Application Note FTMS-55 Laser/Desorption Ionization FT-ICR Mass Spectrometry as a Tool for Statistical Analysis of Crude Oils

Transcription

1 Application Note FTMS-55 Laser/Desorption Ionization FT-ICR Mass Spectrometry as a Tool for Statistical Analysis of Crude Oils Introduction Crude oil is an extremely complex mixture of hydrocarbons and heteroatom-containing chemicals such as thiophenes, phenols, pyrrols, and pyridines. The composition can vary widely, depending on the origin of the oil and how the petroleum was formed. The maturity of each crude oil has a huge influence on its molecular composition. Each raw petroleum or crude oil has a characteristic composition and properties. Therefore, chemical analysis can be used to fingerprint the source of the petroleum. Highboiling point hydrocarbons and highly polar compounds cannot be characterized by GC/MS analysis. However, these compounds can be analyzed at the molecular level by Fourier Transform Ion Cyclotron Resonance (FT-ICR) mass spectrometry, which is a well-established method in petroleomics [1-3]. On the basis of the detected compounds, the relative abundance of different compound classes can be calculated and compared between different crude oils. The chemical composition of crude oil can also be correlated with the chemical properties of the oil. Such analyses complement characterization using standard crude oil parameters such as API gravity, TAN acidity, sulfur and nitrogen content. Authors Matthias Witt Bruker Daltonik GmbH, Bremen, Germany Kolbjørn Zahlsen, Anders Brunsvik SINTEF Materials and Chemistry, Trondheim, Norway Keywords Instrumentation and Software Petroleomics solarix XR, 12 T FTMS ProfileAnalysis 2.1 LDI MTP II MALDI source Composer 1.0.6

2 It should be noted that the abundance of the detected compound classes depends heavily on the ionization method used. Therefore, crude oils can be compared by FT-ICR mass spectrometry, but this method is only semiquantitative [4]. The types of compound classes that are detected are also influenced by the presence of salts and additives used to provide better ionization efficiencies. In this study, laser desorption/ionization (LDI) was coupled to a Fourier Transform Ion Cyclotron Resonance (FT-ICR) mass spectrometer for the analysis of crude oil [5]. A thin film of crude oil was applied onto a stainless steel target and analyzed by LDI using a UV laser at 355 nm. No matrix was added to the sample for these measurements. Nine different crude oils were analyzed and compared at the molecular level. The relative abundances of compound classes with different numbers of heteroatoms were compared. In some cases, the relative abundances of the compounds classes were quite low. Therefore, high reproducibility of the LDI measurements is a prerequisite for reliable characterization of crude oils. Each crude oil sample was measured several times to calculate the standard deviation of the detected compound classes. Additionally, the results were analyzed using the statistical method principle component analysis (PCA). The results showed that the oils were well-separated in the PCA scoring plot, with low variation between the replicate measurements. The statistical analysis also indicated that extremely high resolution mass spectra are required to distinguish between oils. Therefore, LDI coupled to FT-ICR mass spectrometry is a perfect tool for distinguishing between crude oils using statistical methods such as PCA. In addition, LDI is independent of solvent effects observed in atmospheric pressure ionization methods such as ESI or APPI, and sample preparation is extremely simple. Experimental Sample preparation: Samples were kindly provided by SINTEF, Department of Materials and Chemistry, Trondheim, Norway. The crude oil samples were measured by LDI without the addition of matrix and without any purification. Crude oil (10 mg) was dissolved in 990 μl toluene and 1 μl of this solution was spotted onto a stainless steel target and dried for 15 min at room temperature. The samples were placed in the source chamber for 30 minutes at around 3 mbar before mass analysis by laser/desorption ionization. Mass analysis: Mass spectra were acquired using a Bruker solarix XR Fourier transform ion cyclotron resonance mass spectrometer (Bruker Daltonik GmbH, Bremen, Germany) equipped with a 12 T refrigerated actively-shielded superconducting magnet (Bruker Biospin, Wissembourg, France) and the new dynamically harmonized analyzer cell (ParaCell ). The samples were ionized in positive ion mode using the MALDI ion source (Bruker Daltonik GmbH, Bremen, Germany). The mass range was set to m/z using 4M data points with a transient length of 1.6 s resulting in a resolving power of 450,000 at m/z 400 in magnitude mode. One hundred single scans were averaged for a final mass spectrum. The laser power was lowered to 12% to reduce fragmentation during the ionization process. The laser focus was set to a diameter of 30 μm and 4 laser shots were used for each scan. Five replicate measurements were performed for each sample for meaningful statistical analysis. Spectra were zero-filled to processing size of 8M data points before sine apodization. Mass calibration: The mass spectra were calibrated externally using arginine clusters in electrospray ionization positive ion mode. A linear calibration was applied. A 10 μg ml -1 solution of arginine in 50% methanol was used to generate the clusters. To improve the mass accuracy, the crude oil spectra were recalibrated internally with the N 1 series. The RMS mass error of the recalibration of all acquired spectra was better than 150 ppb using a linear calibration. Molecular formula calculation: The molecular formula calculation was performed in Composer (Sierra Analytics, Modesto, CA, USA) using a maximum formula of C n H h N 3 O 3 S 3. Due to the formation and detection of radical cations and protonated species, the electron configuration was set to odd and even, and a mass tolerance of 500 ppb was used. The relative abundances of the compound classes were calculated with the Composer software. The statistical analysis was performed with the software ProfileAnalysis 2.1 (Bruker Daltonik GmbH, Bremen, Germany). Results and Discussion The mass spectra of three different crude oil samples measured in LDI positive ion mode are shown in Figure 1. The mass distribution of the samples is very similar, with an average mass of around m/z 300. Masses of up to m/z 800 were detected. The average number of carbons in compounds detected by LDI can be estimated by mass distribution. Therefore, LDI-TOF-MS can be used as a general tool to measure the average mass and the mass distribution analysis of crude oil, lubricants, bitumen, and asphaltenes. However, ultrahigh-resolution mass spectrometry is required to analyze these samples at the molecular level. Only ultra-highresolution FT-ICR is capable of resolving all mass peaks in the extremely complex mass spectrum, thereby enabling calculation the correct molecular formula of each peak based on highly accurate mass measurements (Figure 2).

3 Mass spectra of three different crude oil samples Figure 1: Mass spectra of three different crude oil samples measured in LDI positive ion mode. Magnified view of crude oil mass spectra Figure 2: Magnified view of mass spectra of crude oils 2, 3, and 4 at m/z 301. Significant differences are only apparent using ultrahigh resolution.

4 The molecular formulas of mass peaks were calculated with a mass tolerance of 500 ppb. Using a maximum formula of C n H h N 3 O 3 S 3, single-candidate molecular formulas were obtained in the mass range up to m/z 500. Higher molecular formulas were obtained by extending the homologous series obtained in the lower mass region. This calculation was performed using the Composer software. The mass spectra showed significant differences in ion abundance (Figure 2) resulting in different relative abundances of compound classes (Figure 3). Oil 8 and oil 8a are identical oils from different batches. Oil 9a corresponds to a sample of oil 9 that was stored in the source vacuum chamber for one day at 3 mbar, which resulted in the evaporation of volatile compounds. Distinguishing between crude oils on the basis of compound class abundances alone only provides approximate and sometimes even inaccurate results. Therefore, our approach for distinguishing between crude oils was performed at the molecular level using all mass peaks detected by LDI-FT-ICR mass spectrometry. The mass spectra of the different oil samples were first analyzed on the basis of relative abundance of compound classes (Figure 3) for an approximate characterization of the crude oils. For a more precise characterization, the mass spectra of the crude oils were analyzed using the statistical method principle component analysis (PCA). To obtain statistically meaningful results, five replicate measurements were performed on each sample. The results of the PCA calculated using the average mass list show that the oils are well-separated in the PCA scoring plot, with low variation between the replicate measurements (Figure 4). Relative abundances of compound classes in crude oil samples Figure 3: Three-dimensional column diagram showing relative abundances of compound classes detected as protonated species and radical cations by LDI-FT-ICR mass spectrometry. Class of protonated species is marked as [H]. PCA scoring plots of LDI-FT-ICR mass spectra using average Figure 4: PCA scoring plots of LDI-FT-ICR mass spectra of all oil samples using average mass lists with a) logarithmic scaling and b) linear scaling. Oil 8 and oil 8a are identical oils from different batches. Oil 9a corresponds to a sample of oil 9 that was stored in the source vacuum chamber for one day at 3 mbar.

5 Improved separation of the oils in the PCA scoring plot was observed using logarithmic scaling (Figure 4a) instead of linear scaling (Figure 4b). However, statistical analysis indicated that extremely high resolution mass spectra were required for efficient separation of the oils by PCA (Figure 5). Using bucketing with a mass resolution of 2 mda for PCA calculation instead of average mass lists resulted in inefficient separation of crude oils (Figure 5a). No separation of the crude oils was observed with low mass resolution of 20 mda bucketing (Figure 5b). A bucketing mass resolution of 20 mda corresponds to a mass spectrum with resolving power of only 20,000, as typically obtained using a MALDI-TOF instrument. Therefore, the ultra-high resolution provided by FT-ICR MS is required to distinguish between the crude oils using LDI and PCA. The high reproducibility of LDI mass spectra was demonstrated by the standard deviation of the relative abundances of the compound classes (Table 1). Low relative standard deviations of less than 4% were observed for highly abundant compound classes such as hydrocarbons or N 1. Relatively low standard deviations (< 10%) were observed for very low-abundance classes. Therefore, LDI-FT-ICR mass measurements are highly reproducible, a factor that is crucial for the efficient characterization of crude oil using this technique. PCA scoring plots of LDI-FT-ICR mass spectra using mass bucketing Figure 5: PCA scoring plots of LDI-FT-ICR mass spectra of all oil samples using logarithmic scaling and a) 2 mda or b) 20 mda mass bucketing. Oil 8 and oil 8a are identical oils from different batches. Oil 9a corresponds to a sample of oil 9 that was stored in the source vacuum chamber for one day at 3 mbar. Table 1: Relative abundances of compound classes found in five replicate measurements of oil 8. LDI-FT-ICR mass spectra Class Oil 8 rep1 Oil 8 rep2 Oil 8 rep3 Oil 8 rep4 Oil 8 rep5 Std. deviation [%] HC HC [H] N N [H] N N 2 [H] NO NO [H] O O [H] O O 2 [H] OS [H] S

6 Bruker Daltonics is continually improving its products and reserves the right to change specifications without notice. Bruker Daltonics , FTMS-55, Conclusion Crude oil samples can be distinguished at the molecular level by LDI ultra-high resolution mass spectrometry. LDI- FT-ICR mass spectrometry is capable of classifying crude oils by principle component analysis (PCA). LDI-FT-ICR measurements provide the reproducibility required for efficient PCA characterization. Crude oils are best separated in PCA scoring plots using average mass lists and logarithmic scaling. References [1] Rodgers, R. P.; Schaub, T. M.; Marshall, A. G. Anal. Chem. 2005, 21A. [2] Marshall, A. G.; Rodgers, R. P. Acc. Chem. Res. 2004, 37, [3] Cho, Y.; Ahmed, A.; Islam, A., Kim, S. Mass Spectrom. Rev. 2014, doi: /mas [4] Witt, M. Comparison of LDI and APPI for correlation studies between physical and chemical properties of crude oil and FT-ICR mass spectra, Poster at Petrophase [5] Cho, Y.; Witt M.; Kim, Y. H.; Kim S. Energy & Fuels 2012, 84, For research use only. Not for use in diagnostic procedures. Bruker Daltonik GmbH Bremen Germany Phone +49 (0) Fax +49 (0) Bruker Daltonics Inc. Billerica, MA USA Phone +1 (978) Fax +1 (978) Fremont, CA USA Phone +1 (510) Fax +1 (510)