Article pubs.acs.org/EF
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 Yunju Cho,† Jeong-Geol Na,‡ Nam-Sun Nho,‡ SungHong Kim,*,§ and Sunghwan Kim*,†,∥ †
Department of Chemistry, Kyungpook National University, Daegu 702-701, Korea Climate Change Technology Research Division, Korea Institute of Energy Research, Daejeon 305-343, Korea § Korea Basic Science Institute, Daegu 702-701, Korea ∥ Green-Nano Materials Research Center, Kyungpook National University, Daegu 702-701, Korea ‡
ABSTRACT: Heavy crude oil samples, fractionated according to the saturates, aromatics, resins, and asphaltenes (SARA) fractionation method, were analyzed by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) equipped with atmospheric pressure photoionization (APPI). SARA fractionation separates the crude oil into four main classes based on polarity and solubility. FT-ICR MS analyses of each if these fractions yielded spectra quite different from those of unfractionated crude oil. However, the spectrum acquired from the aromatics fraction was very similar to those of the unfractionated crude oil. The class, carbon number, and double-bond equivalence distributions obtained from each fraction were in agreement with what is expected from each SARA fraction. The data acquired from SARA fractions can be used to generate four peak lists for each crude oil sample. A master peak list, representing crude oil, was created by adding the same amount of a synthetic standard compound to each fraction. The abundance of the other peaks relative to the standard was used to combine the four peak lists into a single list. The number of compounds in the master list was twice that obtained by APPI FT-ICR MS analysis of unfractionated crude oil. Numerous NOx and SOx class compounds, which were not observed in the direct analysis of unfractionated heavy crude oils, were abundant in the resins fraction. Overall, this study shows that combining chromatographic techniques, including fractionation, with high-resolution MS is needed for a more complete understanding of the heavy molecules in petroleum.
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INTRODUCTION As the remaining global crude oil deposits become heavier, technological developments are required to better use the heavy crude oil. Many studies have been performed analyzing the chemical composition of crude oil because such knowledge can be useful to understand and predict the properties and behaviors of heavy petroleum.1−8 However, knowledge of the chemical composition of crude oil at the molecular level poses a great challenge because of the extreme complexity of crude oil. Currently, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) is one of the few techniques that can resolve and identify numerous compounds existing in crude oils at the molecular level.9−11 FT-ICR MS facilitates the identification of elemental compositions of organic molecules within crude oils. The double-bond equivalence (DBE) values, calculated from the elemental compositions, can be used to structurally elucidate crude oil compounds. However, crude oil contains such an extensive range of compounds that a complete analysis is not trivial, even with ultrahigh-resolution MS. One of the problems is the matrix effect and ionization suppression, which can limit the number of chemicals observed by FT-ICR MS. Each ionization method yields higher abundances with specific types of compounds and discriminates against others.12 For example, atmospheric pressure photoionization (APPI) more easily ionizes aromatic and sulfur-containing compounds. © 2012 American Chemical Society
Separation techniques, such as liquid chromatography (LC), are often combined with MS to improve the detection capability by reducing matrix effects and ionization suppression. Therefore, separation has played a key role in many MS analyses.13−17 The saturates, aromatics, resins, and asphaltenes (SARA) fractionation method is another separation technique that has been widely used to study crude oil.7,18,19 With SARA fractionation, crude oil compounds are separated into their solubility classes. In fact, the SARA method has also been combined with FT-ICR MS analysis to study crude oils.20,21 However, the previous studies combining SARA and FT-ICR MS were mostly focused on analyzing the polar or asphaltenes fraction and were not intended for overall characterization of SARA fractions. In this study, SARA fractionation and FT-ICR MS were coupled to characterize crude oil compounds in detail. APPI was used as an ionization source for broader characterization of various classes of compounds.22−28 FT-ICR MS analysis of saturates, resins, and asphaltenes fractions yielded spectra with Special Issue: 12th International Conference on Petroleum Phase Behavior and Fouling Received: September 1, 2011 Revised: February 28, 2012 Published: March 8, 2012 2558
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APPI analyses. Analyses were performed with a 15 T FT-ICR MS at the Korea Basic Science Institute (KBSI, Ochang-eup, Korea). APPI sources were purchased from Bruker Daltonics (Billerica, MA). Nitrogen was used as the drying and nebulizing gas. For the saturates, aromatics, and resins fraction, a nebulizing temperature of 350 °C was used with a flow rate of 2.0 L/min. Temperatures as high as 400 °C were required to efficiently ionize the asphaltenes fraction. The drying gas temperature was 210 °C at a flow rate of 2.3 L/min and a spray voltage of 2500−3500 V; the skimmer voltage was set to 15.0 V to minimize in-source fragmentation. Each sample was analyzed 3 times to check reproducibility. The average deviation of relative peak abundance was less than 1% for triplicate samples. A total of 4 × 106 data points were obtained for each spectrum to achieve high resolution and mass accuracy. An approximately 2 s transient was obtained for each scan. The signal-to-noise ratio was enhanced by summing 150 time domain transients. Spectral Interpretation. Spectral interpretation was performed with the Statistical Tool for Organic Mixtures’ Spectra (STORMS 1.0) software with an automated peak-picking algorithm for more reliable and faster results. Accurate m/z values were obtained by running experiments in two steps. In the first step, a tuning mix purchased from Agilent (Santa Clara, CA) was added to the samples and the known m/z values of the tuning mix were used to calibrate subsequent spectra. In the second step, the sample was analyzed without the tuning mix to eliminate possible peak contributions from the calibrant material. The second spectra were calibrated using the peak list obtained in the first step. Elemental formulas were calculated from the calibrated peak list and assigned on the basis of m/z values within a 1 ppm error range. Normal conditions for petroleum data (CcHhNnOoSs, where c is unlimited, h is unlimited, 0 ≤ n ≤ 5, 0 ≤ o ≤ 5, and 0 ≤ s ≤ 4) were used for these calculations. DBEs represent the number of rings plus the number of double bonds in a given molecular formula. DBE values can be calculated by the following equation:
expected characteristics, along with spectra that were quite different from those obtained from unfractionated crude oil. The class, carbon number, and DBE distributions obtained from each fraction agreed with the well-known characteristics of SARA fractions. A synthetic standard compound was used to combine four peak lists of SARA fractions into a master peak list, representing the whole crude oil. This study demonstrates that a more complete understanding of heavy crude oil can be achieved by combining chromatographic techniques, including fractionation, with high-resolution MS.
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MATERIALS AND METHODS
SARA Fractionation of Crude Oil. Organic solvents were purchased from Burdick and Jackson (Muskegon, MI) (ACS/ HPLC). SARA fractionation was performed on 500.0 mg of Arabian heavy oil (ARH), and the sample was mixed with n-heptane (20 mL), stirred for 1 h, and stored in the dark overnight.3,21 The solution was filtered using a Whatman glass filter (GFF, Ø 47 mm; Whatman, Maidstone, Kent, U.K.) and washed with n-heptane until the solvent was colorless. The heptane-insoluble solids (as the asphaltene fraction) were dried under an air stream. The n-heptane solution (maltenes) was dried. The dry maltenes (50 mg) were redissolved in n-heptane and then adsorbed onto the surface of activated alumina (80−200 mesh; Fisher Scientific, Fairlawn, NJ). The maltene alumina slurry was dried during stirring under an air stream. The alumina was baked at 450 °C for 3 h before use. A glass column (22 × 400 mm) was packed with neutral alumina adsorbent (about 4 g), and the adsorbed maltenes were packed on the top. In sequence, 300 mL of n-heptane, 150 mL of toluene, and 100 mL of a toluene/MeOH (8:2, v/v) mixture were used to elute the saturates, aromatics, and resins, respectively. Each fraction was rotary-evaporated until dry and then weighed. The weight percent recovery for each fraction was as follows: saturates, 52.19 ± 2.44%; aromatics, 33.94 ± 3.28%; resins, 16.14 ± 6.14%; and asphaltenes, 9.32 ± 1.82%. The total percent recovery for all fractions was 111.60 ± 4.84%, with residual solvent making the total greater than 100%. Preparation and Application of Standard Compounds. A standard compound was used to combine the four peak lists obtained from SARA fractions into one master list representing the whole crude oil sample. The compound was selected according to the following three criteria: (1) The molecular weight must be between 350 and 500 Da, the same molecular weight range as crude oil compounds commonly detected by FT-ICR MS. (2) The standard must be structurally similar to the heavy components in crude oil, i.e., an aromatic core structure with alkyl chains. (3) The standard compound must be easily synthesized according to previously published procedures. The internal standard compound (ISTD), a in Scheme 1, was synthesized
DBE = c − h/2 + n/2 + 1
(1)
for elemental formulas of CcHhNnOoSs.
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RESULTS AND DISCUSSION Comparison of Mass Defect Distributions. Figure 1 shows broadband and expanded views of mass spectra, each obtained from original crude oil and SARA fractions. The mass defect distribution was quite different between the fractions. The mass defect refers to the difference between the observed and integer mass of the observed value. Molecules with a higher mass defect tend to be richer in hydrogen or saturated. Molecules are likely hydrogen-deficient or aromatic when they have smaller mass defects. The expanded area of the spectra shows that the mass defect was largest for saturates because the peaks were located furthest from the integer mass of 638.0. Accordingly, the mass defect was smallest for the asphaltene fraction. Thus, the saturates fraction is composed primarily of saturated compounds, while the asphaltenes fraction is composed of pericondensed molecules.30 It is unlikely that saturated alicyclic hydrocarbons were detected by APPI. Noncyclic, saturated alkyl compounds generally have higher ionization energies than the photon energy used in APPI (10 eV). It is more likely that compounds containing saturated cyclic rings were detected in the saturates fraction.21 The resins fraction contains both hydrogen-rich and hydrogen-deficient molecules. This suggests that the resins fraction is a mixture of saturated and aromatic compounds. Also, the mass defect distribution of the aromatics fraction was very similar to that obtained from original crude oil. Comparison of Heteroatom Class Distributions. The 13 heteroatom classes observed in each fraction are compared in Figure 2. Each fraction had a distinctive heteroatom species
Scheme 1. Synthesis of 9-Docosyl Carbazole (a) as an ISTD in SARA Fractions
from specific reactants under the conditions shown. Carbazole, tetrabutylammonium iodide (TBAI), benzene, and 1-bromodocosane were purchased from Sigma-Aldrich (St. Louis, MO), and sodium hydroxide was obtained from Merck (Gibbstown, NJ) (assay ≥ 99%). More detailed synthesis conditions were described previously.29 MS. The SARA fractions were dissolved in toluene at 1 mg/mL. Prior to analysis, samples were diluted to 0.5 mg/mL with a solution of toluene and ISTD. The final concentration of ISTD was 0.125 ppm. The prepared samples were directly injected with a syringe pump (Harvard, Holliston, MA) at a flow rate of 500 μL/h for positive-mode 2559
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Figure 1. Broadband and expanded (+) mode APPI FT-ICR MS spectra obtained from (a) unfractionated crude oil and (b) saturates, (c) aromatics, (d) resins, and (e) asphaltenes fractions.
Figure 2. Comparison of the relative abundance of heteroatom classes in unfractionated crude oil and (a) saturates, (b) aromatics, (c) resins, and (d) asphaltenes fractions.
that the spectrum obtained from crude oil is similar to that obtained from the aromatics fraction. This agrees with the previous study, in which class and type distributions of the aromatics fraction and original crude oil, observed by ESI FTICR MS, were very similar.19 More studies, including different types of crude oil, would be needed to demonstrate the applicability of this finding. Comparison of DBE versus Carbon Number Distribution. In all fractions, the S1 class was abundant. Therefore, the S1 class compounds were chosen to compare the DBE versus carbon number distribution. The average DBE and carbon numbers of S1 class compounds, calculated from each fraction,
distribution. For example, the resins fraction exhibited unique distributions, with nitrogen- and oxygen-rich compounds, e.g., N1, N1S1, and N1O1, dominating the heteroatom class distribution. This is unusual because aromatic and sulfurcontaining compounds are typically observed in mass spectra obtained by APPI. This agrees with the resins fraction being primarily composed of polar compounds. The class distribution of the aromatics fraction was similar to those of crude oil. Despite a minor difference in the summed relative abundance distribution, the dominant classes were identical. The class distribution observed in Figure 2, combined with the mass defect comparison discussed in the previous section, suggests 2560
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in this sample. The planar limit slopes were in the order of asphaltenes > aromatics ≈ resins > saturates. This suggests that molecules in the saturates fraction contain multiple saturated cyclic ring structures with longer and/or multiple alkyl chains. Molecules in the asphaltenes fraction have pericondensed structures with shorter and/or less alkyl chains. The resins fraction in the DBE versus carbon number plot could be roughly divided into two compartments. The first compartment is composed of compounds with relatively low carbon number and DBE values (circle A in Figure 3d). The second compartment is similar to those observed in the aromatics fraction (circle B in Figure 3d). S1 class compounds with DBE values of less than 3 were abundant in the resins fraction. The most simple aromatic sulfur compound, thiophene, has a DBE value of 3, and hence, the S1 class compounds with DBE values less than 3 are likely to have nonaromatic structures. Overall, the data presented in Figures 1−3 clearly show that APPI FT-ICR MS can efficiently reveal distinctive characteristics of SARA fractions. Detailed Chemical Information Provided by Combining SARA Fractionation. Each SARA solution, containing the same concentration of ISTD (0.125 ppm), was analyzed by APPI FT-ICR MS. The resulting spectra are shown in Figure 4. The abundance of the standard peak was used to normalize the abundance of other peaks by the following equation:
Table 1. Average Carbon Number and DBE Values of S1 Class Compounds Calculated from Data Shown in Figure 3 average carbon number
average DBE
48.9 53.3 45.7 37.9 44.7
14.8 9.0 15.9 15.9 19.9
unfractionated crude oil saturates aromatics resins asphaltenes
are listed in Table 1. The average DBE and carbon numbers were calculated by the following equations: DBEaverage =
∑i Ii(DBE)i ∑i Ii
average carbon number=
(2)
∑i Ii(#C)i ∑i Ii
(3)
where Ii, (DBE)i, and (#C)i are the relative abundance, DBE value, and carbon number of peak i in the S1 class, respectively. In Table 1, the saturates fraction had the lowest DBEaeverage value of 9.0 and the highest average carbon number of 53.3. The asphaltenes fraction had the highest DBEaverage value of 19.9, and the resins fraction had the lowest carbon number of 37.9. The average carbon number and DBEaverage values of unfractionated crude oil were close to those obtained from the aromatics fraction. To further examine DBE and carbon number distribution, the DBE versus carbon number plots of S1 class compounds observed in each fraction were generated and presented in Figure 3. The saturates fraction mainly contained compounds with DBE values less than 13 and a carbon number of up to 100. In contrast, the asphaltenes fraction had molecules with DBE values up to 40 and carbon numbers of less than 80. It was previously reported that the planar limit slope can be used to interpret and predict the structure of compounds.6,21,26 The same trend was also observed
normalized abundance=
Ii I(standard compound)
(4)
where Ii is the abundance of peak i and I is the abundance of the standard compound. The same sample was analyzed with and without the added ISTD. If a peak was observed at the same m/z as the standard compound before adding the internal standard, e.g., top of panels a and b of Figure 4, the abundance was subtracted to calculate the value originating from the standard compound only. The normalized abundance (eq 4) was used to combine the peaks generated from each fraction
Figure 3. DBE versus carbon number distribution of S1 class compounds observed from (+) mode APPI FT-ICR MS spectra from (a) unfractionated crude oil and (b) saturates, (c) aromatics, (d) resins, and (e) asphaltenes fractions. 2561
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Figure 4. Expanded spectra with and without added internal standard (m/z of ISTD: 475.4172) in the (a) saturates and (b) resins fractions.
Figure 5. Comparison of the (a) total number of compounds and (b) number of compounds in each chemical class identified in unfractionated oil and combined SARA fractions.
The master list was made because SARA fractions generate four peak lists for each crude oil sample. The information provided by each fraction needs to be combined in the master list. The number of peaks identified before and after combining SARA fractionation and APPI FT-ICR MS are compared in Figure 5. The total number of peaks identified was
into the master list. If the same elemental formulas were found in two or more fractions, their normalized abundances were summed. For example, the elemental formula of C43H66N1 was found in both aromatics and resins fractions. In this case, the normalized abundances obtained from the two fractions were added. 2562
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approximately doubled, relative to the number of peaks obtained by direct FT-ICR analysis of unfractionated crude oil, after combining the SARA fractions (refer to Figure 5a). The comparison clearly shows that more detailed information on the crude oil can be obtained by combining APPI FT-ICR MS analysis and SARA separation. The number of peaks identified for each class was also compared after combining APPI FT-ICR MS analysis and SARA separation in Figure 5b. A significant increase in the number of peaks was obtained for all classes. The increase was especially significant for minor components, such as NO, NOS, and N2 classes, that were identified mostly in the resins fraction. The reason for the increased number of minor component peaks can be attributed to matrix effects and/or ionization suppression. When easily ionized compounds are abundant, compounds with relatively low abundance and/or low ionization efficiencies can be suppressed.31 The DBE versus carbon number plots of NO and NOS classes are displayed in Figure 6. The comparison in Figure 6
Figure 7. Comparison of the planar limits of DBE versus carbon number plots for NO and NOS classes. The planar limit slope of the NOS class is steeper than that of the NO class.
compounds was reported to have a slope of 1, and the planar limit of saturates S1 class compounds was reported to have a slope of 0.25.21 Therefore, the planar limit calculated from each class confirms that the NOS compounds have a more condensed structure than NO. However, the comparison in Figures 6 and 7 was only possible with data obtained from SARA-fractionated oil samples.
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CONCLUSION This study demonstrates that SARA fractionation can be successfully combined with APPI FT-ICR MS to obtain detailed chemical information from crude oil samples. First, we showed that SARA fractionation separates crude oil compounds based on their structure and chemical composition and that APPI FT-ICR MS is a very efficient technique to detect the characteristics of each fraction at the molecular level. The class, carbon number, and DBE distributions obtained from each fraction were quite different. In addition, the distribution agreed with what is expected from each SARA fraction. For example, the saturates fraction was composed of less aromatic molecules with long or multiple alkyl chains. However, the asphaltenes fraction was composed of pericondensed molecules. The resins fraction is rich in nitrogen and oxygenated compounds with short carbon chains. The peak lists obtained from each SARA fraction were combined into a master list using a synthesized standard compound. The combined peak list contained approximately 2-fold more peaks than those obtained from unfractionated crude oil. On the basis of the increased information, it is possible to study the crude oil composition in more detail. For example, the number of peaks detected for NO and NOS classes were increased by 4−6 times after combining SARA fractionation. It is possible to compare the DBE versus carbon number distribution of the two classes with the increased number of peaks detected. Overall, this study demonstrates that it is important to combine separation techniques and high-resolution MS for a more detailed chemical analysis of crude oils.
Figure 6. DBE and carbon number of distributions of NO and NOS classes show a major difference in the number of compounds observed (a) without (top) and (b) with (bottom) SARA fractionation.
demonstrates the effectiveness of this approach. The NO and NOS DBE versus carbon number plots (top of panels a and b of Figure 6) obtained from unfractionated crude oil do not contain enough information for comparison. However, the same plots drawn from SARA-fractionated samples have a larger amount of information, which enables us to compare the DBE distribution of NO and NOS class compounds. The NO class DBE distribution starts from 2, and the peaks are mostly populated between DBE values 4 and 12 (bottom of Figure 6a). However, the DBE distribution of NOS compounds was higher. The NOS class DBE distribution starts from 6, and peaks are observed mostly between 10 and 17 (bottom of Figure 6b). Therefore, NOS class compounds have a more condensed structure than those in the NO class. Planar limits are represented by lines generated by connecting maximum DBE values at given carbon numbers.21 Planar limits of NO and NOS compounds are shown in Figure 7. The slopes of the planar limits were calculated by linear regression and stated in Figure 7. Slopes of the NO and NOS planar limits are 0.71 and 0.86. Larger planar limit slopes indicate a more condensed structure. For example, the planar limit of asphaltenes S1 class
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AUTHOR INFORMATION
Corresponding Author
*Telephone: 82-53-950-5333. Fax: 82-53-950-6330. E-mail:
[email protected] (SungHong Kim);
[email protected] (Sunghwan Kim). 2563
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Notes
(15) Draisma, H. H. M.; Reijmers, T. H.; van der Kloet, F.; Bobeldijk-Pastorova, I.; Spies-Faber, E.; Vogels, J. T. W. E.; Meulman, J. J.; Boomsma, D. I.; van der Greef, J.; Hankemeier, T. Equating, or correction for between-block effects with application to body fluid LC−MS and NMR metabolomics data sets. Anal. Chem. 2010, 82 (3), 1039−1046. (16) Juhasz, P.; Lynch, M.; Sethuraman, M.; Campbell, J.; Hines, W.; Paniagua, M.; Song, L.; Kulkarni, M.; Adourian, A.; Guo, Y.; Li, X.; Martin, S.; Gordon, N. Semi-targeted plasma proteomics discovery workflow utilizing two-stage protein depletion and off-line LC− MALDI MS/MS. J. Proteome Res. 2010, 10 (1), 34−45. (17) Michalski, A.; Cox, J.; Mann, M. More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC−MS/MS. J. Proteome Res. 2011, 10 (4), 1785−1793. (18) Michael, G.; Al-Siri, M.; Khan, Z. H.; Ali, F. A. Differences in average chemical structures of asphaltene fractions separated from feed and product oils of a mild thermal processing reaction. Energy Fuels 2005, 19 (4), 1598−1605. (19) Klein, G. C.; Angstrom, A.; Rodgers, R. P.; Marshall, A. G. Use of saturates/aromatics/resins/asphaltenes (SARA) fractionation to determine matrix effects in crude oil analysis by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. Energy Fuels 2006, 20 (2), 668−672. (20) Zhu, X.; Shi, Q.; Zhang, Y.; Pan, N.; Xu, C.; Chung, K. H.; Zhao, S. Characterization of nitrogen compounds in coker heavy gas oil and its subfractions by liquid chromatographic separation followed by Fourier transform ion cyclotron resonance mass spectrometry. Energy Fuels 2011, 25 (1), 281−287. (21) Cho, Y.; Kim, Y. H.; Kim, S. Planar limit-assisted structural interpretation of saturates/aromatics/resins/asphaltenes fractionated crude oil compounds observed by Fourier transform ion cyclotron resonance mass spectrometry. Anal. Chem. 2011, 83 (15), 6068−6073. (22) 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. (23) 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. (24) Yeo, I.; Lee, J. W.; Kim, S. Application of clustering methods for interpretation of petroleum spectra from negative-mode ESI FT-ICR MS. Bull. Korean Chem. Soc. 2010, 31 (11), 3151−3155. (25) Headley, J. V.; Barrow, M. P.; Peru, K. M.; Fahlman, B.; Frank, R. A.; Bickerton, G.; McMaster, M. E.; Parrott, J.; Hewitt, L. M. Preliminary fingerprinting of Athabasca oil sands polar organics in environmental samples using electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. Rapid Commun. Mass Spectrom. 2011, 25 (13), 1899−1909. (26) Hsu, C. S.; Lobodin, V. V.; Rodgers, R. P.; McKenna, A. M.; Marshall, A. G. Compositional boundaries for fossil hydrocarbons. Energy Fuels 2011, 25 (5), 2174−2178. (27) 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. (28) Purcell, J. M.; Hendrickson, C. L.; Rodgers, R. P.; Marshall, A. G. Atmospheric pressure photoionization Fourier transform ion cyclotron resonance mass spectrometry for complex mixture analysis. Anal. Chem. 2006, 78 (16), 5906−5912. (29) Zhao, T.; Liu, Z.; Song, Y.; Xu, W.; Zhang, D.; Zhu, D. Novel diethynylcarbazole macrocycles: Synthesis and optoelectronic properties. J. Org. Chem. 2006, 71 (19), 7422−7432. (30) Borton, D.; Pinkston, D. S.; Hurt, M. R.; Tan, X.; Azyat, K.; Scherer, A.; Tykwinski, R.; Gray, M.; Qian, K.; Kenttämaa, H. I.
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This work was supported by the Korea Institute of Energy Research and Project PM55021 (a Grant-in-Aid from the Ministry of Land, Transport, and Maritime Affairs, Korea). This treatise was also supported by a project of the National Junior Research Fellowship conducted by the National Research Foundation of Korea in 2011.
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