

O2 DBE 1

C24H48O2

24:0

368


lignoceric acid

16,23,25,32,35

O2 DBE 1

C26H52O2

26:0

396


cerotic acid

16,35

O2 DBE 1

C28H56O2

28:0

424


montanic acid

16,35

O2 DBE 1

C30H60O2

30:0

448


melissic acid

16

O2 DBE 2

C18H34O2

18:1

282

unsaturated fatty acids

oleic acid

O2 DBE 2

C20H38O2

20:1

310


gondoic acid

23

O2 DBE 3

C18H32O2

18:2

280


linoleic acid

23,25,32,35

O2 DBE 4

C18H30O2

18:3

278


linolenic acid

23,25,32,35

O2 DBE 5

C18H28O2

18:4

276


stearidonic acid

O2 DBE 6

C20H30O2

–

302

resin acids

abietic acid

16,25

O2 DBE 7

C20H28O2

–

300

resin acids

dehydroabietic acid

16,25


15-16

15-16,23,25,32,35

23,25,32,35

35

Page 41 of 46

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Figure 1: (A) Chemical structure of cellulose, (B) Levoglucosan, (C) Main monomers of hemicellulose, (D) Lignin precursors


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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Figure 2: A broadband 12 T negative-ion ESI FT-ICR mass spectrum of birch wood (top) and a mass-scale expanded segment at m/z 309 of the same spectrum (bottom). Adapted from ref.23. © 2014 American Chemical Society.


Page 43 of 46

high-resolution low-resolution

Relative abundance

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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m/z

Figure 3: Illustration of low-resolution vs. high-resolution mass measurement

Figure 4: Different definitions of mass resolution: FWHM definition (left) and 10 % valley definition (right).


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30

Number of possible elemental compositions

m/z 199.0611 (A) m/z 349.1081 (A) 25

m/z 499.1761 (A) m/z 499.1761 (B)

20

15

10

5

0 1

2

5

10

15

Mass accuracy (ppm)

Figure 5: The number of possible elemental compositions in dependence on the obtainable mass accuracy for three different m/z values (200, 350 and 500) under the following constraints: C0-35H0-60N0-5O0-20 (A) and C0-35H0-60N0-5O0-20S0-1 (B). Negative-ion APCI orbitrap-MS analysis of a bio-oil from fast pyrolysis of wood.

A

0.45

B

0.30

0.40

DBE 14

0.35

DBE 13

0.25

Kendrick mass defect

Kendrick mass defect

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 44 of 46

0.30 0.25 0.20 0.15

DBE 12 DBE 11

DBE 10 C4

DBE 9

0.20

DBE 8 C3

DBE 7 DBE 6

C17

DBE 5

0.15

C18

DBE 4 0.10

DBE 3 DBE 2

0.05

0.10 100

200

300

400

500

600

100

Kendrick nominal mass

150

200

250

300

350

400

Kendrick nominal mass

Figure 6: An illustrative Kendrick mass defect diagram of bio-oil from fast pyrolysis of wood, full (A) and “extracted”, O4 bio-oil compounds only (B).


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Figure 7: Van Krevelen diagrams of oily and aqueous phases of slow pyrolysis bio-oil from pine wood with highlighted regions of the main bio-oil compounds. Reprinted with permission from ref.25. © 2015 American Chemical Society.


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O4

O5

25

20

20

20

15

15

15

DBE

10

10

5

5

0 5

10

15

20

25

30

35

0 0

Carbon number

10 5

0 0

O6

25

DBE

DBE

25

5

10

15

20

25

30

35

0

5

Carbon number

10

15

20

25

30

35

Carbon number

Figure 8: DBE vs. carbon number plots for O4–O6 bio-oil compounds, the negative-ion ESI-MS and negative-ion APCI-MS analysis of the bio-oil from fast pyrolysis of wood. Blue: ESI-MS data only, Green: ESI-MS + APCI-MS data, Red: APCI-MS data only. Adapted with permission from ref.17. © 2015 American Chemical Society.

100 000

Molecular weight

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 46 of 46

10 000 1 000

ESI

APPI

APCI

100

Nonpolar

Polarity

Ionic

Figure 9: Application range of the selected soft ionization techniques 17,53


Petroleomic Characterization of Pyrolysis Bio-oils: A Review - Energy

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