Comparing Laser Desorption Ionization and Atmospheric Pressure

Article pubs.acs.org/EF

Comparing Laser Desorption Ionization and Atmospheric Pressure Photoionization Coupled to Fourier Transform Ion Cyclotron Resonance Mass Spectrometry To Characterize Shale Oils at the Molecular Level Yunju Cho,† Jang Mi Jin,†,‡ Matthias Witt,§ Justin E. Birdwell,∥ Jeong-Geol Na,⊥ Nam-Sun Roh,⊥ and Sunghwan Kim*,†,# †

Department of Chemistry, Kyungpook National University, Daegu 702-701, Republic of Korea Division of Mass Spectrometry Research, Korea Basic Science Institute, Ochang 863-883, Republic of Korea § Bruker Daltonik GmbH, 28359 Bremen, Germany ∥ United States Geological Survey, Denver Federal Center, Post Office Box 25046, MS 977, Denver, Colorado 80225, United States ⊥ Climate Change Technology Research Division, Korea Institute of Energy Research, Daejeon 305-343, Republic of Korea # Green-Nano Materials Research Center, Daegu 702-701, Republic of Korea ‡

S Supporting Information *

ABSTRACT: Laser desorption ionization (LDI) coupled to Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was used to analyze shale oils. Previous work showed that LDI is a sensitive ionization technique for assessing aromatic nitrogen compounds, and oils generated from Green River Formation oil shales are well-documented as being rich in nitrogen. The data presented here demonstrate that LDI is effective in ionizing high-double-bond-equivalent (DBE) compounds and, therefore, is a suitable method for characterizing compounds with condensed structures. Additionally, LDI generates radical cations and protonated ions concurrently, the distribution of which depends upon the molecular structures and elemental compositions, and the basicity of compounds is closely related to the generation of protonated ions. This study demonstrates that LDI FT-ICR MS is an effective ionization technique for use in the study of shale oils at the molecular level. To the best of our knowledge, this is the first time that LDI FT-ICR MS has been applied to shale oils.

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desorption electrospray ionization (nano-DESI),15 atmospheric pressure photoionization (APPI),10,13,16,17 atmospheric pressure chemical ionization (APCI),10,14,18,19 atmospheric pressure laser ionization (APLI),10,20,21 easy ambient sonic-spray ionization (EASI),22 laser-induced acoustic desorption (LIAD),23,24 and atmospheric pressure laser-induced acoustic desorption chemical ionization (AP/LIAD-CI).25 FT-ICR MS has been used to study unconventional crude oils, such as shale oils, at the molecular level.13,26,27 Previously, only ESI and APPI have been coupled to FT-ICR MS to assess shale oil composition. Laser desorption ionization (LDI) is another potentially useful ionization technique14,28−32 for shale oil characterization, as demonstrated by the recent application of LDI FT-ICR MS to the analysis of crude oil at the molecular level.33 LDI FT-ICR MS is very sensitive toward aromatic nitrogen-containing classes of compounds. Shale oils (generated from oil shales of the Eocene Green River Formation) contain many nitrogen-containing compounds,26 and hence, LDI FTICR MS should be a powerful method in the study of these oils.

INTRODUCTION Crude oil is an important energy source that modern society is heavily dependent upon and will continue to be for the foreseeable future. The study of crude oil and related materials has been named petroleomics,1−5 and the research in this field has become increasingly focused on heavy petroleum sources, including heavy oil, tar sands, and oil shale. Heavy oils are very complex mixtures but are less amenable to characterization by techniques like gas chromatography than typical crude oils because they contain more low volatility compounds. Hence, ultrahigh-resolution techniques, such as Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), have been particularly useful in the characterization of these materials. In mass spectrometry (MS), no single ionization technique can ionize all of the compounds present in a complex sample like shale oils. The selectivity of different ionization techniques can be beneficial when specific types of compounds are to be studied. However, the same selectivity can be difficult to obtain if a broad range of compounds is targeted. To circumvent this problem, several different ionization techniques can be applied to the same sample to examine specific classes of compounds based on their ionization potential using a particular approach. Oil researchers have used various types of ionization techniques in combination with FT-ICR MS, e.g., electron ionization,6,7 field desorption ionization,8,9 electrospray ionization (ESI),9−14 nanospray © 2012 American Chemical Society

Special Issue: 13th International Conference on Petroleum Phase Behavior and Fouling Received: September 24, 2012 Revised: December 5, 2012 Published: December 12, 2012 1830

dx.doi.org/10.1021/ef3015662 | Energy Fuels 2013, 27, 1830−1837

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Table 1. Shale Oils Used in This Study, Their Origins, and Extraction Methodsa abbreviation

sample origin

extraction method

APM-FA

Piceance Basin Mahogany zone, Anvil Points Mine, near Rifle, CO Piceance Basin Garden Gulch Member, company well Uinta Basin Mahogany zone, near Park Canyon, UT Uinta Basin Mahogany zone, near Park Canyon, UT Uinta Basin Mahogany zone, near Park Canyon, UT

Fischer assay Fischer assay Fischer assay hydrous pyrolysis in situ simulator

GGM-FA UMZ-FA UMZ-HP UMZ-ISS a

oil specific gravity

oil yield (mg of oil/g of rock)

Rock Eval hydrogen index (mg of HC/g of TOC)

Rock Eval oxygen index (mg of CO2/g of TOC)

0.911

155.8

1014

14

0.899

102.9

821

8

0.875

282.2

1003

26

0.846

169.2

1003

26

0.760

100.6

1003

26

Rock Eval parameters are for the original, unpyrolyzed shales. calibrated peak list and assigned based on m/z values with a 1 ppm error range. Normal conditions for petroleum data (CcHhNnOoSs, with c unlimited, h unlimited, 0 ≤ n ≤ 5, 0 ≤ o ≤ 5, and 0 ≤ s ≤ 4) were used for these calculations. Double-bond equivalence (DBE) values represent the number of rings plus the number of double bonds to carbon in a given molecular formula. DBE values were calculated using the following equation:39

In the research reported here, LDI FT-ICR MS was applied in combination with FT-ICR MS to characterize a set of nitrogenrich shale oils.

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EXPERIMENTAL SECTION

Shale Oil Samples. The shale oils used in this study were produced by pyrolysis of oil shales from the Eocene Green River Formation, which is a large oil shale deposit in the western United States composed of several lacustrine basins. Oil shale samples were collected from outcrops of the Piceance Basin Mahogany zone oil shale at the Anvil Points Mine (APM) near Rifle, CO. A sample of Uinta Basin Mahogany zone oil shale (UMZ) was collected from outcrops near Park Canyon, UT. A sample of the Piceance Basin Garden Gulch Member (GGM) illitic oil shale was collected from well cuttings and provided by Alan Burnham of American Shale Oil, LLC. All three oil shale samples were subjected to Fischer assay pyrolysis.34 The UMZ oil shale was also pyrolyzed using the in situ simulator35 (ISS) and hydrous pyrolysis36 (HP) techniques. The pyrolysis methods and typical properties of oils generated by these methods are described in our previous work.26 Details on the shale oils analyzed in this study are summarized in Table 1, including hydrogen and oxygen indices from Rock Eval programmed pyrolysis.37 Mass Spectrometric Analysis. The shale oils were analyzed in the LDI positive-ion mode on a SolariX FT-ICR mass spectrometer (Bruker Daltonik GmbH, Bremen, Germany) equipped with a 12 T refrigerated actively shielded superconducting magnet (Bruker Biospin, Wissembourg, France). This instrument is equipped with a frequency-tripled Nd:YAG laser emitting at 355 nm to perform the LDI step. Shale oils were dissolved in dichloromethane (DCM) to a final concentration of 10−20 μg/μL. A total of 10 μL of this solution was applied to a stainlesssteel target and allowed to dry to form a thin homogeneous oil layer with a spot size of about 5 mm diameter. The measurements were performed without applying matrix solution. Spectra were acquired with 4 × 106 data points, and the signal-to-noise (S/N) ratio was enhanced by summing 200 time-domain transients for each spectrum. The shale oils were also analyzed with positive-mode APPI FT-ICR MS for comparison. The 15 T FT-ICR MS at the Korean Basic Science Institute (KBSI, Ochang, Korea) was used for the analyses. The samples were dissolved in toluene at 2 mg/mL, and the prepared samples were directly injected with a syringe pump (Harvard, Holliston, MA) at a flow rate of 500 μL/h. Nitrogen was used as the drying and nebulizing gas. A nebulizing temperature of 400 °C was used at a flow rate of 2.5 L/min. The drying gas temperature was 210 °C at a flow rate of 2.7 L/min and a spray voltage of 3000−3900 V. The skimmer voltage was set to 15.0 V to minimize in-source fragmentation. An approximately 1.5 s transient was obtained for each scan. S/N was enhanced by summing 300 timedomain transients. Internal recalibration of the mass spectra obtained with APPI and LDI in the positive mode was performed using Data Analysis 4.0 software and radical cations of the N1 series. Spectral Interpretation. Spectral interpretation was performed using the Statistical Tool for Organic Mixtures’ Spectra (STORMS 1.0) software with an automated peak-picking algorithm for more reliable and faster results.38 Elemental formulas were calculated from the

DBE = c − h/2 + n/2 + 1 (for elemental formulae of Cc HhNnOoSs)

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(1)

RESULTS AND DISCUSSION Broadband Spectra and Class Distributions. The shale oils listed in Table 1 were analyzed by (+)-LDI and (+)-APPI FT-ICR MS. The obtained time domain and broadband signals are shown in Figure 1. The average transient length was greater than 2 s with a resolving power of about 500 000 at m/z 400. The achieved resolving power was sufficient to resolve the 3.4 mDa split (C3 versus SH4). More than 10 000 peaks with S/N ratios over 5 were assigned for each spectrum. The root-mean-square (RMS) errors between the theoretical values of the assigned elemental formulas and the observed mass numbers calculated for each spectrum are listed in Table 1S of the Supporting Information. These errors were typically less than 0.16 ppm. High-resolution mass spectra of shale oils obtained with APPI FT-ICR MS have been reported before.13,26 However, LDI FTICR MS has not been previously applied to shale oils, and the spectra presented in Figure 1 show that LDI can be used to obtain high-resolution mass spectra. The mass spectra profiles in Figure 1 show how the results from APPI and LDI differ. APPI provided a broader distribution that extended to higher masses, while LDI produced distributions that showed lower molecular weight peaks in most cases. The class distributions of the obtained spectra are presented in Figure 2. Overall, nitrogen-containing classes dominated spectra obtained with both ionization techniques. The class distribution obtained with APPI and LDI were similar to each other. The N1, N2, and N1O1 classes were the most abundant in the shale oils, which is consistent with the findings of previous studies.13,26 For the UMZ-ISS sample (purple bars in Figure 2), N3 compounds were as abundant as N2 compounds. This agrees with the findings of another study in which N2 and N3 classes were prominent in samples prepared by the ISS method.26 The detailed carbon number and DBE distributions of the abundant classes are discussed in the next section. DBE versus Carbon Number Distribution of the N1 Class Observed by LDI and APPI. Figure 3 shows the DBE versus carbon number for the N1 class as found from (+)-LDI 1831


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Figure 1. Broadband spectra of the five shale oils obtained by (a) APPI and (b) LDI positive-ion mode FT-ICR MS (refer to Table 1 for the abbreviations). Insets show the free induction decays collected during analysis. 1832


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particular carbon number. The peaks at the planar limit correspond to compounds having condensed ring structures and short alkyl chains,40 which indicates that LDI is a sensitive technique for compounds with condensed structures. A significant compositional difference was also observed for the shale oils prepared from the same rocks but using different pyrolysis methods (compare the bottom three rows in Figure 3 for UMZ-FA, UMZ-HP, and UMZ-ISS). The APPI data show that the relative abundance of compounds with high DBE decreases in the order UMZ-FA < UMZ-HP < UMZ-ISS (refer to the circles labeled A in Figure 3). The trend agrees well with that for LDI spectra. The relative abundance of compounds with lower DBEs is in the order of UMZ-FA < UMZ-HP < UMZ-ISS (compare the dotted circles labeled B in Figure 3). The examples shown in this study clearly demonstrate that different and useful information can be obtained from MS data collected using both ionization techniques and that the results, although different, are consistent. van Krevelen Diagrams of N2 and N3 Classes Observed by LDI. The van Krevelen diagram is an effective method of displaying data on heteroatom classes containing the same elements on the same figure simultaneously.13 Figure 4 contains van Krevelen diagrams that include the N2 and N3 classes as observed by (+)-LDI for the shale oils generated from the UMZ oil shale. The ratios are those calculated from the observed formulas. The color of the diagrams represents the abundance of the chemical species that possess the indicated ratio values observed in the data. For samples prepared from the same rocks but using different methods, UMZ-ISS had the distribution with the highest H/C and N/C ratios. This implies that the shale oil prepared by the ISS method has the lowest degree of unsaturation for the N2 and N3 classes. The high N/C ratios are attributable to the abundance of N3 class compounds in samples prepared by the ISS method. The van Krevelen diagrams for shale oils prepared by the HP and FA techniques were shifted to lower H/C values compared to those for shale oils prepared by the ISS technique. In particular, the shale oil prepared by the HP method had the lowest H/C and N/C distributions. The shale oil prepared using the HP method also contained numerous peaks at high H/C ratios, e.g., at about 1.7. This agrees with the location of circle B in Figure 3 corresponding to the N1 class. Radical Cations versus Protonated Ions Observed by LDI. The distributions of radical cations and protonated ion peaks observed by LDI for the UMZ-ISS shale oil are presented in Figure 5. The blue bars refer to the relative abundance of protonated ions for a given DBE value, while the red bars correspond to the radical cations. The UMZ-ISS LDI data were chosen because they showed a significantly higher abundance of compounds with multiple nitrogen atoms (Figure 2). In a previous study performed with (+)-ESI, compounds with multiple nitrogen atoms were also observed in significant abundance for samples prepared with the ISS method.26 In that study, the DBE distributions of ions in the N1, N2, and N3 class ions each started at 3.5, 5.5, and 9.5, respectively, which could be related to specific chemical structures. The distribution in Figure 5 is in agreement with those findings. A significant difference was observed between the types of ions generated by ESI and LDI. Protonated ions were exclusively found in ESI spectra.13,26 However, the types of ions generated with LDI were DBE-dependent. For example, in the DBE distribution for the N1 class (Figure 5a), protonated ions were more abundant at lower DBEs (3−8) but radical cations were

Figure 2. Distribution of heteroatom classes observed for the five shale oils by (a) APPI and (b) LDI positive-ion mode FT-ICR (refer to Table 1 for the abbreviations).

and (+)-APPI FT-ICR MS analyses of the shale oils. DBE distributions are also shown as a bar graph in the figure. The DBE distribution for the data obtained with (+)-LDI begins at a DBE of 3, corresponding to radical cations with pyrrole core structures. The DBE distributions of the N1 class of shale oils are quite different from the previously reported N1 DBE distribution of conventional crude oil observed by (+)-LDI. In that study, compounds with DBE values larger than 9 dominated and compounds with DBEs lower than 8 were seldom observed.33 Additionally, abundant protonated ions were observed for the N1 class in the shale oil spectra. DBE values in whole numbers represent radical cations, but DBE values 0.5 unit less than whole numbers represent protonated ions because adding a proton decreases the DBE value by 0.5. Radical cations were more prevalent than protonated ions for the N1 class observed in LDI spectra of conventional crude oil, but many protonated ions were also observed for shale oils. The differences between N1 class distributions are related to structural differences in the N1 class compounds present in shale oils and conventional crude oil. Compounds with DBE values between 4 and 7 (compounds having mono- or bicyclic aromatic structures) were more abundant in spectra obtained with (+)-APPI. However, compounds with DBE values between 8 and 16 (compounds with tri- to pentacyclic aromatic structures) were predominant in spectra acquired with (+)-LDI. The difference indicates that LDI is more sensitive than APPI toward compounds having higher degrees of aromatic conjugation and that spectra obtained with LDI and APPI complement each to provide a more complete understanding of shale oil composition. Another important difference between LDI and APPI concerns the type of ions generated during the ionization process. Protonated ions are the major type of ions generated by APPI of N1 class compounds. In contrast, LDI generated a significant number of N1 class radical cations as well as protonated ions. Protonated and radical cations are discussed further in a later section. Peaks with a higher relative abundance were observed near the planar limit (straight red lines in Figure 3) using LDI compared to APPI. The planar limit indicates the maximum DBE value for a 1833


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Figure 3. DBE versus carbon number (contour plot to the left) and DBE distribution plots (bar graphs to the right) for N1 class compounds observed by (a) APPI and (b) LDI positive-mode FT-ICR MS (refer to Table 1 for the abbreviations). 1834


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Figure 4. van Krevelen diagrams of N2 and N3 class compounds detected in LDI positive-mode spectra of the shale oil samples prepared from the same rocks but using different pyrolysis methods (refer to Table 1 for the abbreviations).

Figure 5. DBE distribution of radical cations (red bars) and protonated (blue bars) ions in N1, N2, and N3 classes observed for the UMZ-ISS shale oil.

an analytical tool to characterize shale oils are summarized as follows. LDI is an effective ionization method for highly unsaturated compounds. Therefore, LDI FT-ICR MS should be a suitable method for characterizing compounds with condensed structures. In addition, LDI produces radical cations and protonated ions, and the distribution of radical cations versus protonated ions varies with the DBE and the number of nitrogens. Identification of the relationship between protonated ion and radical cation ratios and molecular structures will be the subject of future work.

more abundant at higher DBEs (over 8). The types of ions generated with LDI were class-dependent as well. For example, the ions having a DBE of 15 (radical cations) were more abundant than those with a DBE value of 14.5 (protonated ions) in the N1 class, but they were equally abundant in the N2 class. For the N3 class, the compounds with a DBE of 14.5 were more abundant than those with a DBE of 15. The basicity of the Nx class appears to increase with an increasing number of nitrogen atoms, which can be related to structures of Nx compounds present in shale oils.

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CONCLUSION The results shown in this study demonstrate that LDI FT-ICR MS adds another dimension to the study of shale oils at the molecular level. LDI FT-ICR MS can be effectively used to study nitrogen-rich shale oils. The distinguishing characteristics of LDI compared to conventional ionization methods ESI and APPI as

ASSOCIATED CONTENT

S Supporting Information *

RMS errors for the elemental formulae assignment of major classes (Table 1S). This material is available free of charge via the Internet at http://pubs.acs.org. 1835


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(13) Bae, E.; Na, J.-G.; Chung, S. H.; Kim, H.; Kim, S. Identification of about 30 000 chemical components in shale oils by electrospray ionization (ESI) and atmospheric pressure photoionization (APPI) coupled with 15 T Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and a comparison to conventional oil. Energy Fuels 2010, 24 (4), 2563−2569. (14) Smaniotto, A.; Montanari, L.; Flego, C.; Rizzi, A.; Ragazzi, E.; Seraglia, R.; Traldi, P. Can crude oils be distinguished by different component distribution obtained by laser desorption ionization mass spectrometry and evaluated by chemometrics? Rapid Commun. Mass Spectrom. 2008, 22 (10), 1597−1606. (15) Eckert, P. A.; Roach, P. J.; Laskin, A.; Laskin, J. Chemical characterization of crude petroleum using nanospray desorption electrospray ionization coupled with high-resolution mass spectrometry. Anal. Chem. 2011, 84 (3), 1517−1525. (16) Purcell, J. M.; Merdrignac, I.; Rodgers, R. P.; Marshall, A. G.; Gauthier, T.; Guibard, I. Stepwise structural characterization of asphaltenes during deep hydroconversion processes determined by atmospheric pressure photoionization (APPI) Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry. Energy Fuels 2009, 24 (4), 2257−2265. (17) Cho, Y.; Na, J.-G.; Nho, N.-S.; Kim, S.; Kim, S. Application of saturates, aromatics, resins, and asphaltenes crude oil fractionation for detailed chemical characterization of heavy crude oils by Fourier transform ion cyclotron resonance mass spectrometry equipped with atmospheric pressure photoionization. Energy Fuels 2012, 26 (5), 2558− 2565. (18) Kim, Y. H.; Kim, S. Improved abundance sensitivity of molecular ions in positive-ion APCI MS analysis of petroleum in toluene. J. Am. Soc. Mass Spectrom. 2010, 21 (3), 386−392. (19) Borton, D.; Pinkston, D. S.; Hurt, M. R.; Tan, X.; Azyat, K.; Scherer, A.; Tykwinski, R.; Gray, M.; Qian, K.; Kenttämaa, H. I. Molecular structures of asphaltenes based on the dissociation reactions of their ions in mass spectrometry. Energy Fuels 2010, 24 (10), 5548− 5559. (20) Panda, S. K.; Brockmann, K. J.; Benter, T.; Schrader, W. Atmospheric pressure laser ionization (APLI) coupled with Fourier transform ion cyclotron resonance mass spectrometry applied to petroleum samples analysis: Comparison with electrospray ionization and atmospheric pressure photoionization methods. Rapid Commun. Mass Spectrom. 2011, 25 (16), 2317−2326. (21) Schmitt-Kopplin, P.; Englmann, M.; Rossello-Mora, R.; Schiewek, R.; Brockmann, K.; Benter, T.; Schmitz, O. Combining chip-ESI with APLI (cESILI) as a multimode source for analysis of complex mixtures with ultrahigh-resolution mass spectrometry. Anal. Bioanal. Chem. 2008, 391 (8), 2803−2809. (22) Corilo, Y. E.; Vaz, B. G.; Simas, R. C.; Lopes Nascimento, H. D.; Klitzke, C. c. F.; Pereira, R. C. L.; Bastos, W. L.; Santos Neto, E. V.; Rodgers, R. P.; Eberlin, M. N. Petroleomics by EASI(±) FT-ICR MS. Anal. Chem. 2010, 82 (10), 3990−3996. (23) Pinkston, D. S.; Duan, P.; Gallardo, V. A.; Habicht, S. C.; Tan, X. L.; Qian, K. N.; Gray, M.; Mullen, K.; Kenttamaa, H. I. Analysis of asphaltenes and asphaltene model compounds by laser-induced acoustic desorption/Fourier transform ion cyclotron resonance mass spectrometry. Energy Fuels 2009, 23, 5564−5570. (24) Crawford, K. E.; Campbell, J. L.; Fiddler, M. N.; Duan, P.; Qian, K.; Gorbaty, M. L.; Kenttämaa, H. I. Laser-induced acoustic desorption/ Fourier transform ion cyclotron resonance mass spectrometry for petroleum distillate analysis. Anal. Chem. 2005, 77 (24), 7916−7923. (25) Nyadong, L.; McKenna, A. M.; Hendrickson, C. L.; Rodgers, R. P.; Marshall, A. G. Atmospheric pressure laser-induced acoustic desorption chemical ionization Fourier transform ion cyclotron resonance mass spectrometry for the analysis of complex mixtures. Anal. Chem. 2011, 83 (5), 1616−1623. (26) Jin, J. M.; Kim, S.; Birdwell, J. E. Molecular characterization and comparison of shale oils generated by different pyrolysis methods. Energy Fuels 2011, 26 (2), 1054−1062. (27) Chen, X.; Shen, B.; Sun, J.; Wang, C.; Shan, H.; Yang, C.; Li, C. Characterization and comparison of nitrogen compounds in hydro-

AUTHOR INFORMATION

Corresponding Author

*Telephone: 82-53-950-5333. Fax: 82-53-950-6330. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean government (MEST) (20110003796) and the Korea Institute of Energy Research. This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (NRF-2011-Fostering Core Leaders of the Future Basic Science Program). We thank M. Lewan (U.S. Geological Survey, Denver, CO) for assistance with various aspects of this work. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

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