Correlation among Petroleomics Data Obtained with High-Resolution

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Correlation among petroleomics data obtained with highresolution mass spectrometry, elemental and NMR analyses of maltene fractions of atmospheric pressure residues Eunkyoung Kim, Eunji Cho, Chulsoon Moon, Jihyun Ha, Eunsang Cho, Ji-Hyoung Ha, and Sunghwan Kim Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b01047 • Publication Date (Web): 26 Jul 2016 Downloaded from http://pubs.acs.org on July 27, 2016

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Correlation among petroleomics data obtained with high-resolution mass spectrometry, elemental and NMR analyses of maltene fractions of atmospheric pressure residues Eunkyoung Kim1,2, Eunji Cho2, Chulsoon Moon1, Jihyun Ha2, Eunsang Cho1, Ji-Hyoung Ha3*, Sunghwan Kim2,4* 1SK

Innovation Institute of Technology, Daejeon, Republic of Korea

2Kyungpook

National University, Department of Chemistry, Daegu, 702-701 Republic of Korea

3

World Institute of Kimchi, Gwangju, Republic of Korea

4Green-Nano

Materials Research Center, Daegu, 702-701 Republic of Korea

*Corresponding author phone: 82-53-950-5333; fax: 82-53-950-6330; e-mail: [email protected], [email protected], Abstracts In this study, maltenes of atmospheric pressure residue oils were fractionated into five fractions and the fractions were examined by elemental analysis, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), and 1H nuclear magnetic resonance (NMR) spectroscopy. The obtained data were compared to examine the correlations among the data. The correlation coefficients (R2) were 0.88 and 0.89 respectively between the N and S contents determined by elemental analysis and summed relative abundances of N and S containing compounds observed by FT-ICR MS. Especially, correlation between S content and summed relative abundances of S containing compounds was observed. Further, correlation between %1Hnon-aro determined from NMR data and %Cnon-aro obtained from FT-ICR MS data was observed. %1Hnon-aro was calculated from relative summed area of peaks in the non-aromatic region (0.5-4.5 ppm) of the NMR spectra. %Cnon-aro values were calculated from DBE values by assuming linear polyaromatic hydrocarbon structure as the basis for the conversion. The correlation further suggests that FT-ICR MS data can be used to estimate the aromaticity of samples. Overall, the results suggest that the FT-ICR MS data can be used as a quantitative interpretation of samples. However, it is important to note that the quantitative interpretation described in this study is limited to samples having similar mass (or boiling point) distribution and maltene fractions of heavy oils.

Keywords: Preparatory-scale MPLC, mass spectrometry, FT-ICR MS, NMR, Correlations, Elemetal analysis

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Introduction Ultrahigh-resolution mass spectrometry has played an important role in identifying heavy compounds that could not have been otherwise analyzed at the molecular level with conventional analytical techniques.1-3 Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), as a high-resolution mass spectrometry technique, has been applied to study asphaltene, oil shales, crude oils, and processed oils at the molecular level.4,

5

Thanks to the technical advances in

instrumentation and data visualization techniques,3 there have been a number of laboratories utilizing FT-ICR MS to study several kinds of crude oils and their derivatives and fractions.6-19. Ion mobility mass spectrometry (IM-MS) can be also used for gas phase separation and structural interpretation of heavy oil molecules.20-24 Even with the growing number of published papers on this analytical technique, concerns remain over the quantitative nature of FT-ICR MS data. A high degree of precision of petroleomic data obtained with FT-ICR MS has been found. Particularly high precision for the most abundant classes of components was reported.25 However, the ionization process of MS analysis can suffer from discrimination problems. Electro-spray ionization (ESI) and atmospheric pressure photo ionization (APPI) are most widely used ionization techniques for heavy oil analyses.26-29 ESI is an efficient technique for nitrogen and/or oxygen containing compounds but not for sulfur containing or non-hetero atom containing hydrocarbon compounds. APPI is used to analyze medium polar compounds including aromatic hydrocarbons and

2


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sulfur containing compounds. However, both ESI and APPI can’t be used to ionize aliphatic compounds.

Further, the ionization efficiency of compounds can be highly dependent on their chemical structures. In addition, the acquisition parameters (source temperatures, voltages, etc) can significantly change the observed chemical classes. There are many factors affecting the ionization efficiency and the factors have not been fully understood yet. Therefore, up to now, the ionization efficiency of compounds can’t be quantitatively predicted. Due to this limitation, quantitative interpretation of mass spectrometry data is done by comparing sample results to those of standards. However, the use of standards in the analysis of samples containing oils is not practical as there are too many compounds Despite this concern, a few studies showed the use of ultrahigh-resolution mass spectrometry data for quantitative interpretation of crude oil compounds. For example, the statistical analysis of spectra obtained from ten crude oils showed correlation between elemental composition and relative intensity of peaks observed by FT-ICR MS.30 In addition, when chromatographic separation was combined with ultrahigh-resolution mass spectrometry analysis, the combination allowed for the observation of a greater number of compounds.4 Therefore, there have been a number of works dedicated to developing and applying various separation techniques for ultrahigh-resolution mass spectrometry analysis.5, 31-34 In this study, to compare and evaluate the chemical compositions of different heavy oils, six atmospheric pressure residues were fractionated by the previously reported preparatory-scale liquid chromatography method.35 The obtained fractions were analyzed by elemental analysis, FT-ICR MS, and nuclear magnetic resonance (NMR) 3


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spectroscopy and the data obtained were comprehensively compared to identify correlations between the obtained data. As far as we know, this is the first study investigating quantitative correlation between heavy oil data obtained from NMR and FT-ICR MS

Experimental Materials. Six atmospheric residue (AR) oil samples obtained from the bottom of an atmospheric distillation unit for crude oil were used in this study. The AR samples were provided by SK Innovation Co., Ltd. (Daejeon, Korea). Table 1 summarizes their bulk properties, such as API gravity, sulfur content, nitrogen content, and metal content. AR maltene samples were prepared by extraction using hot heptane by the ASTM D3279 method.36 HPLC-grade solvents were purchased from Burdick & Jackon (Honeywell, USA) and used without further purification. Asphaltenes were excluded in this study because ionization efficiency of asphaltenes is very low compared with maltene compounds.

Fractionation of AR maltenes. Samples were separated by use of CombiFlash Rf medium pressure liquid chromatography (MPLC) system purchased from Teledyne Isco Co. (USA). 80g normal-phase silica column purchased from Teledyne was used. About 0.5 g of the AR maltene samples were weighed and dissolved in 3 mL hexane and loaded onto the silica column. HPLC Grade 4 solvents including hexane, toluene, ethyl acetate, and methanol purchased from Honeywell (State of New Jersey, USA) and used as eluting solvents. The detailed solvent program was described in our previous study.35 Briefly explained, hexane for saturate fraction, 5% 4


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toluene and hexane mixture for Aro1 fraction, 30% toluene mixture with hexane for Aro2 fraction, toluene 100% for Polar1 fraction, finally, ethyl acetate and methanol for Polar2 fractions were sequentially eluted. The elution time between eluting solvents was 7 min and the flow rate of 25 mL/min was used. UV detector monitoring (Teledyne Isco CO., USA) between 200 nm and 360 nm was used. The eluted fractions from the column were collected by use of a fraction collector (Teledyne Isco CO., USA).The solvent of each fraction was evaporated by vacuum evaporation and recovered oil fractions were weighed.

Elemental analysis. The CHNS element composition of all maltene fraction samples were analyzed using a Flash EA2000 Series Elemental Analyzer (Thermoscientific) equipped with a thermal conductivity detector (TCD). Samples were weighed in a tin capsule. The sample was loaded on a catalyst column composed of copper oxide and copper to combust the sample. The catalyst column furnace temperature was 950 °C. Helium gas was used as the carrier gas and oxygen gas was introduced for 5 s for complete combustion. The sample was combusted and reduced into CO2, H2O, NH3, and SO2 gases. The combustion gases were separated through a separation packed column and detected in the TCD detector. Reference fuel oil was used for the calibration of C, H, N, and S contents.

Mass spectrometry. The sample fractions were dissolved in toluene at 1000 ppm (1 mg/mL). A volume of 1 µL of sample was dropped on the stainless steel target plate and dried. Laser desorption ionization mass spectra were obtained with Tinkerbell LT MALDI-TOF (ASTA, Suwan, Republic of Korea) equipped with an Nd: YLF UV laser (349 nm, 5 kHz). Each mass spectrum was acquired from 100 shots 5


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at 20 different spots. Laser intensity, detector voltage, and delayed extraction were 56.4%, -2.1 kV, and 500 ns. Subsequent baseline subtraction and Savitzky-Golay smoothing were used for obtained spectra. Bruker Daltonics (Billerica, MA) APPI 7T FT-ICR MS was used for composition analysis of the maltene fractions. Samples were dissolved in toluene at 0.05 wt% and introduced directly using a syringe pump at a flow rate of 350 µL/h. For (+) mode APPI analysis, the nebulizing temperature was set to 380° C with a flow rate of 3.0 L/min, and the drying gas temperature was set to 200 °C with a flow rate of 2.0 L/min. Nitrogen was used as both the drying and nebulizing gas. Ionized samples were accumulated in the collision cell for 0.3 s and transferred to the ion cyclotron resonance (ICR) cell with a 2 ms time-of-flight window. At least 100 scans were accumulated to increase the signal-to-noise ratio. In total, 4 ×106 data points were recorded. The length of time domain transient was about 3.5 s and the resolving power of 420,000 at 400 m/z was achieved. Spectral interpretation was performed using the Statistical Tool for Organic Mixture Spectra software with an automated peak picking algorithm for more reliable and faster results.37, 38 The threshold for peak-picking was a signal-to-noise ratio greater than 5.0. After peak-peaking and internal calibration, molecular formulae were assigned within a 1-ppm error range. Typical conditions for petroleum data (CcHhNnOoSs, c unlimited, h unlimited, 0 ≤ n ≤ 5, 0 ≤ o ≤ 5, 0 ≤ s ≤ 4) were used for these calculations. Double bond equivalence (DBE) values were calculated from the determined elemental compositions by using the following equation:

DBE = c + h/2 + n/2 + 1 (for elemental formula of CcHhNnOoSs) 6


(1)

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NMR spectroscopy. 1H liquid state NMR spectra were recorded on a Bruker Avance III spectrometer operating at 1H Larmor frequency of 600.26 MHz. 1H NMR spectra were obtained using CDCl3 solution as internal standard in a 5 mm tube with a pulse width of 3.3 µs (30º pulse), recycle delay of 10 s, and spectral width of 18 ppm following at least 60 scans. The peaks of 1H-NMR spectra were integrated into two regions: 6.5-9.5 ppm for aromatic protons and 0.5-4.5 ppm for non-aromatic (aliphatic) protons.

Results and Discussion Preparations and basic data for the fractions Six AR maltene samples were fractionated by preparatory-scale MPLC using a previously reported method.35 The total elution time was 50 min, and the elution solvents were collected into five fractions (saturates, Aro 1 and 2, and Polar 1 and 2) for each AR maltene sample. This results in a total of 30 fractions. After the solvent was completely removed, the weight of each fraction was measured. The weight% of fractions for each AR is provided in Table 2. The summed recovery rate ranged between 95-100%. Elemental analysis for C, H, N, and S content was performed for each fraction and the obtained data are presented in Table 3. Nitrogen was mainly distributed in the ARO2 and Polar fractions, and sulfur was distributed mainly in ARO1, ARO2, Polar1, and Polar2 fractions. Sulfur compounds in heavy oils are mainly benzothiophenes and sulfides. Benzothiophene type compounds were separated with aromatic

hydrocarbon

compounds.

Nitrogen

compounds in

petroleum heavy oil are typically composed with basic nitrogen compounds such as 7


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pyridine and aniline derivatives and non-basic nitrogen compounds including carbazole derivatives. Nitrogen compounds are typically abundant in polar fractions.4, 31, 35, 36

Correlation between high-resolution MS and elemental analysis data Each fraction was analyzed by (+) APPI FT-ICR MS and the obtained spectra are presented in the supporting information (Figure S1). The time domain signals were also shown as inserts in Figure S1. The average m/z values observed from (+) APPI FT-ICR MS spectra were calculated and noted in the Figure S1. The m/z distributions were not significantly different from each other. As was reported previously,

low-resolution

mass

spectrometry

is

useful in

evaluating

m/z

distributions.11 To further examine the m/z distribution, selected samples were analyzed by laser desorption ionization time of flight mass spectrometry (LDI-TOF MS) and the data are presented in the supporting information (Figure S2). The m/z distributions observed by LDI-TOF MS are also similar to each other. The data presented in Figure S1 and S2 show that the separation is not based on the molecular weight of compounds. The obtained spectra were further processed and the elemental compositions were calculated as described in the experimental section (Equation 1). The class distributions of the fractions are presented in Figure 1. The numbers used to plot Figure 1 are tabulated and provided in supporting information (Table S1). N1 and N1O1 class compounds were mainly observed in the polar fractions; a result that agrees well with the elemental composition analysis data presented in Table 3. Nitrogen content of Polar fractions was typically between two to five times greater than that of the ARO fractions. 8


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The summed relative abundances (RA) of N containing classes are calculated by Equation 2 and the results are plotted as a function of N content of the samples as presented in Figure 2a.31 The raw data used to plot Figure 2a are provided in the supporting information (Table S2).

Summed abundance of N classes = RA(N1) + RA(N1O1) + RA(N1O2) + RA(N1S1)

(2)

The R2 value of the plot is 0.88 indicating that a correlation exists between nitrogen content and summed abundances of N containing classes. It is important to note that poor correlation among the samples with N content greater than 1.0% was observed (refer to dots in the red circle at Figure 2a). One of the possible reasons for the poor correlation can be attributed to high ionization efficiencies of the nitrogen compounds. The response to nitrogen compounds may be saturated at around 1.0% of nitrogen content.” The RA of S containing classes was summed by Equation 3. In Equation 3, relative abundances of the classes are multiplied by the number of sulfur atoms in a given class. For example, the summed abundances of S2 class are multiplied by 2 because there are two S atoms in the class. The plot of summed abundances of S containing classes as a function of the S content of the samples is presented in Figure 2b. The raw data used to plot Figure 2b are provided in the supporting information (Table S2).

Summed abundance of S classes = RA(S1) + 2 x RA(S2) + RA(N1S1) + RA(O1S1) + 2 x RA(O1S2)

(3)

9


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The R2 value of the plot presented in Figure 2b is 0.89 indicating that a correlation exists between sulfur content and summed abundances of S containing classes. The plots of sulfur content vs summed abundance of S classes for each ARO and Polar fractions were generated and are presented in Figure 2c. The correlations are linear with R2 values ranging between 0.94 and 0.99. In summary, the plots presented in Figure 2 show good correlation between data obtained with the relative abundance analysis obtained by FT-ICR MS and elemental analysis. The data presented in Figure 2 clearly shows that the fractionation by MPLC helps to build good correlation between elemental analysis and FT-ICR MS data.

Correlation between NMR and high-resolution MS data The DBE vs carbon number plots of the FT-ICR MS data obtained from ARO and Polar fractions are presented in Figure 3. The major classes of AR fractions are presented in Figure 3. The average DBE value of each major class observed from the FT-ICR MS data are calculated by Equation 4. In the equation, Ii represents relative abundance of peak i. =

∑ ∑

(4)

Calculated average DBE values were presented in the supporting information (Table S3). In all of the AR fractions, the DBE distribution was increased accordingly: ARO1 < ARO2 < Polar1. The DBE distribution became lower in Polar2 fraction. This trend agrees well with the one reported in the previous studies4, 31, 32, 35. Each fraction of all six AR maltene samples was analyzed by 1H-NMR and the data are presented in Figure 4. The area of peaks designated as aromatic regions (1Haro) was relatively smaller compared to that of non-aromatic regions (1Hnon-aro) and 10


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hence the aromatic regions are expanded and presented as insets (refer to Figure 4).39 The relative percent areas of

1Haro

and

1Hnon-aro

of NMR data (%1Haro

and %1Hnon-aro) are obtained and tabulated in Table 4. These are two of the typical parameters that can be calculated from NMR data.39-41 Similar to the trend observed in the FT-ICR MS analysis (refer to Figure 3), the relative area percentage of the aromatic region was gradually increased from ARO1, to ARO2, to Polar1 fractions but decreased in Polar2 fractions. According to high resolution mass data, while the DBE distributions were increased from ARO1 to Polar1, the carbon distributions became lower and narrower (refer to Figure 3). It means that the nonaromatic carbons in the molecules were decreased and aromaticity was increased from ARO1 to Polar1. It was expected that the value of

1Hnon-aro

were

decreased

from

ARO1

to

Polar1,

but

for

the AR1, AR4 and AR6 samples, the percentages of non-aromatic region H between ARO2 and Polar1 fractions were not decreased. The reason for this observation can be attributed to the fact that those samples have more polycondensed structures. The carbons inside of the polycondensed structures does not have hydrogen atoms directly attached to them and hence the 1Hnon-aro isn’t increased even though the number of carbons in polycondensed structures are increased. DBE values calculated from FT-ICR MS data designate summed number of multiple bonds and rings for a given molecular formula and hence are not directly related to the number of aromatic rings. Therefore, DBE value cannot be directly correlated with 1Haro values presented in Table 4. To estimate the number of aromatic rings from the DBE values, the following assumptions are made: 1. All the compounds with DBE values smaller than the ones of the basic structures are considered non-aromatic. 11


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2. Linear polyaromatic hydrocarbon structure is dominant. Therefore, an increase of 3 in the DBE value is equivalent to addition of an aromatic ring to the basic structure. 3. Increase of 1 and 2 in the DBE value from the aromatic structured obtained from assumption 1 and 2 is not caused by increased number of aromatic ring. 4. The basic structures are tabulated and presented in Table 5.

The assumptions are made based on the reported structures in previous studies 45

42-

and the DBE distributions observed in Figure 3. From the assumptions listed

above and the DBE value of a given elemental formula, the number of aromatic rings and hence the total number of aromatic carbon (#Ctotal) in the elemental formula can be calculated.37-42, 46 The number of non-aromatic (or aliphatic) carbon (#Cnon-aro) can be calculated by subtracting the number of aromatic carbon from the total number of carbon for a given molecular formula. Finally, the percentage of non-aromatic carbon (%Cnon-aro) for each fraction can be calculated by Equation 5.

% = ∑( () × ∑(,

()×# ()!"!#$%" # ()&"&$'

(5)

In Equation 4, RA(i) represents the relative abundance, #(*) the number of non-aromatic carbon, and #(*)++, the total number of carbon in the elemental formula (i) or in a given chemical class (k). RA(k) represents the summed relative abundances of the chemical class (k). %Cnon-aro values calculated from FT-ICR MS spectra of each fraction of AR samples are

listed

in

Table

4.

%Cnon-aro

determined

from

12


FT-ICR

MS

is

plotted

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against %1Hnon-aro value from NMR in Figure 5. %Cnon-aro and %1Hnon-aro obtained from each AR sample is presented in Figure 5a. The plots obtained for each AR sample show strong correlation between %Cnon-aro and %1Hnon-aro. The ones obtained from AR 2, 4, 5, and 6 exhibit especially strong correlation. The plot combining all the points obtained from six AR samples is provided in Figure 5b. Overall, good correlation between %Cnon-aro and %1Hnon-aro was observed and this shows that FTICR MS data can be used to estimate the aromatic nature of samples.

CONCLUSIONS In this study, the correlations among elemental, FT-ICR MS and NMR analyses data were investigated. Good correlation between elemental and FT-ICR MS analyses data was observed. Correlation coefficient of R2 > 0.88 between elemental and FTICR MS analyses data was observed. In particular, correlation between S content determined by elemental analysis and summed relative abundances of sulfur containing compounds determined by FT-ICR MS was observed. Further, the correlation was improved when the number of elements in a given formula was considered. The correlation suggests that FT-ICR MS data can represent quantitative distribution of N or S containing species in an oil sample. Additionally, a correlation observed between %1Hnon-aro determined from NMR data and %Cnon-aro obtained from FT-ICR MS data. %Cnon-aro values were calculated from DBE values following a set of assumptions suggesting that FT-ICR MS data can be used to estimate the aromaticity of samples. In this study, six AR samples prepared with the same chemical processes were compared. Therefore the quantitative estimation from FT-ICR MS data shown in this study has to be limited to cases when samples with similar mass (or boiling 13


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point) distribution are compared. Further study is required to compare samples with different mass (or boiling point) distributions using FT-ICR MS. In addition, it is important to note that samples separated and fractionated by use of MPLC were used in this study. As previously established, separation is helpful in overcoming the limitation of ionization methods and improving quantitative interpretation of mass spectrometry data.4 Most critically, the results presented in this paper suggest that the value of FT-ICRMS data in the quantitative analysis of samples.

Author Information This Corresponding Author Phone:

82-53-950-5333.

Fax:

82-53-950-6330.

E-mail:

[email protected],

[email protected].

Acknowledgments This work was supported by the Ministry of Knowledge Economy (MKE, Korea) and by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korean

government

(MEST)

(2015R1A2A1A15055585

2014R1A2A1A11049946).

14


and

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Table Captions Table 1. Macro properties of AR samples used in this study

API Sp.Gr. S, wt% N, mg/kg Ni, mg/kg V, mg/kg Asphaltene, wt%

AR1 9.8 1.002 4.68 2528 33.5 103.9 10.2

AR2 20.4 0.932 1.76 1240 5.9 8.0 1.3

AR3 16.1 0.959 0.47 3901 46.3 6.1 1.5

AR4 22.5 0.919 0.32 1630 15.8 3.7 6.6

AR5 11 0.993 5.51 1960 25.6 92.9 7.2

AR6 21.5 0.924 0.38 1630 8.9 2.5 2.8

Table 2. Weight % distribution of fractions obtained by the preparatory fractionation method. AR1

AR2

AR3

AR4

AR5

AR6

Saturate

23.2

46.1

22.8

42.3

25.5

52.1

ARO1

27.8

15.1

24.5

28.8

22.8

15.0

ARO2

27.9

20.8

18.9

10.5

31.8

12.9

Polar1

11.1

5.5

13.1

6.1

11.9

8.3

Polar2

9.6

7.1

17.3

7.3

9.8

7.2

recovery, %

99.6

94.6

96.6

95.0

101.8

95.5

Table 3. Elemental analysis data of fractions of six AR samples AR1 Saturate ARO1 ARO2 Polar1 Polar2

C 85.9 83.5 82.8 82.4 81.2

H 14.4 11.6 9.8 9.2 9.7

S

Correlation among Petroleomics Data Obtained with High-Resolution

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