Exploring the Molecular Origin of Jet Fuel Thermal Oxidative Instability

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Exploring the Molecular Origin of Jet Fuel Thermal Oxidative Instability Through Statistical Analysis of Mass Spectral Data Krege M Christison, Robert M Lorenz, Liang Xue, and O. David Sparkman Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.8b03670 • Publication Date (Web): 09 Jan 2019 Downloaded from http://pubs.acs.org on January 12, 2019

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Energy & Fuels

Exploring the Molecular Origin of Jet Fuel Thermal Oxidative Instability Through Statistical Analysis of Mass Spectral Data Krege M. Christison1,2*, Robert M. Lorenz1, Liang Xue2, O. David Sparkman2 1Fuels

Technology and Additives, Chevron Energy Technology Company, 100 Chevron Way, Richmond, CA 94801, United States 2University

of the Pacific, 3601 Pacific Ave, Stockton, CA 95211, United States *Email: [email protected]

Abstract Statistical analysis of electrospray ionization (ESI) high-resolution accurate-mass (HRMS) liquid chromatography/mass spectrometry (LC/MS) data offers a method to study the molecular origin of jet fuel thermal oxidative stability issues as measured by ASTM D3241 (Standard Test Method for Thermal Oxidation Stability of Aviation Turbine Fuels). It is important to understand this type of data processing and how to appropriately use it as a data analysis technique. In this work, Mass Profiler Professional (MPP) is used to analyze ESI HRMS LC/MS data of jet fuels. The data are combined with ASTM D3241 results for the jet fuels studied offering insight into the identities of molecules that correlate with poor results. These data offer support for the Soluble Macromolecular Oxidatively Reactive Species (SMORS) model for distillate fuel instability. Introduction Statistical analysis of mass spectral data can help elucidate differences between samples by investigating variations of ion abundance across a sample set leading to mechanistic insights for complex chemical processes. Mass Profiler Professional (Version 14.9 2017, Agilent Technologies, Santa Clara, CA) is a commercially available software platform with built-in statistical tools designed for these types of data analysis. There are numerous examples that demonstrate the utility of this data analysis platform for differential analysis of samples, many of which are in the field of metabolomics.1-3 This type of data analysis can be performed on any type of mass spectral data, however, it is particularly useful for determining the similarities and differences between samples utilizing high-resolution accurate-mass data.4 Jet fuel is a petroleum-derived product defined by a number of different specifications, but mainly the ASTM D1655 standard (Standard Specification for Aviation Turbine Fuels) in the

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United States and the Defence Standard 91-91 in many other countries.5-6 It is composed primarily of alkane, cycloalkane, and aromatic molecules7 with a boiling range of approximately 120-300 °C.7 An important property of jet fuel is its thermal oxidative stability as measured by ASTM D3241.8 ASTM D3241 measures the deposit formation tendency of an aviation turbine (jet) fuel under high temperature and pressure conditions which correlates to the thermal oxidative stability of the fuel. A failure is signified by either a pressure drop across a filter in the test apparatus or excessive deposit formation on a heated metal tube. A failure due to pressure drop across the filter signifies the formation of insoluble particles that do not adhere to the metal tube and instead plug a filter in the test apparatus. Understanding the structure and composition of the deposits could lead to better understanding of their formation.9 This is a critical property of jet fuel because fuel is exposed to high temperature and pressure conditions when in-service and deposit formations during flight may lead to catastrophic failure. Trace amounts of polar molecules containing heteroatoms have been shown to lead to ASTM D3241 failures.10-12 However, a simple correlation based on the concentrations of these molecules in a fuel have failed to predict ASTM D3241 failures. It has also been shown that trace amounts of dissolved metals can lead to thermal oxidative instability in jet fuel.13 The Soluble Macromolecular Oxidatively Reactive Species (SMORS) model for distillate fuel instability was first proposed to explain storage stability issues in diesel fuel,14 and has been applied to help understand thermal oxidative instability in jet fuels (Figure 1).12, 15 The first step is the oxidation of phenol or an alkyl phenol which acts as an antioxidant in the autoxidation pathway (Figure 2).15-16 This oxidation leads to the formation of hydroquinone and quinone.15 The electrophilic quinone then reacts with an electron-rich aromatic nitrogen heterocycle, such as indole or carbazole (or their alkyl derivatives), through electrophilic aromatic substitution (EAS) leaving a hydroquinone moiety on the product.15 The product of this reaction can then undergo oxidation to reform the quinone moiety allowing for dimerization through EAS.15 This dimer reaction product is thought to be the largest soluble compound that can form via this pathway based on size and polarity.15 From an analytical perspective, it would be advantageous to directly measure and correlate the quantities of phenol, indole, carbazole, and other possible precursors to SMORS in jet fuel with the fuel’s thermal oxidative stability. LC/MS offers a way to measure the relative amounts of these molecules in different fuels10, 17 and statistical analysis of the resulting data offers a methodology to correlate these data with the thermal oxidative stability of the fuels. To explore these methodologies, a set of six unstressed Jet A fuels and one unstressed No. 1-D S15 diesel


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fuel, which is an ultra-low sulfur kerosene-range fuel defined by the ASTM D975 Specification (Standard Specification for Diesel Fuel Oils),18 all produced in the United States, were analyzed by LC/MS with positive ion ESI and also by ASTM D3241. The data were then processed in Mass Profiler Professional to explore correlations between the components in the fuels and their thermal oxidative stabilities as measured by ASTM D3241. One of the drawbacks of the ASTM D3241 test method is that the precision of the methodology cannot be determined.5 Also, when the same fuel is analyzed on different models of the jet fuel thermal oxidation tester, the results of the test can be different.19 There is an on-going debate in the industry around which model of instrument gives the “correct” result.19 Additionally, there is an effort within a working group of the Coordinating Research Council to develop a reference fluid, with a known ASTM D3241 result, to help calibrate different models of instruments to achieve uniform results. From the perspective of using ASTM D3241 test results for differential analysis of mass spectral data, this variability could be an issue because a correlation is being made between a methodology of unknown precision (ASTM D3241) and a precise method (LC/MS). However, for this study a narrow set of samples was chosen with ASTM D3241 results at the extremes. The passing samples all had results of 4P. This adds confidence that the passing fuels have good thermal oxidative stability, the failing sample has poor thermal oxidative stability, and that meaningful differential analysis can be achieved. Experimental Fuels Table 1 shows the identification numbers, descriptions, and some key properties for the fuels used in this study. All fuels could be used as standalone jet fuels or jet fuel blending components with the exception of J2971 which does not meet the ASTM D3241 thermal oxidative stability requirements. LC/MS Analysis Samples were analyzed on an Agilent 1290 UHPLC coupled to an Agilent 6230 TOF MS with a dual ESI source. 1 μL aliquots of samples diluted 1:1 with isopropyl alcohol were injected onto an Agilent RRHD 2.1 x 50 mm C8 column with 1.8 μm particles. The mobile phase was run as a gradient from 9:1 water:methanol with 0.1 vol % formic acid to 9:1 methanol:water with 0.1 vol


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% formic acid at a 0.300 mL/min flow rate. The initial solvent was held for 1 minute and then ramped to the final solvent over 10 minutes. The final solvent was held for 5 minutes. The mass spectrometer was operated in positive ion mode. The ion source had a drying gas temperature of 350 °C and a nebulizer pressure of 50 psi. Data were collected over the m/z-range of 70 – 3600. The capillary voltage was set to 3,000 V. The fragmentor voltage was 120 V. Data were collected at 4 GHz in high-resolution mode. ESI-L tuning mix (Agilent Technologies, Santa Clara, CA) was introduced through the second ESI nebulizer to allow for real-time mass accuracy calibration during the analysis. All samples were run in triplicate in a randomly generated order, thereby minimizing the same sample being analyzed sequentially. Analyzing the samples in triplicate reinforces confidence in the data during statistical analysis. At the beginning of the sequence, three blank runs with no injection were performed to equilibrate the column to the solvent system. One of the samples, J2990, was chosen to be used as a quality control (QC) sample. The QC sample was run 3 times after the blank runs and then again after every 3rd sample. This allowed for the determination of drift in retention times and reproducibility of the data across the sequence. Data Processing Data were pre-processed by batch molecular feature extraction with recursive analysis in MPP. The molecular feature extraction algorithm identified ions in the data that exhibited chromatographic behavior. An ion that exhibits a normal distribution of intensity over time has chromatographic behavior, it rises and falls like a chromatographic peak. The MPP recursive analysis then took the list of ions found across the entire sample set and performed a find-by-ion algorithm which allowed for the detection of ions that may have been missed during the molecular feature extraction. This resulted in an entity list of ions at particular retention times defined by their chromatographic peak maxima. The entities were aligned by saving them with their average m/z and retention time values across the data set. The pre-processed data were then imported to MPP (Figure 3). When data are imported into MPP the raw abundance values are processed by a Log2 Transformation. This is intended to make the data more normally distributed which helps meet the assumptions of most statistical tests. The entities were normalized by a 75-percentile shift. This normalization is done within each sample where the Log2 intensity of the entity at the 75th percentile is subtracted from the Log2 intensity of itself and every other entity in the sample. It is assumed that the total intensity


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value from each sample is approximately the same and helps to normalize small differences in the samples due to experimental factors. Data were baselined to the median intensity values for the ions across all samples. The samples were grouped according to their ASTM D3241 results (as seen across the X-axis label). Entities were initially filtered by frequency (Supplemental Figure 1). For the entity to remain in the list it must have been present in 100% of samples in at least 1 group. The entities were then filtered on variability (Supplemental Figure 2). For an entity to remain in the list it had to have a coefficient of variation that was ≥ 25%. This coefficient of variation helped to remove some of the entities that did not show variability without erroneously removing too many. An analysis of variance (ANOVA) with p ≤ 0.01 was performed on the resulting entity list (Supplemental Figure 3). Finally, a filter by fold change was performed on the resulting entity list looking for entities that increased by ≥ 16-fold in the fuel that failed ASTM D3241 (Figure 4). The maximum allowable fold change filter in the software is 16-fold and this allowed for the maximum reduction in entities across the data set. A principal component analysis (PCA) was performed with MPP to visualize the sample groupings after all the data processing (Figures 5 and 6). The resulting entity list was then processed in the ID browser which allowed for elemental compositions to be generated and searched against a proprietary database. Results and Discussion One of the disadvantages of ESI is that there can be ion suppression and large ionization efficiency differences between compounds.20 This can lead to bias in interpretation of LC/MS data causing the analyst to see a large response for a compound and identifying that compound as being significant. Baselining the relative intensities for each entity to their median value puts all entities on the same scale regardless of their absolute intensity. Visually, this can help to minimize bias when looking at charts of the entities together. The multiple data filtering steps and statistical analyses performed on the LC/MS data of the jet fuel samples reduced the number of entities from 1669 to 284. The 284 entities that remained showed statistical significance when examining the differences in samples that passed and failed ASTM D3241. Performing a PCA on these entities shows that the samples cluster along ASTM D3241 results and the technical replicates also cluster with themselves (Figure 5) after the data processing steps. The 3-D PCA results distinctly show that most of the variation occurs along the first 2 principal components (Figure 6). J2971 sits outside of the 95% confidence percentage of the T2 Hotelling Ellipse which suggests that there is a high level of confidence that the variance between this sample and all the others is significant.


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Generating elemental compositions for the entities in the final entity list and searching against a library of compounds created in the Personal Compound Database Library (PCDL) (Version B.07.00 2017, Agilent Technologies, Santa Clara, CA) built using the described LC/MS method provides some insights. A list of detected compounds and generated elemental compositions is available in the supplemental information (Supplemental Table 1). Based on the elemental compositions and library matches from searching the PCDL, homologous series consistent with expected compounds are detected. These homologous series have elemental compositions consistent with alkyl phenol, alkyl indole, and alkyl indoline; an elemental composition consistent with tetrahydrocarbazole was also detected. Homologous series consistent with other nitrogen containing species, alkyl aniline and alkyl quinoline, were also detected. It has been shown that aniline and quinoline compounds don’t correlate strongly with thermal oxidative stability issues in jet fuels.10 There are also groups of compounds with general elemental compositions of CnHyOS, CnHyNO, CnHyNOS, and CnHyNO2 that are not necessarily homologous series. It is important to note that there is only one sample in the data set that fails ASTM D3241, hence any entity that is significantly different in this sample compared to the other 6 samples will also appear to be significant with regard to the ASTM D3241 failure; therefore, not all of these identified entities are necessarily related to the ASTM D3241 failure. However, as has been discussed previously in this paper and in the literature, phenol, indole, and carbazole homologues have been implicated in causing thermal oxidative stability issues in jet fuels. The compounds with the general elemental composition of CnHyNO2 are notable because they fit the elemental composition of the first EAS reaction products in the SMORS model. For example, the raw abundance plot for 2,5-dimethylphenol (Figure 7) illustrates it is present at higher levels in J2971 which failed ASTM D3241. This is also the case for 2,3,7-trimethlyindole (Figure 8). The profile plot for C19H21NO2 (Figure 9) makes it apparent that the compound with this elemental composition has similar behavior across the data set. Although there is no structural information available by the soft ionization ESI LC/MS method employed here, it is reasonable to believe that C19H21NO2 could be the product of an EAS reaction between a quinone formed from 2,5-dimethylphenol and 2,3,7-trimethylindole (Figure 10). A similar exercise can be performed with C13H19N (consistent with a C5 alkyl indoline, where C5 denotes 5 saturated carbons attached to the indoline) (Figure 11) and C24H33NO2 (Figure 12) leading to the hypothetical structure of a SMORS intermediate (Figure 13). However, there were no C5 alkylphenol molecules detected. Therefore, the support for this SMORS intermediate is not as compelling as for the previous one. Furthermore, it would be reasonable to expect that if these


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alkyl indole and alkyl indoline compounds react to form intermediates, then the other alkyl indole and alkyl indoline compounds that were detected would react as well, but the products that would result from those reactions are not detected. Their absence could be due to differing response factors leading to some of the compounds being below their respective detection limits. It is unclear whether the compounds with the general elemental compositions of CnHyOS, CnHyNO, and CnHyNOS are related to the poor thermal oxidative stability of J2971. The general elemental composition of CnHyOS could be related to hydroxybenzenethiols which have been discussed as possible reactants with quinones,12 yet none of the detected elemental compositions fit this compound class. This general elemental composition could also fit a sulfur containing analog of quinone. In fact, one of the detected elemental compositions C11H14OS would fit the structure of a C5 alkyl monothioparabenzoquinone. It is plausible that this type of molecule could fit as a reactant in the SMORS model replacing quinone, but without structural information this is purely hypothetical. MS/MS experiments could offer insight into the structures of these compounds by providing fragmentation. Synthesis of SMORS intermediates has been explored previously and could offer a way to determine the retention behavior of these compounds by this LC/MS method and add them to the library.12 Increasing the number of passing and failing samples could help to improve the understanding of whether or not CnHyOS, CnHyNO, and CnHyNOS truly have a statistically significant correlation to thermal oxidative stability. By introducing more samples, there could be some samples that pass ASTM D3241 and have high levels of one or more of these groups of compounds, whereas some samples that fail ASTM D3241 do not. If this is the case, then the statistical analysis of the data set might show them to not be correlated with ASTM D3241 failures. However, by introducing more failing samples that contain high levels of one or more of these groups of compounds whereas none of the passing samples do then this will provide supporting evidence that they might be relevant to thermal oxidative stability. Fundamentally, by increasing the number of passing and failing samples the statistical power of the experiment will increase, leading to more certain conclusions.21 Summary and Conclusions Thermal oxidative stability of jet fuels as measured by ASTM D3241 is an important property due to the operational conditions in jet engines. In this study, MPP has shown itself to be a useful tool for the correlation of LC/MS ESI data from jet fuels with their thermal oxidative


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stability. The results here lend support to the SMORS model for the formation of thermal oxidative deposits by showing that elevated levels of phenol, indole, indoline and tetrahydrocarbazole as well as increases of compounds with elemental compositions that fit as intermediates in the SMORS model reaction pathway correlate to fuels that fail ASTM D3241. In the jet fuel that failed ASTM D3241, there were also some species that were significantly higher in concentration having elemental compositions that were not directly attributable to the SMORS model as described in figure 1. These had general elemental compositions of CnHyOS, CnHyNO, and CnHyNOS. It is possible to rationalize that CnHyOS could be monothioparabenzoquinones which could take the place of quinone in the SMORS reaction. It is also possible to rationalize that CnHyNO could be from the formation of an imine due to the reaction of a quinone with an aniline. However, there is no other evidence detected for this occurring. By increasing the number of ASTM D3241 passing and failing samples the statistical power of the experiment would increase and more certainty in the results could be achieved. Therefore, more samples will be analyzed in the future and the results will be reported in due course. Ultimately, this work could benefit from MS/MS studies and synthesis of standards to help validate some of these results.


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References 1. Gu, Q.; David, F.; Lynen, F.; Rumpel, K.; Dugardeyn, J.; Van, D. S. D.; Xu, G.; Sandra, P., Evaluation of automated sample preparation, retention time locked gas chromatography-mass spectrometry and data analysis methods for the metabolomic study of Arabidopsis species. J Chromatogr A 2011, 1218 (21), 3247-54. 2. Hu, J.-b.; Jiang, F.-s.; Gu, H.-c.; Ding, Z.-s.; Yao, L.; Fan, Y.-s.; Ding, X.-h., Metabolomics Study on the Effects of Jieduquyuziyin Prescription on Systemic Lupus Erythematosus Mice by LC-Q-TOF/MS. Chromatographia 2013, 76 (13-14), 791-800. 3. Telu, K. H.; Yan, X.; Wallace, W. E.; Stein, S. E.; Simon-Manso, Y., Analysis of human plasma metabolites across different liquid chromatography/mass spectrometry platforms: Cross-platform transferable chemical signatures. Rapid Commun. Mass Spectrom. 2016, 30 (5), 581-593. 4. Avula, B.; Smillie, T. J.; Wang, Y.-H.; Zweigenbaum, J.; Khan, I. A., Authentication of true cinnamon (Cinnamon verum) utilising direct analysis in real time (DART)-QToF-MS. Food Addit. Contam., Part A 2015, 32 (1), 1-8. 5. ASTM International., ASTM D1655-18, Standard Specification for Aviation Turbine Fuels. West Conshohocken, PA, 2018. 6. Defence Standard 91-91. Turbine Fuel, Aviation Kerosene Type, Jet A-1 NATO Code: F-35 Joint Service Designation: AVTUR (18 2011) 2015. 7. Hemighaus, G.; Boval, T.; Bacha, J.; Barnes, F.; Franklin, M.; Gibbs, L.; Hogue, N.; Jones, J.; Lesnini, D.; Lind, J., Aviation fuels technical review. Chevron Corporation 2006. 8. ASTM International, ASTM D3241-18, Standard Test Method for Thermal Oxidation Stability of Aviation Turbine Fuels. West Conshohocken, PA, 2018. 9. Altin, O.; Eser, S., Analysis of Solid Deposits from Thermal Stressing of a JP-8 Fuel on Different Tube Surfaces in a Flow Reactor. Industrial & Engineering Chemistry Research 2001, 40 (2), 596-603. 10. Balster, L. M.; Zabarnick, S.; Striebich, R. C.; Shafer, L. M.; West, Z. J., Analysis of Polar Species in Jet Fuel and Determination of Their Role in Autoxidative Deposit Formation. Energy & Fuels 2006, 20 (6), 2564-2571. 11. Sobkowiak, M.; Griffith, J. M.; Wang, B.; Beaver, B., Insight into the Mechanisms of Middle Distillate Fuel Oxidative Degradation. Part 1: On the Role of Phenol, Indole, and Carbazole Derivatives in the Thermal Oxidative Stability of Fischer−Tropsch/Petroleum Jet Fuel Blends. Energy & Fuels 2009, 23 (4), 2041-2046. 12. Kabana, C. G.; Botha, S.; Schmucker, C.; Woolard, C.; Beaver, B., Oxidative stability of middle distillate fuels. Part 1: Exploring the soluble macromolecular oxidatively reactive species (SMORS) mechanism with jet fuels. Energy & Fuels 2011, 25 (11), 5145-5157. 13. Hazlett, R. N., Thermal oxidation stability of aviation turbine fuels. ASTM International: West Conshohocken, PA, 1991. 14. Hardy, D. R.; Wechter, M. A., Characterization of soluble macromolecular oxidatively reactive species (SMORS) from middle distillate diesel fuels: Their origin and role in instability. PREPRINTS OF PAPERS-AMERICAN CHEMICAL SOCIETY DIVISION FUEL CHEMISTRY 1994, 39, 912-912. 15. Beaver, B.; Gao, L.; Burgess-Clifford, C.; Sobkowiak, M., On the mechanisms of formation of thermal oxidative deposits in jet fuels. Are unified mechanisms possible for both storage and thermal oxidative deposit formation for middle distillate fuels? Energy & Fuels 2005, 19 (4), 1574-1579. 16. Kuprowicz, N. J.; Zabarnick, S.; West, Z. J.; Ervin, J. S., Use of measured species class concentrations with chemical kinetic modeling for the prediction of autoxidation and deposition of jet fuels. Energy & fuels 2007, 21 (2), 530-544. 17. Commodo, M.; Fabris, I.; Groth, C. P.; Gülder, O. m. L., Analysis of aviation fuel thermal oxidative stability by electrospray ionization mass spectrometry (ESI–MS). Energy & Fuels 2011, 25 (5), 2142-2150.


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18. ASTM International, ASTM D975-18-Standard Specification for Diesel Fuel Oils. ASTM International West Conshohocken: 2018. 19. Coordinating Research Council, I. Review of Existing Test Methods Used for Aviation Jet Fuel and Additive Property Evaluations with Respect to Alternative Fuel Compositions; May 2018, 2018. 20. Watson, J. T.; Sparkman, O. D., Introduction to mass spectrometry: instrumentation, applications, and strategies for data interpretation. John Wiley & Sons: 2007. 21. Lane, D., Online statistics education: a multimedia course of study (http://onlinestatbook. com/). Rice University 2006.


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Exploring the Molecular Origin of Jet Fuel Thermal Oxidative Instability

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