Contribution of Fourier transform mass spectrometry to bio-oil study

CHAPTER 22

Contribution of Fourier transform mass spectrometry to bio-oil study
a, Fre
de
ric Aubrieta Jasmine Hertzoga,b,c, Vincent Carre a

Laboratory of Chemistry and Physics – Multi-Scale Approach of Complex Systems, FR 2843 Jean Barriol Institut, FR 3624 French High Field FT-ICR Mass Spectrometry Network, Lorraine University, ICPM, Metz, France b Comprehensive Foodomics Platform, Analytical Food Chemistry, Technical University Munich, Freising, Germany c Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum M€ unchen, Neuherberg, Germany

Chapter outline Introduction Properties and upgrading of bio-oils Physicochemical properties Upgrading treatments Analytical methods for bio-oil characterization Introduction Sample preparation and targeted analytical methods Petroleomic non-targeted approach Introduction Analyses of bio-oils by ESI-FTMS Analyses of bio-oils by APPI, APCI, and LDI-FTMS—additional insights to ESI-FTMS analyses Concluding remarks on the characterization of bio-oil by FT-MS Acknowledgments References

679 682 682 685 686 686 686 688 688 689 708 719 721 721

Introduction The world’s dependence on fossil energies is a critical issue dealing with economy, environment, and geopolitics. Both the global population and the energy demand increase, whereas the total resources decrease. One key is the development of sustainable and greener sources of energy and chemicals. Among the renewable energy sources (solar, wind, ocean, …), the use of the lignocellulosic biomass is a promising way. The biomass includes cellulose, hemicellulose (complex carbohydrates), and lignin (Fig. 22.1) [1, 2]. Fundamentals and Applications of Fourier Transform Mass Spectrometry https://doi.org/10.1016/B978-0-12-814013-0.00022-3

© 2019 Elsevier Inc. All rights reserved.

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680 Fundamentals and Applications of Fourier Transform Mass Spectrometry

Fig. 22.1 General overview of the biomass composition with the main molecular units in regards to the biomass components.

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681

The biomass may be used to produce chemical derivatives, by means of different processes, which can be used as a bio-fuel [3–6] or which may be refined in classical or specific devices to yield interesting components for the chemical industry [7–9]. Depending on the raw material, as well as the used transformation process, different biofuels have to be considered. The first generation biofuels are produced from transesterification of vegetable oils (sunflower, rapeseed, oil palm) to yield biodiesel [3, 10–17] or from fermentation of sugar originating from sugar cane, sugar beet, wheat or corn to yield bioethanol [18–26]. Nevertheless, the production of both biofuels affects the foodstuff cultures for human and livestock farming [27, 28]. The third generation bio-oils are from algae or bacteria and are still on laboratory stage and required significant process developments before their use on an industrial scale [29]. At present, second generation bio-oils are very promising [1, 19, 27, 30], as they are produced from non-food feedstock such as bark, grass, agricultural wastes or wood [31–38]. The conversion of the lignocellulosic biomass into liquid biofuels is mainly performed through two different approaches. The first one is biochemical processing that is based on catalytic hydrolysis and fermentation of specific biomass components such as polysaccharides [39–41]. In the second process (thermochemical route), the biomass is heated and is converted into a large range of products. Different thermochemical pathways can be distinguished depending on the heating temperature, the pressure, the residence time in the reactor, the nature of the medium, and the oxygen concentration. Gasification [42–44], pyrolysis [45–47], and liquefaction [48–50] are the most used conversion processes. They present the advantage to convert all the organic biomass components in a single step. Thus, different types of biofuels can be obtained from the conversion of non-food feedstock. Some are refined products such as bioethanol, others are unrefined and are mainly represented by the production of bio-oils from the pyrolysis and the liquefaction of the biomass, also called Biomass-to-Liquids (BTL) processes [51]. The production of such bio-oils by these BTL processes presents some strong assets. In contrast to the biochemical process, it does not require extraction pretreatment, which reduces the cost of the bio-oil production. It is established that fast pyrolysis route is the cheapest conversion route [52]. Nevertheless, additional conversion steps are required to enable the bio-oil to be used as biofuels or to produce valuable chemicals. To assist in optimizing of such processes, analytical chemistry is a key point. In regards to the complexity of bio-oils, high-resolution mass spectrometry is able to provide numerous data on the elemental composition of bio-oils.

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Fundamentals and Applications of Fourier Transform Mass Spectrometry

The purpose of this chapter is to review different studies performed on the bio-oils obtained from BTL processes by non-targeted analysis using Fourier transform mass spectrometry (FTMS) such as Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap. Such exhaustive bio-oil description is essential for, at least, two purposes. First, it will allow defining the most suited refining treatment in respect with the considered bio-oil. Secondly, it will ensure to define the more adapted upgrading procedure such as deoxygenation and denitrogenation. Consequently, we also present how different ionization sources can ensure a better understanding of this material and how FTMS fingerprint of bio-oils, obtained before and after an upgrading treatment, can attest to its efficiency. We will see how this approach is critical in the development of green petroleum and how it will face the next challenges.

Properties and upgrading of bio-oils Physicochemical properties Depending on the used BTL process, the physicochemical properties of the bio-oil may significantly differ from petroleum oil (Table 22.1) [53, 54]. Pyrolysis bio-oil In pyrolysis process, the biomass is converted by thermal treatment in a nonoxidizing atmosphere, at a pressure ranging from 1 to 5 atm. Depending on the operating parameters, different amounts of bio-oil, biochar (or charcoal), and gas (particularly syngas) are generated. The biomass pyrolysis generates vapor containing thousands of compounds. After condensation by cooling, the so-called bio-oil is obtained. Three kinds of pyrolysis methods have to be distinguished (Table 22.2) [61–63]. Most of the studied bio-oils are obtained by a 500 °C fast pyrolysis. These conditions are optimum to obtain the maximum of bio-oil amount [64]. The bio-oil composition, in terms of compound classes, is given in the Table 22.3. The major part of the bio-oil components presents at least one oxygenated chemical function [65, 66]. These oxygenated organic compounds extend on a wide range of molecular weight and abundance. The large amount of carboxylic acids leads to a low pH, which is responsible for corrosiveness and, consequently, storage issues. The acidity of the bio-oil may be evaluated by the total acid number (TAN). It is determined by acid-base titration and expressed as mg KOH/g of sample. It was estimated by Oasmaa et al. at 10.2 mg of KOH/g [67].

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Table 22.1 Typical physicochemical properties of pyrolysis, liquefaction, and heavy fuel oils Properties

Pyrolysis oil

Liquefaction oil

Heavy fuel oil

Moisture, wt.% pH Elemental composition, wt.% C H O N S Ash Higher heating value (HHV), MJ.kg
1 Viscosity, cP Solids, wt.% Distillation residue, wt.%

15–30 2.5

5.1 –

0.1 –

54–58 5.5–7.0 35–40 0–0.2 0.00–0.02 0–0.2 16–19

72.6 8.0 16.3 – 0.00–0.03 – 34

85 11 1.0 0.3 1.0–1.8 0.1 40

40–100 (at 50 °C) 0.2–1 Up to 50

15,000 (at 61 °C) – –

180 1 1

Data obtained from D.C. Elliott, G.F. Schiefelbein, Liquid hydrocarbon fuels from biomass, in: Am. Chem. Soc. Div. Fuel Chem. Annu. Meet. Prepr., 1989, pp. 1160–1166; S. Czernik, A.V. Bridgwater, Overview of applications of biomass fast pyrolysis oil, Energy Fuel 18 (2004) 590–598. Adapted with permission from S. Czernik, A.V. Bridgwater, Overview of applications of biomass fast pyrolysis oil, Energy Fuel 18 (2004) 590–598. Copyright 2004 American Chemical Society.

Table 22.2 Pyrolysis methods and their key features

Name

Particle size of reactor bed

Residence time

Heating rate

Temp. (°C)

Slow

Massive

5–30 min

Low

500 °C

Fast

Fine (<1 mm) 0.5–5 s

High

400–650 °C

Flash

Very fine <1 s (<0.2 mm)

Very high 650–1000 °C

Major products Charcoal, bio-oil, gas Bio-oil, charcoal, gas Bio-oil, gas, charcoal

References [55] [56–59] [60]

Data obtained from D. Vamvuka, Bio-oil, solid and gaseous biofuels from biomass pyrolysis processes— an overview, Int. J. Energy Res. 35 (2011) 835–862; A. Demirbas, Pyrolysis of ground beech wood in irregular heating rate conditions, J. Anal. Appl. Pyrolysis 73 (2005) 39–43.

A bio-oil is not thermodynamically stable. Indeed, its highly reactive components may react with each other [68]. The efficiency of these reactions increases with time and temperature. During aging, the bio-oil properties are modified at the exception of the pH [69]. An increase of the

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Fundamentals and Applications of Fourier Transform Mass Spectrometry

Table 22.3 Typical abundance of the major classes of pyrolysis bio-oil components Major components

Abundance wt.%

Aldehydes Alcohols Carboxylic acids Furans Ketones Phenolic monomers Phenolic oligomers Sugars Water

10–20 2–5 4–15 1–4 1–5 2–5 15–30 20–35 20–30

Adapted with permission from M. Stas, D. Kubicka, J. Chudoba, M. Pospisil, Overview of analytical methods used for chemical characterization of pyrolysis biooil, Energy Fuel 28 (2014) 385–402. Copyright 2014 American Chemical Society.

viscosity was associated with the formation of water by condensation mechanisms [69] and increase of the bio-oil component mass. Indeed, polymerization reactions occur, especially with aldehydes, which induces a shift of the average molecular weight to higher values [68]. Liquefaction bio-oil The liquefaction of the biomass occurs at a lower temperature than pyrolysis (200–500 °C) but at a higher pressure (5–30 MPa) with a reaction time ranging from 30 to 240 min [48, 70]. The highly pressurized system is responsible for the important cost of the liquefaction process. Depending on the composition of the medium, it is possible to distinguish three kinds of liquefaction processes: the hydropyrolysis [71], the hydrothermal liquefaction (HTL) [49, 72, 73], and solvolysis [50, 74]. The first process consists of heating the lignocellulosic material, without drying step as in pyrolysis, at high pressure (H2) with a solid catalyst. The second one is performed by mixing the biomass with a solvent (water is commonly used). The solvolysis is carried out in a solvent different from water. Ethanol, methanol or acetone may be used. The liquefaction bio-oils have a more important higher energy density (HHV), lower oxygen content and moisture than the pyrolysis bio-oils (Table 22.1). Nevertheless, they have a lower HHV and a higher oxygen content than conventional fuel oils. They are water-insoluble liquids containing phenols, carboxylic acids, aldehydes, ketones, alcohols, and nitrogen compounds [75, 76]. This kind of bio-oil is also subject to aging processes

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685

[77, 78]. Similarly to pyrolysis bio-oil, some upgrading processes involving heterogeneous catalysis are necessary to remove oxygen, reduce the viscosity and the TAN, and to increase the HHV.

Upgrading treatments As previously explained, the physicochemical properties of the bio-oils, initially obtained by BTL processes, do not enable its direct use as fuels. Therefore, some upgrading strategies have been developed, as illustrated in Fig. 22.2, to convert bio-oil into fuels and value-added chemicals [47, 79–82]. The goals of such treatments are to increase the HHV, to reduce or eliminate the oxygen content, to decrease the value of the TAN, and to finally obtain bio-oils with properties close to petroleum oils. These upgrading processes are mostly applicable to both liquefaction and pyrolysis bio-oils [83–85]. Different upgrading treatments, including heterogeneous catalysis such as hydrotreatment [86–89], zeolite upgrading [90–96], condensation [97, 98] or fractionation [99–103] may be used after the production of the bio-oil. Nevertheless, some of these treatments may be conducted during the bio-oil production itself like the hydropyrolysis [104, 105] and the catalytic pyrolysis [89, 92, 106, 107]. The most efficient upgrading treatments of bio-oil (heterogeneous catalysis) are already used for petroleum refinery. Therefore, the development of biomass derived fuels only requires limited-cost development [108]. Unfortunately, the deactivation of the catalysts is significant and is mainly due to coke deposition [95, 109–111]. The water contained in the bio-oil vapors is

Fig. 22.2 Upgrading pathways for the conversion of pyrolysis bio-oils into biofuels and chemicals. (Reprinted from H. Wang, J. Male, Y. Wang, Recent advances in hydrotreating of pyrolysis bio-oil and its oxygen-containing model compounds, ACS Catal. 3 (2013) 1047–1070, with permission from American Chemical Society (2013).)

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Fundamentals and Applications of Fourier Transform Mass Spectrometry

responsible for dealumination phenomenon, which induces the catalyst deactivation [96], as well as catalyst poisoning [111]. To improve the bio-oil yield and the catalyst efficiency, an extensive description of the bio-oil is necessary before and after upgrading treatment. This may be performed by employing an important analytical workflow. The different possible approaches will be described in the following sections.

Analytical methods for bio-oil characterization Introduction The chemical composition of the bio-oil is highly complex in terms of features. Therefore, a detailed knowledge of its thousands of components is required. This knowledge ensures to improve the processes involved in the production, upgrading, and valorization of this resource [65, 112, 113]. The thousands species that compose this material extend on a broad range of polarity and molecular weight [113, 114]. Hence, different analytical methods are used depending on the class of investigated compounds and the required information. On an analytical viewpoint, the first kind of techniques is dedicated to identifying the chemical functions present in the different bio-oil components and the second one tries to identify the bio-oil components at a molecular level. The targeted analytical methods used with bio-oils will be briefly mentioned in this section. A deep insight will be given on the petroleomic non-targeted approach, which uses FT-MS-based instruments and ensures to provide access to the molecular formulae of each detected bio-oil constituent, especially for high-mass species.

Sample preparation and targeted analytical methods Sample preparation Prior analysis, the bio-oils can be subject to specific sample preparation in order to obtain more detailed information [65, 115]. Some of these pretreatments are related to adsorption chromatography [116–119], liquid-liquid extraction [120–123], supercritical fluid extraction [124, 125], gel permeation chromatography [114, 126, 127], sample derivatization [128–130], solid phase extraction [131, 132], and high performance thin layer chromatography [133]. Some solvent-free methods including solid phase micro-extraction [134, 135], and molecular distillation are also employed [136–138].

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687

Targeted analytical methods The targeted analyses refer to methods able to characterize specifically a given number of predefined compounds or species with a specific chemical function. For example, FT-IR or NMR ensures to give information on bio-oil compounds with a specific chemical group. The liquid or gas chromatography coupled to mass spectrometry is able to give quantitative information on known pre-selected components in respect to retention time and the obtained mass spectrum in MS or MSn detection mode [65, 115, 139–141]. Some studies are reported in Table 22.4 in respect to the used analytical technique. Both GC and LC techniques ensure to carry out qualitative and quantitative analyses of some bio-oil components. The compounds identified by these approaches only represent a portion of the bio-oil components [45]. Indeed, GC allows observing the volatile bio-oils species while LC is dedicated to the observation of the polar and semi-volatile ones. Moreover, the molecular mass of the detected compounds is generally low (<370 Da). A global bio-oil description is necessary in order to improve the operating conditions of both its production and its upgrading. Consequently, other analytical approaches have to be applied to identify compounds on a broader molecular mass range and to limit the possible discrimination effects of chromatography previously mentioned. High-resolution mass spectrometry

Table 22.4 Targeted analysis performed to study bio-oils Analysis

References of some studies

Fourier transform infrared spectroscopy Nuclear magnetic resonance spectroscopy ▪ 1H and 13C (1D and 2D) ▪ 31P and 19F (after derivatization)

[142–146] [147] [148–152] [153–157]

Gas chromatography (GC) ▪ GC-FID and GC-MS analyses ▪ Pyrolysis GC-MS ▪ 2D-GC

[158–163] [164–166] [167–173]

Liquid chromatography (LC) ▪ Gel permeation chromatography ▪ High performance liquid chromatography and 2D-LC

[114, 126] [174, 175]

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Fundamentals and Applications of Fourier Transform Mass Spectrometry

with different ionization methods has this capability. It enables to detect and assign thousands of compounds (at the exception of isomers), regardless of their functional groups or classes, and are widely used for the analysis of petroleum crude oils. This non-targeted approach is named petroleomic approach and will be detailed in the next section of this chapter [65, 112, 113].

Petroleomic non-targeted approach Introduction Marshall et al. introduced the petroleomic approach with FT-ICR MS [176]. His method refers to the non-targeted analysis used to characterize petroleum crude oils. Typically, the sample is introduced without preliminary separation in the ion source of the mass spectrometer to obtain a “global” mass spectrum. Due to the complexity of the sample (thousands of compounds), a high-resolution mass spectrometer (HRMS) is required to distinguish the different features with an ultrahigh mass resolving power (>400,000). Moreover, the high mass measurement accuracy achieved by these instruments is needed to unambiguously assign a unique molecular formula (CcHhNnOoSs) to the different features. The high-resolution instruments capable of such performances are FT-ICR MS and FT-Orbitrap-MS. The critical parameter to obtain an extensive description of an oil is the efficiency of the ionization of its constituents. Unfortunately, no universal technique is capable of ionizing all the oil components whatever its polarity and/or its molecular weight. The combination of the results obtained by different and, if possible, complementary ionization sources has to be employed in order to achieve the most comprehensive sample description. Among the different available ionization techniques, electrospray ionization (ESI), laser desorption ionization (LDI), atmospheric pressure photoionization (APPI), and atmospheric pressure chemical ionization (APCI) are the most commonly used for petroleomic analyses. ESI allows medium to high polar compounds to be ionized. The LDI enables the ionization of nonvolatile species that significantly absorb the laser light and have low to medium polarity. Atmospheric pressure chemical ionization (APCI) is well suited to low polar to polar compounds, but the investigated species have to be thermally stable and volatile or semi-volatile. Moreover, a lower mass range of compounds than the one obtained by ESI is accessible by APCI. APPI is applicable for nonpolar to low polar compounds, which are not efficiently

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689

ionized by the other methods mentioned above. The species detected by both APCI and APPI-MS are extending on a similar mass range. Due to the huge amount of collected data, the interpretation and the comparison between different data sets are not simple. Different graphical representations are generally used to facilitate the data treatment. The van Krevelen diagram is one of them. It represents the observed compounds by dots whose x and y coordinates are O/C and H/C ratios, respectively. Specific area of the diagram gathers lipids, cellulose and hemicellulose (sugar) derivatives, or lignin derivatives (Fig. 22.3) [178]. For compounds involving nitrogen and/or sulfur, N/C and S/C ratios may also be used to draw similar diagrams [179–181]. The double bound equivalent (DBE), which is also a useful descriptor, represents the degree of unsaturation of a molecule and is given by the Eq. 22.1 [179–181]. DBE ¼ C


H N + +1 2 2

(22.1)

Analyses of bio-oils by ESI-FTMS Most of the studies have been performed by ESI-FTMS to characterize biooils. An extensive review of the different published works will be detailed in the next paragraphs and in Table 22.5. Different parameters influencing

Fig. 22.3 van Krevelen diagram with some major biomolecular component areas [177]. (Reprinted from J. Guigue, M. Harir, O. Mathieu, M. Lucio, L. Ranjard, J. Leveque, P. SchmittKopplin, Ultrahigh-resolution FT-ICR mass spectrometry for molecular characterisation of pressurised hot water-extractable organic matter in soils, Biogeochemistry 128 (2016) 307–326, with permission from Springer: Springer Nature, Biogeochemistry (2016).)

Table 22.5 Overview of the publications dealing with the analysis of bio-oils by ESI-HRMS Reference

Process— feedstock

Ion source

Solvent—dopant

Fractionation step

Oxygen range (Max)

Highlights

Influence of the mass spectrometer and pH solution on the bio-oil mass spectrum Smith et al. Fast pyrolysis [182] Red oak

(
) ESI

MeOH:H2O (1:1)

–

O2–O12 (O4–O6)

CxHyOz species with DBE < 5 are sugar compounds CxHyOz species with DBE > 5 are phenolics

O2–O10 (O5) O3–O9 (O5) –

Ox (98% of the TIC), NnOx (0%), SOx (2%) Ox (73% of the TIC), NnOx (27%), SOx (0%) Ox (66% of the TIC), NnOx (0%), SOx (34%)

–

1. Ox (96% of the TIC), NnOx (4%)/2. Ox (32%), NnOx (68%) Ox (11% of the TIC), NnOx (89%) Ox (15% of the TIC), NnOx (85%) Ox (3% of the TIC), NnOx (97%),

Influence of the dopant on the bio-oil mass spectrum Hertzog et al. [183]

Hertzog et al. [184]

Fast pyrolysis Miscanthus

Fast pyrolysis Oak

(
) ESI MeOH – (+) ESI (
) ESI No dopant NH4OH (1%, v/v) HCOOH (1%, v/v) (+) ESI No dopant (2 times)

(+) ESI

NH4OH (1%, v/v) HCOOH (1%, v/v)

O2–O7 (O4) O2–O13 (O5)

AcONH4 (1 mg mL
1) AcONa (0.1 mg mL
1) (2 times) MeOH 3-Cl aniline (1%, v/v)

O3–O6 (O4–5) O2–O14 (O4)

1. Ox (98% of the TIC), NnOx (2%)/2. Ox (98%), NnOx (2%)

Oz*: O3–O12 (O5) Oz*: features initially assigned as CxHyOz Oz**: O1–O8 (O4) Oz**: features initially assigned as CxHyOzNCl for which 3-Cl aniline formula was subtracted and H2O added (correspond to the imine formation) Oz***: O2–O5 Oz***: features initially assigned as CxHyOzN2Cl2 for which 3-Cl aniline formula was subtracted and (O3) H2O added twice (correspond to the formation of 2 imines)

Influence of the feedstock on the bio-oil composition Jarvis et al. [185]

Fast pyrolysis ▪ Mixed conifer – normal – fire salvage

(
) ESI

MeOH NH4OH (1%, v/v)

– O1–O7 (O2,O4) O2–O13 (O7,O10)

Hartman et al. [189]

HTL Two plant cuticles

O2–O7 (O2,O4) O1–O7 (O2,O4) O2–O14 (O5) O3–O13 (O5)

C5–30
DBE ¼ 0–15
N1O5–9 C5–30
DBE ¼ 0–15

O2–O12 (O7) O2–O12 (O5) O2–O11 (O5) O2–O8 (O2) O2–O10 (O5)

Lignin, sugar derivatives, and lipids. Lignin, sugar derivatives, and lipids. Lignin and lipids. Lipids, condensed aromatics, and lignin. Lignin, sugar derivatives, and lipids. (+) ESI: N2O0–2 and N3 classes

O2–O13 (O7)

– beetle kill salvage ▪ White Oak ▪ Scotch broom Flash pyrolysis Tessarolo ▪ Empty palm fruit et al. [186] ▪ Pine wood chips Fast pyrolysis Abdelnur ▪ Eucalyptus et al. [187] ▪ Eucalyptus bark ▪ Cellulosic mud ▪ Water hyacinth ▪ Pine Santos et al. Fast pyrolysis [188] Three invasive aquatic plants

C10–50
DBE ¼ 0–15
B1O5 C10–45
DBE ¼ 0–20
B1O4–12 (B1O6)
N1O6–10 (N1O8) C10–45
DBE ¼ 0–20
B1O4–12 (B1O7)
N1O6–10 (N1O8) C10–40
DBE ¼ 0–25
B1O4–6 C10–50
DBE ¼ 0–30
B1O4–6

(
) ESI

(
) ESI

(
) ESI (+) ESI

(
) ESI

MeOH

–

MeOH NH4OH (0.2%, v/v)

–

MeOH:Toluene (1:1) NH4OH (0.2%, v/v) HCOOH (0.2%, v/v) MeOH:THF (1:1)

–

MeOH

Spontaneous bio-oil separation into aqueous1 and oily2 phases O2–O14 (O7)1

(
) ESI: O2–4 and NO3, and N3O2 classes Oz species with DBE ¼ 1–17 (7 max) –

O1–O14

C10–51HyO1–14 (90%), C13–45HyNO2–7 (5%), and CxHySOz (5%) assigned classes.

Composition of fractionated bio-oils Miettinen et al. [190]

Slow pyrolysis Pine

(
) ESI

O2–O8 (O2)

2

1

2

Sugar derivatives, pyrolytic lignin, and lipids. C8–25
DBE ¼ 0–15 Lipids and pyrolytic lignin. C12–30
DBE ¼ 0–15

Continued

Table 22.5 Overview of the publications dealing with the analysis of bio-oils by ESI-HRMS—cont’d Reference Jarvis et al. [191]

Process— feedstock Fast pyrolysis

Ion source (
) ESI

Solvent—dopant MeOH

▪ Pine ▪ Peanut hull

Stankovikj et al. [192]

Fast pyrolysis with two different technologies Pine

Sudasinghe HTL Microalga et al. [193]

(
) ESI

MeOH

Oxygen range (Max)

Fractionation step 1

Spontaneous bio-oil separation into aqueous and oily phases Pine O2–O14 (O10)1 O2–O11 (O4)2 Peanut O2–O10 (O7)1 O2–O8 (O2)2 Tech. 1 Bio-oils (BO) were injected into ice cold water, the waterWS: O2–O18 (O8) insoluble fraction precipitate and the remaining solution formed the water-soluble fraction (WS). Bio-oil and WS BO: O2–O17 (O7) were characterized. Tech. 2 WS: O2–O17 (O10) BO: O2–O16 (O10)

(
) ESI CHCl3:MeOH:2Lighter oil (BO) and heavier aqueous fraction (AF) were (+) ESI spontaneously formed. The last one was diluted 200-fold propanol (1:2:4) and 1000-fold for (+) and (
) modes, respectively. NH4OH (0.1%, v/v) HCOOH (0.1%, v/v)

(
) ESI

MeOH

C5–30
DBE ¼ 0–15 C5–40
DBE ¼ 0–20 C5–30
DBE ¼ 0–15
N1–2O3–10
N3O4–9 C5–35
DBE ¼ 0–20
N1O3–8
N2O3–7 C6–30
DBE ¼ 1–15
Mw ¼ 418 g/mol C6–30
DBE ¼ 1–14
Mw ¼ 383 g/mol C6–27
DBE ¼ 1–12
Mw ¼ 410 g/mol C5–27
DBE ¼ 1–12
Mw ¼ 378 g/mol  450 peaks assigned in the BO.  700 peaks assigned in the WS. Other compound classes were identified.

(
) ESI BO: O2–O8 (O2) AF: O2–O10 (O2) (+) ESI BO: / AF: /

Cheng et al. Fast pyrolysis [194] Red pine

Highlights

2

Three fractions were obtained by a three-step CO2 supercritical Raw BO fluid extraction by increasing the pressure and the methanol O2–O16 (O10) ratio. Fraction 1 O2–O13 (O8) Fraction 2 O2–O17 (O10) Fraction 3 O4–O18 (O12)

N1O1–5
N2O1–6.  2770 identified peaks. N1O1–9
N2O1–6.  1740 identified peaks. N1O0–2
N2–3O0–3
N4O0–1.  4600 identified peaks. N1O0–2
N2–3O0–3
N4O0–1.  3370 identified peaks. Lipids, condensed aromatics, sugar and lignin derivatives. Lipids and lignin derivatives. Lipids, condensed aromatics, sugar and lignin derivatives. Condensed aromatics, sugar and lignin derivatives.

Liu et al. [195]

Fast pyrolysis Red pine

(
) ESI

(
) ESI

MeOH

MeOH

Dhungana et al. [196]

Slow pyrolysis

He et al. [197]

Slow pyrolysis (
) ESI MeOH Cottonseed meal (+) ESI

Liquid-liquid extraction was performed to obtain the hexane-soluble (HS), ether-soluble (ES), ether-insoluble (EIS), dichloromethane-soluble (DS), and methanolsoluble (MeS) fractions.

Spontaneous separation of the bio-oils into aqueous1 and oily2 fractions.

▪ Corn stover (CS) ▪ Pine shavings (PS) Spontaneous separation of the bio-oils into aqueous (AF) and oil (OF) fractions.

Raw BO O2–O16 (O7) HS O2–O13 (O6) ES O3–O14 (O8) EIS O2–O17 (O10) DS O2–O15 (O7) MeS O3–O16 (O8) CS1: O1–O7 (O2) CS2: O1–O6 (O2) PS1: O1–O8 (O5) PS2: O1–O7 (O2) (
) ESI AF: O2–O10 (O2) OF: O2–O10+13 (O2) (+) ESI AF: O1–O10 (O2) OF: O1–O11 (O4)

C4–39
DBE ¼ 1–22 Acids, alcohols, and lignin monomers. Soluble polar compounds (acids, ketones, furans, phenols, alcohols, nitrides, …) Polysaccharides. Lignin dimers. Lignin dimers and trimers DBE ¼ 0–10 DBE ¼ 1–14 DBE ¼ 1–12 DBE ¼ 1–15

N1–3Ox
S1Ox N1–4Ox
S1–2Ox
N1–4S1Ox
N1S2Ox

N1–4Ox N1–4Ox
S1Ox
N1+4S1Ox

Effect of the production process and operating conditions on the bio-oil composition Yan et al. [198]

Methanolysis*/ Ethanolysis** Sweet sorghum stalk

(
) ESI MeOH:Toluene (+) ESI (1:3)

–

(
) ESI *: O1–O10 (O4) **: O1–O10 (O3) (+) ESI *: O1–O2 **: O1–O2

C5–35
DBE ¼ 1–14
N1O0–10 (N1O3) C5–35
DBE ¼ 1–14
N1O0–10 (N1O3) DBE ¼ 1
N1O0–3
N2O0–6 (N2O3) DBE ¼ 1–2
N1O0–3
N2O0–6 (N2O3)

Continued

Table 22.5 Overview of the publications dealing with the analysis of bio-oils by ESI-HRMS—cont’d Reference

Process— feedstock

Ion source

Yan et al. [199]

Methanolysis Cornstalk

(
) ESI

Kek€al€ainen Pyrolysis at 300* and (
) ESI et al. 380** °C [200] Silver birch Sudasinghe HTL*/Fast pyrolysis**/ et al. Hydrotreatment [148] with HTL*** Pine

Tessarolo et al. [201]

Fractionation step

MeOH:Toluene (1:3)

Sequential extraction of the bio-oil was performed (see Fig. 22.8).

MeOH NH4OH (1%, v/v)

–

(
) ESI CHCl3:MeOH (1:1) – (+) ESI NH4OH (0.1%, v/v) HCOOH (0.1%, v/v)

(
) ESI Fast pyrolysis at 450*, 500**, and 550*** °C without and with catalyst (Z) ▪ Pine ▪ Sugarcane bagasse

Solvent—dopant

MeOH

–

Oxygen range (Max) SP1 O1–O10 (O3) SP2 O1–O8 (O3) SP5 O1–O10 (O8)

* O2–O14 (O7) ** O2–O12 (O6) (
) ESI *: O1–O10 (O4) **: O1–O15 (O6) ***: O1–O3 (O1) (+) ESI no CxHyOz compounds whatever the used process or feedstock Pine *: O2–O12 (O4) **: O2–O11 (O4) ***: O2–O11 (O4) Pine +Z *: no CxHyOz **: O2–O9 (O4) ***: O2–O9 (O4)

Highlights 1800 assigned formula. Mw ¼ 334 g/mol C5–35
DBE ¼ 1–14
N1O2–8 (N1O4) 1800 assigned formula. Mw ¼ 324 g/mol C5–35
DBE ¼ 1–14
N1O2–8 (N1O4) 1000 assigned formula. Mw ¼ 286 g/mol C5–35
DBE ¼ 1–14
N1O1–8 (N1O5) Ox (90% of the TIC), NnOx (5%), SSOx (3%), NamOx (3%) C10–24
DBE ¼ 0–14
N1O4–10 (N1O7) C8–23
DBE ¼ 0–14
N1O4–9 (N1O7) C8–45
DBE ¼ 5–28 C5–40
DBE ¼ 2–25

*: N1O0–5 (N1O1)
N2O0–3
N3 **: N1O2–12 (N1O8)
N2O0–6
N3O0–1 ***: N1O0–3 (N1)
N2O0–2

Among the O2 species, – fatty acids (DBE ¼ 1) – aromatics (DBE > 5) Some O4 species related to lignin derivatives.

(
) ESI Koike et al. Fast pyrolysis [202] without and with catalyst Cedar

Bi et al. [203]

(
) ESI Fast pyrolysis Forestry residues HDO treatment at 1501, 2102, 3003, and 3604 ° C.

Ethanol (1 μL/mL)

MeOH

–

–

Without catalyst O1–O16 (O7) Ni2P/SiO2 O1–O9 (O2) Ni/SiO2 O1–O17 (O3) Raw BO O2–O16 (O7) UBO1 O2–O14 (O7) UBO2 O2–O13 (O6) UBO3 O1–O11 (O5) UBO4 O1–O10 (O4)

Results obtained with Ni2P/SiO2

• •

At 300 °C: 10, 20, and 30 g of catalyst yield biooils with 21.9, 21.0, and 20.6 wt.% of oxygen. At 350 °C: 5, 10, and 20 g of catalysts yield biooils with 25.8, 22.3, and 17.9 wt.% of oxygen.

DBE ¼ 1–21 (10) DBE ¼ 1–20 (9) DBE ¼ 1–21 (9) DBE ¼ 1–21 (9) DBE ¼ 1–21 (9)

696

Fundamentals and Applications of Fourier Transform Mass Spectrometry

the bio-oil composition have been assessed. The focus has been put on the implied process, the feedstock, the solvent, and the dopant used for the production and/or the analysis of these bio-oils. Influence of the ESI FTMS methodology on the bio-oil description Nature of the mass spectrometer and pH of the bio-oil solution

Smith et al. characterized a bio-oil with different high resolution mass spectrometers (FT-ICR, Orbitrap, and Quadrupole Time of Flight Q-TOF) by (
) ESI [182]. They achieved similar results whatever the instrument. FT-ICR MS provided the best mass resolution but presented a significant low-mass discrimination. Similar observations were made by Abdelnur et al. who characterized bio-oil by FT-ICR and Q-TOF mass spectrometry [187]. The method developed by Smith et al. enabled them to assign over 800 features, mainly CxHyO2–12 compounds related to sugar derivatives and phenolic compounds. Furthermore, these authors evidenced the effect of the pH of the bio-oil solution on the ionization efficiency of the bio-oil components. To adjust the pH, acetic acid and ammonium hydroxide were added to obtain solutions with 3.5, 5, 7, or 9 pH values. The observed variation of the ion signal, in negative ion mode, was associated with the pKa of the bio-oil components. At the highest pH values, the signal for the O4–O5 sugar compounds was suppressed while at pH 5, a more important signal was obtained. At low pH, acetate ion abstracted a proton from sugar derivatives, which ensured their detection. Some of the phenolic derivatives are efficiently detected at low pH value, while others are more intensely observed with neutral or alkaline pH condition. Nevertheless, ion suppression phenomenon is observed at high pH values [182]. Influence of dopants added to the bio-oil solution

Formic acid and ammonium hydroxide are typically used to increase protonation and deprotonation for positive and negative ion ESI analyses, respectively. Nevertheless, other dopants may be used to promote ionization. Hertzog et al. used formic acid and NH4OH, for the (
) ESI FT-ICR MS analysis of a bio-oil from Miscanthus pyrolysis and, formic acid, NH4OH, ammonium acetate, and sodium acetate for (+) ESI FT-ICR MS [183]. Whatever the used ion detection mode, the achieved bio-oil composition description (class compound distribution) significantly differed from one experiment condition to another depending on used dopant. Moreover, in some dopant conditions, the achieved bio-oil description differed from the bio-oil elemental composition. The sulfur and nitrogen

Contribution of FTMS to bio-oil study

697

amounts for such bio-oil represented <1% of its elemental composition, but nitrogen- and sulfur-containing ions were responsible for 27% and 85% of the total ion current (TIC), in the negative (with ammonium hydroxide) and the positive (with formic acid) detection mode, respectively. They consequently demonstrated the dramatic biased effect introduced by the commonly used dopants on the bio-oil composition description and concluded that (+) ESI and (
) ESI analyses have to be carried out with sodium acetate and without dopant, respectively, to give a representative description of the bio-oil composition. Moreover, the used conditions ensured a high measurement reproducibility. These results were compared with the Jarvis et al. [191] and Miettinen et al. [190] studies, where the bio-oil sample was fractionated prior to ESI-FT-ICR-MS analysis. In both studies, the authors observed two phases: the oily and aqueous ones. Each of these fractions was associated with compounds resulting from the pyrolysis of the different lignocellulosic biomass constituents, as illustrated in Fig. 22.7. In the Hertzog et al. approach, no fractionation was needed to obtain a full description of the bio-oil. The use of sodium acetate in (+) ESI allowed for an efficient and simultaneous ionization of the compounds relative to the oily and aqueous phases (Fig. 22.4). This methodology has consequently the great advantage of assessing the relative importance of each of these fractions in

Fig. 22.4 van Krevelen diagram of the CxHyOz compounds of a pyrolysis Miscanthus bio-oil analyzed by ESI FT-ICR MS in negative (yellow) and positive (blue) detection modes. (Adapted from J. Hertzog, V. Carr
e, Y. Le Brech, A. Dufour, F. Aubriet, Toward controlled ionization conditions for ESI-FT-ICR-MS analysis of bio-oils from lignocellulosic material, Energy Fuel 30 (2016) 5729–5739, with permission from American Chemical Society (2016).)

698

Fundamentals and Applications of Fourier Transform Mass Spectrometry

the investigated bio-oil, in contrast to other methods, without any tedious fractionation steps. Hertzog et al. also highlighted that primary amine reacted in ESI ion source with the carbonyl functions of the bio-oil components to yield imine species. This behavior was responsible for the large contribution of the CxHyOzNn to the TIC and confirmed that ammonium hydroxide has to be carefully used as dopant in (
) ESI-MS of bio-oils. This bias was used as a tool to seek carbonyl compounds by non-targeted analysis. Indeed, the (+) ESI FT-ICR MS analysis on an oak pyrolysis bio-oil doped with 3-chloroaniline led to the detection of CxHyOzNnCln features, which corresponded to imine derivatization of carbonyl compounds [184]. Tracking ion pairs, which were separated by the mass difference of 35Cl and 37Cl, was an easy way to highlight aldehyde and ketone bio-oil components. Application of the ESI FTMS methodology on different types of bio-oil ESI-FTMS analysis of bio-oils from various feedstock

Bio-oils from three different woody species (oak, scotch broom, and conifer) have been characterized by (
) ESI FT-ICR MS by Jarvis et al. [185]. They evidenced CxHyOz and CxHyBOz compounds whose relative distribution was similar regardless of the feedstock. In the bio-oils from oak and scotch broom, the C12–30HyO2 species, with DBE ¼ 1, were relative to fatty acids. For the mixed conifer bio-oil, the O2 species with 20 carbon atoms and DBE of 6 or 7 corresponded to resin acids (abietic acid, dihydroabietic acid). The presence of boron in part of the bio-oil components was confirmed by 11B NMR analysis, which demonstrated that boron was involved in polysaccharide complexes. The presence of boron is not senseless as it was defined to be essential to the plant development [204]. The bio-oils from mixed conifer after beetle kill salvage and fire salvage had a similar composition but it differed from those of the oak, scotch broom, and mixed conifer bio-oils. Indeed, for the two first bio-oils, the CxHyOz and CxHyBOz compound classes extend on a broader range, in respect to oxygen atom count, than the distribution obtained with the bio-oil from non-salvaged wood. The CxHyBOz class was more abundant in the mixed conifer beetle kill salvage bio-oil. Some nitrogen species were also observed. They were more abundant in the mixed conifer fire salvage bio-oil. For this latter bio-oil, the carbon number vs. DBE was different to what was observed for the other investigated bio-oils. Boron and nitrogen components can be problematic in terms of bio-oil upgrading process, as these compounds are known to be catalyst poisons.

Contribution of FTMS to bio-oil study

699

Tessarolo et al. performed (
) ESI FT-ICR MS and GCxGC-TOF-MS measurements on bio-oils from empty palm fruit and pine wood chips [186]. In both samples, different CxHyOz compound classes were identified and the observed distributions, in respect to oxygen atom count, were similar (O5 species were prominent and mainly related to sugar derivatives). The O2 compounds were only observed in the empty palm fruit bio-oil and they corresponded to fatty acids, such as palmitic and stearic acid. Nitrogen compounds, which were specifically detected in the palm fruit bio-oil, were thought to be sugar-derived components coupled to some nitrogencontained secondary pyrolysis products of the biomass. The phenolic compounds were pointed out in both samples. Thus, the assignment of 836 and 564 peaks was achieved for the mass spectra of the empty palm fruit and pine wood chip bio-oil, respectively. The authors also demonstrated the complementarity of both techniques for the comprehensive bio-oil description. The bio-oils from eucalyptus, eucalyptus bark, cellulosic mud, water hyacinth, and pine were characterized by Abdelnur et al. by (
) ESI coupled to a FT-ICR or a Q-TOF instrument [187]. Oxygen-containing compounds were detected and their distribution, in respect with the oxygen atom count, was found to be dependent on the raw material (Fig. 22.5).

35 30 25 %

20 15 10 5 0 O2

O3

O4

O5

O6

O7

O8

O9

O10

O11

O12

Class Hyacinth

Eucalypt. bark

Eucalypt.

Mud

Pine

Fig. 22.5 Oxygen class distribution for five bio-oils analyzed by (
) ESI FT-ICR MS. (Reprinted from P.V. Abdelnur, B.G. Vaz, J.D. Rocha, M.B.B. de Almeida, M.A.G. Teixeira, R.C.L. Pereira, Characterization of bio-oils from different pyrolysis process steps and biomass using high-resolution mass spectrometry, Energy Fuel 27 (2013) 6646–6654, with permission from American Chemical Society (2013).)

700

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Some compositional differences were observed between the light fraction bio-oils and the bio-oils and between the bio-oils from different feedstock. The O2 compounds, which were prominent in the water hyacinth bio-oil, were related to lipids and more especially to fatty acids. In both eucalyptus and eucalyptus bark bio-oils, levoglucosan (C6H10O5) was intensely detected. The light bio-oil fraction of each sample was collected at a different step of the pyrolysis process. The yielded bio-oil composition was highly dependent on the location in the reactor where bio-oil was collected. For both eucalyptus-based bio-oils, the oxygen class distributions extended on a similar range. Oxygenated compounds were less abundant in the light fraction than in the heavy one. The light fraction of the water hyacinth bio-oil contained more oxygen-rich (O4–O11) compounds than the heavy one, which was mainly composed of O2 and O3 species. The van Krevelen diagram of the light fraction evidenced mainly sugar derivatives (levoglucosan) and, to a lesser extent, aromatics. Lipids, condensed aromatics, and lignin were also detected in the heavy fraction. Thus, these authors demonstrated that the bio-oil composition was dependent on the biomass feedstock but also on the vapor residence time in the reactor. Santos et al. characterized bio-oils from three different freshwater plants in positive and negative ESI FT-ICR MS [188]. In positive ion detection mode, more than 1000 formulae were assigned whatever the studied sample, and N2O0–2 compounds were the main detected species. These compounds belong to pyridine, imidazole, and pyrazine derivatives. In negative ion detection mode, between 718 and 944 features were assigned. Oxygencontaining species and, to a lesser extent, nitrogen compounds were identified. In the former class of compounds, phenols and carboxylic acids were identified. For the O2 species, fatty acids were the major detected species. Thus, these authors demonstrated the contribution of the main acidic and basic components of bio-oils by using negative and positive ion detection modes, respectively. The HTL bio-oils from two plant cuticles were characterized by (
) ESI FT-ICR MS in the work of Hartman et al [189]. Similar bio-oil compositions were obtained with close to 90% of the signal related to CxHyOz species. For the agave cuticle bio-oil mass spectrum, 861 peaks were assigned whereas 1683 ones were attributed in the study of Capsicum cuticle bio-oil. Close to 650 of the obtained features are common to both bio-oils. The van Krevelen diagrams of both samples showed that the main part of the compounds was from lipids. These authors demonstrated that the achieved biooils have a HHV of 40 MJ kg
1.

Contribution of FTMS to bio-oil study

701

ESI-FTMS analysis of bio-oil fractions

Water-soluble and water-insoluble fractions In the study of Miettinen et al., the bio-oil obtained by slow pyrolysis of pine spontaneously fractioned into aqueous and oily phases, which were analyzed by (
) ESI FT-ICR MS [190]. In both fractions, CxHyOz compounds were identified, whereas some CxHyNamOz species were specifically pointed out in the aqueous phase. Both classes of compounds contributed to more than 95% of the total ion signal. In the oily phase, the distribution of the CxHyOz class in respect with the oxygen atom count was narrower than in the aqueous phase (Fig. 22.6). The obtained van Krevelen diagrams demonstrated that lipids (fatty and resin acids) and phenolic compounds from lignin pyrolysis were the major components of the oily phase. Even if these components were also detected in the aqueous phase, this latter fraction contained mainly sugar compounds from the cellulose and hemicellulose pyrolysis (Fig. 22.7). Indeed, such sugar derivatives presented a higher oxygen amount than those of the pyrolytic lignin and the lipid components. Similar experiments were performed by Jarvis et al. [191] on aqueous and oily phases of bio-oils from pine and peanut hull. Both bio-oils spontaneously

Fig. 22.6 Distribution of CxHyOz compounds in respect with oxygen atom count from the oily and aqueous phases. (Reprinted from I. Miettinen, M. Ma€kinen, T. Vilppo, J. Ja€nis, Compositional characterization of phase-separated pine wood slow pyrolysis oil by negative-ion electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry, Energy Fuel. (2015), with permission from American Chemical Society (2015).)

702

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Fig. 22.7 Color-mapped van Krevelen diagrams of CxHyOz compounds in the oily and aqueous. (Reprinted from I. Miettinen, M. Ma€kinen, T. Vilppo, J. Ja€nis, Compositional characterization of phase-separated pine wood slow pyrolysis oil by negative-ion electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry, Energy Fuel. (2015), with permission from American Chemical Society (2015).)

separated into two phases, which were analyzed by (
) ESI FT-ICR MS. The authors observed a broader distribution of the oxygen class compounds in the aqueous fraction than in the oily phase whatever the used feedstock. The O2 species, which were intensely detected in the oily phases, were attributed to fatty and resins acids. The other compounds are relative to aromatics. Some nitrogen species were observed for the peanut hull bio-oil, in the aqueous phase and, to a lesser extent, in the oily one. This was in agreement with the bulk analysis performed on the used feedstock. This study demonstrated the capability of the petroleomic approach to distinguish bio-oil in regards to the nature of the feedstock and the oily or water fraction. Stankovikj et al. characterized bio-oils from pinewood obtained by two different pyrolysis technologies. After fractionation into water-soluble and water-insoluble phases, different analytical methods have been used (GCMS, ion chromatography, UV spectroscopy, NMR, and ESI-MS), which allowed more than 90 wt.% of the water-soluble fraction and the raw bio-oils to be quantified [192]. The light bio-oil and the aqueous fraction of a microalga HTL bio-oil were analyzed in both positive and negative ion ESI FT-ICR MS by Sudasinghe et al. [193]. In (
) ESI, CxHyOz and CxHyN1–2Oz compounds were evidenced for all samples. In (+) ESI, the bio-oil compositions were similar and contained only the basic CxHyN1–4Oz species. The predominant species observed in both samples were related to aromatic nitrogen compounds and free fatty acids.

Contribution of FTMS to bio-oil study

703

The results obtained by Dhungana et al. [196] were found to be qualitatively similar to Jarvis et al. [191] and Liu et al. [195] ones. In this study, authors also performed ion-mobility-time-of-flight-mass spectrometry on bio-oils. This technique ensured to give insight into the structure of the most abundant bio-oil components belonging to a selected CH2 homologous series. Nevertheless, the low resolving power in the drift time and mass dimension is still a hurdle for the comprehensive structural characterization of bio-oils. He et al. [197] performed (+) and (
) ESI FT-ICR MS analysis of a slow pyrolysis bio-oil of cottonseed, a resource known to be N-rich. Both oily and aqueous fractions were characterized. The main assignments were relative to N-containing components but S-containing species were also evidenced. Complementary results, in terms of assignments and compound classes, were highlighted concerning on the one hand, the investigated fractions, and on the other, the ion-detection modes. Other fractionation processes Cheng et al. carried out supercritical CO2 extraction on bio-oil to obtain three sub-fractions [194]. The four samples (whole bio-oil and three sub-fractions) were analyzed by (
) ESI FT-ICR MS and by other analytical methods (1H NMR, FT-IR, and GC-MS). Both CxHyOz and CxHyN1O3–16 compounds were detected. In respect with the investigated sample, the van Krevelen diagrams of the CxHyOz compound class showed some differences. Lipids, derivatives of cellulose, hemicellulose and lignin, and condensed aromatics were observed in the raw bio-oil. The fraction E1 was characterized by a large amount of lipids, whereas sugar derivatives and condensed aromatic were more importantly evidenced in fraction E2 and E3, respectively. This demonstrated the ability of CO2 supercritical extraction for bio-oil fractionation to yield a more detailed bio-oil composition description and to give some insights at the molecular level [194]. Liu et al. performed solvent fractionation and obtained hexane-soluble (HS), ether-soluble (ES), ether-insoluble (EIS), dichloromethane-soluble (DS), and methanol-soluble (MeS) extracts [195]. The bio-oil and its five sub-fractions were characterized by (
) ESI FT-ICR MS and GC-MS. CxHyOz and CxHyN1O3–14 compounds were evidenced in all investigated samples. GC-MS ensured to observe the most volatile and/or the lightest bio-oil constituents. The ESI FT-ICR MS was more suited to the analysis of the less volatile and/or the heaviest compounds. The volatile ones were lost due to solvent evaporation during the ESI process. Specific classes of

704

Fundamentals and Applications of Fourier Transform Mass Spectrometry

compounds have been identified by both mass spectrometry approaches in respect to the investigated fraction. In the HS and DS sub-fractions, the polar compounds were detected but the DS one contained the heaviest ones. More information dealing with the description of the different sub-fractions is available in the Table 22.5. ESI FT-MS analysis for optimizing the production and upgrading processes of bio-oils

Yan et al. compared the chemical composition of two bio-oils obtained by methanolysis and ethanolysis of sweet sorghum stalk by (+) and (
) ESI FT-ICR MS [198]. In negative ion mode, C5–35HyO1–10 and C7–35HyN1O0–10 compounds were identified in both samples. The O1–O3 species were more intensely detected in the bio-oil from ethanolysis than the one from methanolysis. The van Krevelen diagrams of the oxygenated compounds demonstrate that lipids (fatty acids), sugar compounds, and lignin derivatives were detected in both bio-oils. The authors showed that the lignin-derived compounds were the main acidic species. In positive ion mode, the C10–40HyN2O1–6 basic nitrogen compounds were the main contributor to the mass spectrum. These species were related to amine, pyridine, and quinolone linked-compounds. The same research group performed methanolysis of cornstalk in different alkaline conditions at different temperatures [199]. A solvent fractionation was applied. Five fractions (SP1–SP5) and a residue, named inextractable portion (IEP), were obtained (Fig. 22.8). Whatever the operating conditions (temperature and NaOH amounts), the highest liquid amounts were obtained for the SP1 and SP5 fractions. The increase of the NaOH/cornstalk ratio and temperature led to a significant increase of the SP1 fraction and, to a lesser extent, of the SP2, SP3, and SP4 ones. The amount of the SP5 fraction decreased with the increase of the temperature but increased with the NaOH/cornstalk ratio. Overall, the amount of soluble fractions increased with the increase of the temperature and the NaOH/cornstalk ratio. The six samples were analyzed by GC-MS and the global chemical composition of SP1, SP2, and SP5 fractions was assessed by (
) ESI FT-ICR MS. The results achieved by petroleomic approach ensured to identify C5–35HyO1–10 and C10–30HyN1O0–8 compounds in the three fractions. Some differences, in terms of mass range and oxygen distribution, were evidenced in respect with the investigated fraction. Kek€al€ainen et al. studied the influence of the pyrolysis temperature (300 or 380 °C) on the composition of birch wood bio-oils. The observed

Contribution of FTMS to bio-oil study

705

Fig. 22.8 Solvent fractionation procedure for cornstalk bio-oil obtained by methanolysis. CDS, carbon disulphide; EOE, ethoxyethane; PE, Petroleum ether. (Reprinted from H.-L. Yan, Z.-M. Zong, Z.-K. Li, J. Kong, Q.-X. Zheng, M.-X. Zhao, Y. Li, X.-Y. Wei, Insight into the chemical complexity of soluble portions from cornstalk methanolysis, Energy Fuel 30 (2016) 3020–3029, with permission from American Chemical Society (2016).)

O2–O4 species were attributed to lipids and the O5–O7 ones to ligninderived compounds. The most oxygenated species were associated with cellulose and hemicellulose derivatives. Their amounts were more important when the pyrolysis was performed at 300 °C. Sulfur hydrocarbons were also observed and assigned to sulfonic acids [200].

706

Fundamentals and Applications of Fourier Transform Mass Spectrometry

The bio-oils from pine produced by HTL, pyrolysis, and hydrotreated HTL were characterized by ESI FT-ICR MS by Sudasinghe et al. [148]. The pyrolysis process yielded more bio-oil than the HTL one. Moreover, the pyrolysis bio-oil contained a broader range of oxygenated and nitrogen compounds than the HTL one. For a given oxygen compound class, the bio-oil components were more unsaturated for the HTL oil than for the pyrolysis one. A Co/Mo catalyst was used to upgrade the HTL bio-oil. The resulting bio-oil presents smaller oxygen and nitrogen amounts than the raw bio-oil, which demonstrated the efficiency of the deoxygenation and denitrogenation treatment. Tessarolo et al. characterized normal and catalytic pyrolysis bio-oils from pine wood and sugarcane bagasse by (
) ESI FT-ICR MS, 1H NMR, and GCxGC-TOF-MS [201]. These authors investigated the effects of the pyrolysis temperature and the use of a ZSM-5 catalyst on the produced bio-oil composition. They demonstrated that an increase of the temperature was responsible for a higher degradation rate of the biomass and the formation of poorly oxygenated compounds. The efficiency of the deoxygenation treatment was assessed by the removal of the highest oxygenated compounds. In their operating conditions, they did not observe any influence of the temperature on the effectiveness of the catalytic treatment. The authors also evidenced that ZSM-5 catalyst provided an increase of the phenolic compounds from the lignin degradation. The same analytical workflow was employed by Koike et al. to study the upgrading efficiency of different deoxygenation catalysts (Ni2P/SiO2, Ni/ SiO2, Pd/C, and ZSM-5), at 300 and 350 °C. This post-treatment was applied on the bio-oil components obtained by fast pyrolysis of Cedar. They observed that the activity of Ni2P/SiO2 was superior to the other used catalysts. Moreover, the lowest oxygen amounts were obtained when the highest amounts of catalyst were used at 350 °C [202]. Bi et al. assessed the influence of the temperature during HDO treatment on the chemical composition of upgraded bio-oils by comparing the composition of the raw bio-oil with HDO upgraded bio-oils in respect with the used temperatures [203]. The distribution of CxHyOz compounds according to the oxygen atom count was found to be narrower and shifted to lower values when the temperature was increased, without modification of the DBE values. The obtained van Krevelen diagrams evidenced some reactions, which occurred during the HDO treatment (Fig. 22.9). The authors demonstrated that HDO at 150 °C promoted the breakdown of the ether linkages. Between 210 and 300 °C, dehydration and hydrodeoxygenation

Contribution of FTMS to bio-oil study

707

Fig. 22.9 van Krevelen diagram of CxHyOz compounds assigned by (
) ESI FT-ICR MS analysis of the raw pyrolysis bio-oil. Red lines represent chemical reactions: methoxylation/demethoxylation (A), hydrogenation/dehydrogenation (B), hydration/ dehydration (A/C), hydrodeoxygenation (D), decarboxylation (E), decarbonylation (F), and de-ethyoxyl (G). (Adapted from Y. Bi, G. Wang, Q. Shi, C. Xu, J. Gao, Compositional changes during hydrodeoxygenation of biomass pyrolysis oil, Energy Fuel 28 (2014) 2571–2580, with permission from American Chemical Society (2014).)

occurred. Other reactions such as decarboxylation and decarbonylation required a temperature higher than 360 °C. The results obtained by (
) ESI FT-ICR MS demonstrated the loss of the most oxygenated compounds in the upgraded bio-oils and the increase of the abundance of the oxygen-poor ones. However, this method is not effective enough to assess catalysis efficiency. In fact, part of the new catalysis products, which are likely to be more unsaturated and less or not oxygenated, cannot be efficiently detected by ESI. Comments on the ESI FT-MS analysis of bio-oil ESI is an efficient and commonly used technique to characterize a wide variety of bio-oils. In ESI, the influence of the operating conditions has been observed on the elemental composition of a bio-oil. Moreover, some studies demonstrated the ability of the ESI FTMS to highlight the influence of the used feedstock or catalyst on the bio-oil composition description. In most works involving ESI-FTMS, negative-ion mode was used. However, it has been demonstrated that positive detection mode, in well-controlled ionization solution, dramatically increases the detection of new class of compounds. In addition, as it was observed for ketones and aldehydes, the use

708

Fundamentals and Applications of Fourier Transform Mass Spectrometry

of a reagent in the spray solution allows observing a functional selectivity, supplying information that are not available by non-targeted analysis. ESI only ionizes the mid-polar to polar bio-oil components. To detect the less polar and apolar compounds, other ionization sources have to be employed. Typically, laser desorption ionization (LDI), atmospheric pressure photoionization (APPI), and atmospheric pressure chemical ionization (APCI) sources are more suited to ionize less polar compounds than ESI. Moreover, these ionization techniques are well suited to address the follow-up of the upgrading treatments due to an efficient ionization of the less oxygenated and, consequently, the less polar bio-oil components.

Analyses of bio-oils by APPI, APCI, and LDI-FTMS—Additional insights to ESI-FTMS analyses Table 22.6 gathers the published works related to analysis of bio-oils by LDI, APPI, and APCI. Part of them also were compared to the results obtained with ESI-FTMS experiments. The HTL bio-oils from three different feedstock were characterized by (+/
) ESI and (+) APPI FT-ICR MS. The results were combined and compared to what it was obtained in the study of crude petroleum and shale oils [205]. The heteroatom class distributions obtained by Jarvis et al. are reported in Fig. 22.10. They evidenced that HTL bio-oils were compositionally more similar to the shale oil than to the petroleum crude oil. Nevertheless, the HTL bio-oil mainly contained oxygenated compounds, whereas crude and shale oils were mixtures of hydrocarbons and nitrogen-containing hydrocarbon species. Similar comparison was made by Ware et al. [206] who studied by (+) APPI FT-ICR MS, among other analytical technics, bio-oils from fast pyrolysis of landfill, plastic, and pine. They observed that bio-oils from the two first raw materials presented high hydrocarbon content whereas the pine bio-oil was more aromatic and also contained important amounts of polar species. Therefore, landfill and plastic bio-oils have to be regarded as promising materials for renewable fuels. Cole et al. characterized the pyrolysis bio-oils of switchgrass harvested on June, July, August, and November 2010, and April 2011 [207]. The bio-oils were analyzed, in positive and negative ion modes, by ESI Orbitrap-MS and in positive ion mode by APPI Orbitrap-MS. In (+) ESI, a broad variety of nitrogen compounds has been observed whose amounts were in agreement with the elemental composition of the used feedstock. The CxHyNz compounds were efficiently protonated according to their basicity and represent

Table 22.6 Overview of the publications dealing with the analysis of bio-oils by APPI, APCI, and LDI-FTMS Reference

Process—feedstock

Ion source

Solvent—dopant

Chemical composition

MeOH TMAH (0.25%, v/v) HCCOH (1%, v/v)

• More than 85% of the assigned

(
) ESI (+) ESI (+) APPI

MeOH:Toluene (9:1)

Analyses involving APPI/APCI/LDI-FTMS

Jarvis et al. [205]

HTL ▪ Pine ▪ Microalgae ▪ Sewage sludge

Fast pyrolysis ▪ Landfill ▪ Plastic ▪ Pine

(+) APPI Toluene Toluene MeOH:Toluene

CH + O1–6 (CH) CH + O1–7 (CH) CH + O1–16 (O8–10)

709

Continued

Contribution of FTMS to bio-oil study

Ware et al. [206]

formula from the (+) ESI mass spectra are exclusive to HTL bio-oils and more than 43% in negative ion mode. • APPI ensured to assign peaks 11,000, (+) ESI 9900 and (
) ESI 1650 in HTL bio-oils. For shale and crude oils, 3150, 2350, and 4200 peaks were assigned by (+) APPI, (+) ESI, and (
) ESI, respectively. • The oxygen-containing species range from 1 to 8 in the pine bio-oil, from 1 to 4 in the microalgae one, and from 1 to 5 in the sewage sludge bio-oil.

Process—feedstock

Ion source

Solvent—dopant

Chemical composition

Cole et al. [207]

Fast pyrolysis Switchgrass

(
) ESI (+) ESI (+) APPI

MeOH:H2O (1:1) MeOH:H2O (1:1) MeOH:Toluene (85:15)

• (
) ESI: CxHyOz (sugar deriva-

HTL Organic solid wastes

(+) APPI

MeOH:Toluene (1:1)

Leonardis et al. [208]

Miettinen et al. [209]

Fast pyrolysis

(
) ESI

MeOH

Coppice willow

(+) APPI

MeOH:Toluene (1:1)

tives and acidic species) compounds. • (+) ESI: CxHyNOz (95%) and CxHyOz (5%) compounds. N1O0–6 (N1O1)
N2O0–3 (N2)
N3O0–1
O1–6 (O2–3)
NaO1–7 (O3–4) • (+) APPI: CxHyOz (76%) and CxHyNOz (24%) compounds observed. N1O0–3 (N1O2)
N2O0–2 (N2)

• 2091 assigned formulae. • For N1–2, N1O1–2, and N2O1 classes about 45% of the carbon atoms are aliphatic and 55% are aromatic. Reversed values were observed for the N2O2 class. 585 CxHyOz assignments: O2–10 (O5–6)—lipids, and lignin and sugar derivatives. 1120 CxHyOz assignments: O1–13 (O8)—lipids and lignin derivatives (less oxygenated species)

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Reference

710

Table 22.6 Overview of the publications dealing with the analysis of bio-oils by APPI, APCI, and LDI-FTMS—cont’d

Croce et al. [210]

HTL ▪ Glucose ▪ Cellulose

Croce et al. [211]

Chiaberge et al. [212]

▪ Cellulose ▪ BSA ▪ Tripalmitin HTL Organic solid wastes

Fast pyrolysis Wood

MeOH MeOH:Toluene (1:1)

The resulting bio-oil is composed by two different phases: an aqueous phase analyzed in (
) ESI and an organic one analyzed in (+) APPI.

(+) APPI

MeOH MeOH:Toluene (1:1)

The resulting bio-oil is composed by an aqueous phase and an organic phase, both analyzed in (+) APPI.

(+) ESI

MeOH

(+) APPI

MeOH:Toluene (1:1)

(+) APCI

MeOH

(
) ESI (
) APCI

MeOH

(+) ESI: N1–3 (N2)
N1–4O1 (N1O1)
N1–3O2 (N2O2)
N1–3O3 (+) APPI: CH, O1–2 (O2)
N1–3 (N2)
N1–4O1 (N1O1)
N1–3O2 (N2O2)
N1–3O3 (+) APCI: CH, O1–2 (O2)
N1–3 (N2)
N1–4O1 (N1O1)
N1–3O2 (N2O2)
N1–3O3 Similar oxygen class distributions were obtained whatever the mass spectrometer and the ionization source. O1–12 (O4)-

711

Continued

Contribution of FTMS to bio-oil study

Stasˇ et al. [213]

HTL

(
) ESI (+) APPI

Table 22.6 Overview of the publications dealing with the analysis of bio-oils by APPI, APCI, and LDI-FTMS—cont’d Ion source

Solvent—dopant

Stasˇ et al. [214]

Flash pyrolysis ▪ Spruce wood ▪ Beech wood ▪ Poplar wood ▪ Miscanthus HTL Microalgae

(+/
) ESI (+/
) APCI

MeOH

(+) APCI

Heptane

Fast pyrolysis

(
) LDI (337.7 nm)

(+) ESI

Sample dissolved in methanol, 2-propanol, and ACN MeOH AcONa (0.1 mg mL
1)

(
) ESI (+) APPI (+) LDI* (
) LDI (
) LDI*

– – 7/3 MeOH/H2O v/v 7/3 MeOH/H2O v/v 7/3 MeOH/H2O v/v

(
) ESI (
) LDI 355 nm

MeOH

Sanguineti et al. [215]

Smith et al. [216]

Hertzog et al. [217]

Olcese et al. [171]

▪ Hydrolytic lignin ▪ Pine Fast pyrolysis Oak

Fast pyrolysis Lignin

Chemical composition

O1–8 (O4)
DBE: 0–12 (2) O1–9 (O4)
DBE: 0–11 (3) O1–8 (O4)
DBE: 0–10 (2) O1–8 (O4)
DBE: 0–10 (4) HHV of the HTL bio-oils average between 35 and 39 MJ/kg which makes them heavy crude-like bio-oil. O3–8 (O4) O3–7 (O4)
136 assigned chemical formulas. Lignin derivatives. O1–14 (O5 and O10–11)
DBE: 0–10 O2–16 (O4–6)
DBE: 5–10 O1–13 (O4–8)
DBE: 5–20 O2–14 (O5 and O11)
DBE: 3–15 O3–17 (O4–6)
DBE: 5 O3–10 (O4–6)
DBE: 10–20 * Nd:YAG (355 nm) (
) ESI: Raw: O2–8 (O4) (
) LDI Raw BO: O2–8 (O4) With 2 g of catalyst: O1–6 (O3) With 4 g of catalyst: O1–5 (O3)

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Process—feedstock

712

Reference

Hertzog et al. [218]

Catalytic fast pyrolysis Oak

(+) ESI (
) ESI (+) APPI (
) LDI

MeOH AcONa (0.1 mg mL
1) – – 7/3 MeOH/H2O v/v

Microporous (A) and mesoporous (B) zeolites were used for catalytic fast pyrolysis. Two runs were undertaken with the pyrolysis of seven oak cylinders respectively. Hetereoatom class distributions achieved for the different produced bio-oil, in regards to the ionization sources and the detection modes (see Fig. 22.12).

Contribution of FTMS to bio-oil study

713

714

Gom crude oil Shale oil Pine biocrude Chlorella biocrude Sewage sludge biocrude

50

% Relative abundance

% Relative abundance

60

60

50 40 30

40

30

20

20 10

10 0 HC

N1

N2

N1Ox

Heteroatom class

N2Ox

Ox

0 HC

N1

N2

N1Ox

N2Ox

N3Ox

N4Ox

Ox

S1

Heteroatom class

Fig. 22.10 Heteroatom class distributions derived from the (
) ESI and (+) APPI mass spectra in respect with the different samples. (Adapted from J.M. Jarvis, J.M. Billing, R.T. Hallen, A.J. Schmidt, T.M. Schaub, Hydrothermal liquefaction biocrude compositions compared to petroleum crude and shale oil, Energy Fuel 31 (2017) 2896–2906, with permission from American Chemical Society (2017).)

Fundamentals and Applications of Fourier Transform Mass Spectrometry

70

(+) APPI

(-) ESI

80

Contribution of FTMS to bio-oil study

715

95% of the TIC. The remaining contribution was linked to oxygenated compounds detected as proton and sodium adduct ions. From June to April, the relative abundance of all nitrogen compounds decreased, whereas those of the oxygenated species increased. In (+) APPI, the nitrogen species only represented 24% of the TIC. Finally, this study demonstrated the compositional change of the bio-oil in respect to the harvest time. Leonardis et al. characterized a HTL bio-oil from organic wastes. Such a bio-oil was investigated by FT-ICR MS, 1H and 13C NMR, and GC-MS, to develop a quantitation method of the different heteroatom bio-oil classes of compounds. The measurement performed by (+) APPI FT-ICR MS ensured to “quantify” pure hydrocarbons, N1–3, N0–4O1, N0–3O2, and N1–3O3 species [208]. The characterization of pyrolysis bio-oils of coppice willow was undergone by ESI and APPI FT-ICR MS, in negative and positive ion modes, respectively [209]. In this study, Miettienen et al. demonstrated the complementarity of both ionization sources for a more exhaustive bio-oil composition description. Croce et al. used a similar approach to study and model the HTL process of cellulose and glucose. More specifically, the authors investigated the decomposition of these carbohydrates under HTL. They demonstrated that polymerization, dehydration, and decarboxylation reactions occurred [210]. They recently extended this workflow to other compounds of bio-oil produced by HTL of organic wastes [211]. Proteins (with bovine serum albumin BSA), lipids (with tripalmitin), and cellulose were used as model compounds. The achieved molecular composition was very similar to the HTL bio-oil one obtained by Leonardis et al. with solid organic wastes [208]. Chiaberge et al. investigated a bio-oil produced by HTL of organic solid wastes by ESI, APPI, and APCI FT-ICR MS, in positive ion mode [212]. The results obtained in APCI and APPI were found to be very similar. Indeed, Among 2356 and 2247 formulae assigned in APCI and APPI, respectively, they found that 598 were common to both analyses. More than 2000 features were observed in ESI while only a tiny fraction of them were also observed by APPI (41) or APCI (210). ESI was found to be more sensitive to the most polar components of the bio-oil, which were characterized by a higher N/C ratio. A bio-oil from wood pyrolysis was characterized by ESI and APCI-Orbitrap-MS in negative ion detection mode by Stasˇ et al. [213]. The capabilities of different generations of Orbitrap mass spectrometers were evaluated in this study. The same range of oxygen-containing species

716

Fundamentals and Applications of Fourier Transform Mass Spectrometry

(O1–12) were observed in all experiments irrespective of the used ionization source and instrument. The maximum of the obtained distributions in respect with the oxygen atom count was always associated with the O4 compounds. However, some differences were highlighted according to the resolution power of the different instruments and the used ionization source. In ESI, O2 and O3 compounds were more significantly observed with the most powerful instrument (resolution ranging from 240,000 to 480,000). In contrast, the relative abundance of the oxygen-rich assignments is more important with the less powerful device (resolution ranging from 30,000 to 100,000), which meant incorrect assignment in that latter case and demonstrated the need of very high resolution to achieve such analysis. A similar trend is observed when they considered the DBE distribution of the features observed by ESI-MS. With low mass resolution instrument, the relative abundance of the low unsaturated compounds was indeed more important than with the high mass resolution device. In APPI, the different distributions did not reveal significant differences whatever the mass spectrometer. Furthermore, for both ionization sources, an increasing number of assigned molecular formulae was obtained with the increase of the instrument mass resolution. This group also carried out the analysis of pyrolysis bio-oils from four different biomass raw materials by ESI and APCI Orbitrap-MS in both positive and negative detection modes [214]. The achieved results were compared with those obtained from the analysis, with the same conditions, of a model bio-oil. They evidenced some differences in respect with the abundances of the compound classes whatever the investigated bio-oil. Sanguinetti et al. performed (+) APCI FT-ICR MS analyses of HTL bio-oils from algae to evaluate the influence of the temperature (250 or 300 °C) and the nature of the medium (distilled water or saltwater) on the composition of the produced bio-oil [215]. Among the thousand identified compounds, N1O1, CH, and O2 classes represented the most abundant compounds whatever the used process conditions. The authors demonstrated that a high temperature was responsible for a more important yield of the HTL process, in terms of number of generated components. In contrast, smaller number of compounds was obtained when saltwater was added than without. At 300 °C with saltwater, the abundance of the N1O1 class was definitely lower than with other operating conditions. LDI may also be used to study bio-oil. For example, Smith et al. used LDI to characterize hydrolytic lignin and a bio-oil from pine pyrolysis by Orbitrap-MS [216]. The authors investigated the influence of several matrices for MALDI analysis. A slight improvement of the signal was achieved

Contribution of FTMS to bio-oil study

717

when colloidal graphite was used but graphite significantly contributed to the background. For other evaluated matrices, no significant differences were observed when the results were compared to LDI experiments. The same behavior was observed, especially for oxygenated bio-oil compounds. The DBE distribution, in respect with the oxygen atom count, demonstrated that the O4 compounds were less unsaturated than the O6 species. In fact, they assessed that O4 and O6 compounds correspond to dimer and trimer units of lignin depolimerization products, respectively. This study demonstrated that LDI is well suited for the characterization of non-volatile aromatic compounds. A bio-oil from oak pyrolysis was characterized by ESI, APPI, and LDI FT-ICR MS in both positive and negative mode by Hertzog et al. [217]. It was demonstrated the capabilities of the combination of these three ionization sources to achieve an extensive bio-oil composition description. Indeed, ESI was found to be more specific to the detection of the more polar species (high O/C and low DBE) such as sugars, while APPI ensured to ionize more unsaturated and less polar species. The species that were specifically detected by LDI analysis, presented a range of unsaturation degrees intermediate to ESI and APPI one. The combination of negative ion ESI and LDI FT-ICR MS analyses was reported by Olcese et al. to monitor the effectiveness of different catalytic hydrotreatment of a bio-oil produced by lignin pyrolysis [171]. Indeed, the polarity of bio-oil decreases after upgrading, which makes ESI less adapted to characterize the treated bio-oil. In contrast, LDI is less sensitive to the compound polarity. When experiments were performed on the raw bio-oil, the distributions of the oxygenated compounds, in respect with the oxygen atom count, were similar for both ionization sources. Significant differences were observed for the distribution of these oxygenated compounds in respect with the DBE value (Fig. 22.11). LDI enabled to detect more unsaturated compounds than ESI. The van Krevelen diagrams confirmed these observations. Compounds with the lowest O/C and H/C ratios were more intensely detected by LDI. After bio-oil upgrading treatment, these authors observed a decrease of the oxygen amounts in the bio-oil components by LDI FT-ICR MS. Consequently, LDI was used to more specifically investigate the influence of the catalytic treatment on the upgraded bio-oil composition. The abundance of the poor-oxygen compounds was more important when large amounts of catalyst were used. The authors also demonstrated the limitations of the catalytic treatment and the occurrence of some refractory oxygenated compounds.

718

Fundamentals and Applications of Fourier Transform Mass Spectrometry

Fig. 22.11 Oxygen (left) and DBE (right) distributions for the CxHyOz compounds of the raw lignin pyrolysis bio-oil analyzed in ESI and LDI FT-ICR MS in negative ion mode. (Adapted from R. Olcese, V. Carr
e, F. Aubriet, A. Dufour, Selectivity of bio-oils catalytic hydrotreatment assessed by petroleomic and GC*GC/MS-FID analysis, Energy Fuel 27 (2013) 2135–2145, with permission from American Chemical Society (2013).)

A similar approach was used by Hertzog et al. to assess the efficiency of two different zeolite catalysts on an oak pyrolysis bio-oil by ESI, APPI, and LDI FT-ICR MS in both positive and negative detection modes [218]. The (+) and (
) ESI analyses ensured to monitor the selectivity of the catalysts toward the sugar species, whereas (+) APPI and (
) LDI enabled to evidence the formation of the catalysis products that are less oxygenated or fully

Contribution of FTMS to bio-oil study

719

deoxygenated and more unsaturated than the raw bio-oil components. In addition, this approach allowed distinguishing the effectiveness of these catalysts overtime (Fig. 22.12).

Concluding remarks on the characterization of bio-oil by FT-MS Regarding the different studies performed by FTMS on bio-oils, most of the experiments are conducted by ESI, and more specifically in negative ion mode. In fact, the bio-oil components have the ability to be easily deprotonated according to their acidity. For this reason, (
) ESI appears to be more efficient and sensitive than (+) ESI. Typically, the bio-oils are characterized without pretreatment, apart from dilution in appropriate solvents. The addition of deprotonation and protonation agents can also be carried out to improve ionization. It is well known that ESI is more sensitive to polar compounds, which represent only a part of the bio-oil components. Therefore, ionization sources that are less sensitive to the compound polarity such as APPI, LDI, and APCI have to be employed to achieve an extensive description of bio-oils. The petroleomic approach is suitable to obtain the chemical signature and the global composition of the bio-oil by assignment of thousands of compounds. Such analyses ensure to observe compositional changes depending on the feedstock and the parameters of the thermochemical process (nature of the medium, temperature, …). It is also a very useful tool for monitoring the efficiency of an upgrading treatment such as the catalytic cracking and the deoxygenation. Even if FTMS analyses enable to detect and assign thousands of species, they have some limitations. Indeed, volatile and low-mass compounds are not easily analyzed and no structural information is achieved. Moreover, quantitation of the different species or classes of compounds cannot be achieved and the isomers are not distinguished by this method. Therefore, some complementary analyses have to be considered to obtain additional information on the bio-oil composition and to try to overcome some of the drawbacks of the non-targeted approach. It has been seen that some reagents can specifically react with bio-oil compounds of particular chemical functions [184]. Such reaction can be used as part of a nontargeted approach to highlight specific compound classes. Furthermore, the ion mobility may be used to distinguish the isomeric compounds. While only a few number of studies were performed on bio-oil [196], this

720 Fundamentals and Applications of Fourier Transform Mass Spectrometry

Fig. 22.12 Relative distribution of CxHy and CxHyOz compounds according to the number of oxygen atoms in positive and negative ion ESI, LDI, and APPI FT-ICR MS for raw bio-oil and upgraded A and B bio-oils from the pyrolysis of the 1–7 and 8–14 oak cylinders. (Adapted from J. Hertzog, V. Carr
e, L. Jia, C.L. Mackay, L. Pinard, A. Dufour, O. Mašek, F. Aubriet, Catalytic fast pyrolysis of biomass over microporous and hierarchical zeolites: characterization of heavy products, ACS Sustain. Chem. Eng. 6 (2018) 4717–4728, with permission from American Chemical Society (2018).)

Contribution of FTMS to bio-oil study

721

technique was already applied to the investigation of petroleum oils [219–223]. It ensured to perform a rapid isomer separation and to highlight compound structural diversity. Structural information may be obtained by using tandem mass spectrometry. This aspect is still poorly documented in the literature for the bio-oil analysis and more generally for non-targeted FTMS methodology. Indeed, it is quite impossible with FTMS to efficiently isolate a parent ion with a m/z value that differs from the neighboring ion by some mDa. Consequently, other strategies may be used. The first one is to separate the bio-oil components by chromatography prior to performing MSn experiment. In that case, it may be expected that the preliminary separation allows isobaric interferences to be limited. The main advantage of this approach is the ability to perform MSn with n > 2 and to assess the possible quantitation according to the TIC. Nevertheless, the huge number of features will make this approach difficult and long. The second strategy is the so-called 2D FTMS approach [224, 225]. This method ensures to obtain a 2D map on which one direction is associated with the parent ions and the second one is relative to the fragment ion. No selection of the parent ions is required. In fact, the succession of two excitations, separated by a delay time, ensures to specifically put, at the center of the ICR cell, an ion with a given m/z ratio, which may be submitted to infra-red multiphoton activation. The variation of the time between both excitation steps ensures to perform the activation of all the ions present on the mass spectrum. This requires some hours to be achieved and intensive treatment of the huge amounts of data (some tens of GB) is also due.

Acknowledgments The authors would like to thank the editors, Philippe Schmitt-Kopplin and Basem Kanawati, for having given them the great opportunity to participate to the drafting of this book dedicated to the fundamentals and applications of the Fourier transform mass spectrometry.

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