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Group type analysis of hydrocarbons and sulfur compounds in thermally stressed Merox jet fuel samples Chadin Kulsing, Paul M. Rawson, Renee L. Webster, David John Evans, and Philip John Marriott Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b01119 • Publication Date (Web): 02 Aug 2017 Downloaded from http://pubs.acs.org on August 3, 2017

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Group type analysis in Merox jet fuel

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Group type analysis of hydrocarbons and sulfur compounds

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in thermally stressed Merox jet fuel samples

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Chadin Kulsing1, Paul Rawson2,3, Renée L. Webster 1,2, David J. Evans2, and Philip J.

7

Marriott1,*

8 1

9

Australian Centre for Research on Separation Science, School of Chemistry, Monash University, Wellington Rd, Clayton 3800, Australia

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Defence Science and Technology, 506 Lorimer Street, Fisherman’s Bend 3207, Australia

3

School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, 124a Latrobe Street, Melbourne, Victoria, Australia

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Revised Submission to Energy & Fuels: ef-2017-01119aR1

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*Author for Correspondence:

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Philip J Marriott.

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TEL: + 61 3 99059630;

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FAX: + 61 3 99058501

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Email: [email protected]

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ACS Paragon Plus Environment

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Abstract

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Methods to investigate response of hydrocarbons and sulfur compounds to different extents

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resulting from thermal oxidation of jet fuels refined via the mercaptan oxidation (Merox)

34

process were developed. Relative comparison of hydrocarbon contents including n-paraffins,

35

iso-paraffins, olefins, naphthenes and aromatics (PIONA) was performed by using

36

comprehensive two dimensional gas chromatography hyphenated with a time of flight mass

37

spectrometer (GC×GC−TOFMS). A simple approach for quantification of sulfur compound

38

groups was then developed using both GC×GC and GC hyphenated with flame photometric

39

detection (GC×GC-FPD; GC−FPD). The approach largely separated the sulfur compounds

40

into two distinct regions comprising of lower polarity S1 (thiophenes, thiophenols, thiols,

41

disulfides, sulfide and alkyl thianaphthene) and higher polarity S2 (sulfones and

42

phenylsulfides) groups. The reliability of this approach was evaluated by using higher

43

resolution GC×GC−FPD, showing less than ±1% error arising from the co-elution of minor

44

compounds in GC−FPD analysis. The calibration method was further applied in order to

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reduce the error caused by the dependence of analyte contents on sample dilution effect from

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±14% to ±4%. The GC−FPD analysis showed significant increase in S2 content from 47% -

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59% after 113 min oxidation. This approach was then applied for characterisation of 15

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practical jet fuel samples.

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Introduction

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Under thermal stressing or oxidation during ambient storage, reactive components in jet fuels

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could react with oxygen to form hydroperoxides which will affect the performance of aircraft

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fuel system parts such as seals, diaphragms or other elastomer based materials.1-3 These

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peroxide species in a fuel are also known to initiate autoxidation reactions which ultimately

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lead to formation of sediments and gums, decreasing performance and further causing engine

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failure.4,

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addition of synthetic phenolic antioxidants which is mandated in jet fuel specifications for

59

hydroprocessed fuels.6, 7 These antioxidants may form stable intermediate radical species in

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peroxidation reactions, inhibiting the propagation of long chain products.8 However, re-

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addition of antioxidants into the same aged fuel types results in no improvement for

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inhibition of hydroperoxide formation, whilst oxidation induction times were improved for

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the re-addition of antioxidants.9

5

Gum formation and peroxidation after fuel manufacture may be prevented by

64 65

Detailed, class-type analysis of hydrocarbon fuels is an important and common analysis

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carried out in the liquid hydrocarbon fuel industry. The classes and relative amounts of

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different hydrocarbon types comprising hydrocarbon fuels will influence their physical and

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performance properties. A standardised test method for determining the paraffins,

69

isoparaffins, olefins, naphthenes and aromatics (PIONA) in middle distillate fuels is available

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in ASTM D2425.6, 7. This method however, requires a lengthy pre-fractionation step, detailed

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in ASTM D25496, 7 prior to mass spectrometric analysis of group hydrocarbon classes based

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on selected mass ions unique to each group. The high peak capacity of comprehensive two

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dimensional GC (GC×GC) analysis can eliminate the need for the pre-fractionation step as

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demonstrated by Lissityna et al. and Vendeuvre et al. 10, 11 PIONA type analyses will remain

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to be important into the future as new alternatively-derived fuel types continue to enter the

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market. These fuels often have characteristic composition i.e. predominantly isoparaffins, that

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enables chemical differentiation of these fuels from those that are conventional, fossil crude

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oil derived.

79 80

A recent advance in fuel analysis involves application of accurate mass time-of-flight mass

81

spectrometry (acc-TOFMS) offering high resolution analysis for informative structural

82

elucidation of analytes according to their accurate mass values with reduced uncertainty in


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compound library searching, especially for compounds containing heteroatomic species.10, 12

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With the limited capability to differentiate isomers or compounds sharing similar mass

85

fingerprints in direct MS analysis, separation is conventionally performed prior to MS

86

detection. The fast full-scan analysis capability of acc-TOFMS (up to about 50 Hz) also

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allows effective hyphenation with a high resolution separation technique such as GC×GC,

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improving capability to separate analytes and their isomers from matrix interferences10, 13, 14

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as well as more reliable confirmation of compounds according to retention index values.13

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Depending on the column set used, analyte peaks should be well separated in the two

91

dimensional (2D) space, enhancing analyte peak capacity compared to separation in 1DGC.15,

92

16

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different peak areas of mass fingerprints of each compound class and their homologues.10

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Such analysis allows reliable information (as illustrated for jet fuel analysis) being equivalent

95

to that obtained from the conventional reference methods such as IP 548/06 (liquid

96

chromatography based approach) and GC hyphenated with a flame ionisation detection for

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determination of aromatics and n-alkanes, respectively.

Semiquantification of PIONA can be obtained using GC×GC−TOFMS according to

98 99

Apart from hydrocarbons, fuels also contain a range of heteroatomic species, with the most

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abundant being sulfur. In addition to the influence on fuel stability and corrosion properties,

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combustion of fuels containing sulfur produces SO2 leading to suppression of vehicle-

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emissions-control performance and negative environmental outcomes such as acid rain.17

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Sulfur content in jet fuel is limited to C0, compound 4), thiols (up to about C10, compound 5), disulfides

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(compound 6), and sulfide (< C12, compound 7), and possible higher polarity compounds in

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S2 region attributed to alkyl benzothiophene (> C0, compound 8), sulfone (compound 9) and

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phenylsulfide (compound 10), see Figure 2B. Note that the studied Merox jet fuel samples

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apparently did not contain dibenzothiophene (compound 11), phenyldisulfide (compound 12),


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diphenylsulfone (compound 13) and dibenzothiophenesulfone (compound 14) since these

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compounds eluted much later than observed for the S2 region.

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Improved calculation method for group type analysis of sulfur compounds by GC−FPD

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Calibration curves of the group type concentration (x) and the corresponding FPD response (y)

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can be generated for each sulfur group type resulting in a linear relationship y = mx +

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c .where m and c are slope and intercept of the linear calibration curves. Note that the

detection mechanism in FPD involves formation of excited sulfur (S2*) in a reducing

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(hydrogen/air) flame. S2* will generate characteristic chemiluminescence at specific

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wavelengths which can be detected by adjusting the optical filter to allow the photomultiplier

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to record the signal at the wavelength of 394 nm. The detector response to sulfur is nonlinear

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depending on the square of the concentration.20 In contrast, response in SCD is linear. For the

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FPD analysis in this study, by rearranging in terms of x,

327 328

= √

(1)

(2)

330

= √

331

where subscripts 1 or 2 represent each group type index in the sample containing two groups

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of analytes. Percentage concentration of the species x1 (%x1) can be calculated as

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% =

329

334

× 100 =

335

× 100

(3)

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For the sake of simplicity, %x1 may be assumed to be % peak area as

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%x ~

338

339

× 100

(4)

340

More accurate calculation can be obtained by substituting Equations 1 and 2 into 3

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%x =

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!" $ #

!" $ #

%

× 100 =

# & !"'

#& !" '

× 100


(5)

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Note that Equation 4 is a special case of Equation 5 when m1 = m2 and c1 = c2 = 0. The error

345

arising from Equation 4 can be significant especially when calculating the percentage

347

content at low abundance, where y ≪ c or when m ) m . Due to samples with known S1

348

calibration curves. Generation of calibration curves from the normalised concentration can be

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used for extrapolation of the y-intercept (c value) since samples of any concentration can be

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diluted to be close to zero, Figure 4A. On the other hand, in the absence of a sample with

351

known S1 or S2 content, a precise m value cannot be calculated since a certain value of

352

normalised concentration (X) can be derived from many absolute concentration values, e.g.

353

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or S2 content not being available, normalised concentration was used for construction of

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the sets of concentration A = *1, 5, 10- and B = *2, 10, 20- resulting in the same normalised

355

concentration of X = *0.1, 0.5, 1-. Note that the slope m is calculated from the ratio of the

square response difference to the absolute concentration difference. Since ∆A = 5–1 = 4 and

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∆B = 10–2 = 8 are not equal to the normalised concentration difference, ∆X = 0.5–0.1 = 0.4.

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The use of ∆X thus leads to error in calculation of m1 and m2 (and thus resulting in %S1 or

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%S2 error) especially when the concentrations of S1 and S2 are significantly different.

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However, such error can be minimised by using a sample with similar S1 and S2 content for

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the generation of calibration curves for %S1 or %S2 calculation. Thus, P24 with roughly

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comparible amount of S1 and S2, was selected for the construction of calibration curves since

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this sample underwent about 50% thermal oxidation as mentioned earlier.

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364 365 366

Figure 4. (A) Calibration curves (peak area2 vs normalised concentration) of S1 and S2

367

group, dashed and solid lines, respectively and (B) a plot illustrating the variation of %S2

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with samples prepared with different dilution calculated by using Equation 4 and 5. The data

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in A and B were obtained by using the P24 sample analysed with 1DGC−FPD on the

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SUPELCOWAX 10 column. GC conditions as in Figure 3.


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Since the calibration curves for S1 and S2 groups (see dashed and solid lines, respectively, in

373

Figure 4A) showed m1 ≠ m2 and c1 ≠ c2 ≠ 0, Equation 4 is not suitable for the %S2

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calculation due to significant errors as mentioned above. As a result, %S2 values calculated

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by using this equation showed strong dependency on the extent of sample dilution leading to

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about ±14% error. Application of Equation 5 clearly reduced variation to be ±4% error, as

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shown in Figure 4B. Equation 5 is thus applied further for the %S2 calculation for the 15 jet

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fuel samples.

379 380

Group type analysis of sulfur compounds in 24 jet fuels from different sources, analysed by

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GC−FPD

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An increase in the content of higher polarity compounds (%S2) with progression of thermal

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oxidation was observed, Figure 5A. This is expected, and corresponds to the formation

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primarily of sulfoxides and sulfones, resulting from the progressive oxidation of sulfides and

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disulfides. These compounds are important contributors to fuel thermal stability as possible

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precursor molecules in the generation of insoluble desposits in fuel. The jet fuel samples from

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a range of sources were then investigated, Figure 5B, revealing different S2 contents with the

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corresponding concentration profiles for S1, S2 and total concentrations plotted in Figure

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5C. Although not definitive, the S1 and S2 contents of the fuels can assist in determining the

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provenance and refining method of the fuel, which can be valuable. For example, it is clear

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that fuel 155, which is very low in S2 content, is a highly processed, hydrotreated fuel.

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Comparison of the sum of S1 and S2 concentration derived from the GC approach, is well

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aligned with the results obtained for total sulfur in the same fuels. The GC×GC approach

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adds valuable richness to the distribution of different sulfur-containing functional groups in a

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fuel, which is often still only determined by carrying out several discrete and time consuming

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test methods for each group of interest. Traditional test methods are limited in the speciation

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that can be achieved, and there is much value in the group-typing approach for rapid

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assessment of sulfur compound classes in jet fuels. Further investigation and differentiation

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of functionalities within each of the S1 and S2 sulfur groupings is desirable to determine

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which sulfur compounds improve fuel stability by scavenging radical peroxide species and

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stopping the radical propogation phase in autoxidation of hydrocarbon fuels. Also of interest

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is which sulfur compounds decrease fuel stability through contributing to the formation of

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insoluble deposits.


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404 405

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Figure 5. (A) Illustration of the variation of %S2 (relative to total S) with progression of

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thermal oxidation. (B) %S2 of different jet fuel samples from different sources calculated by

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using Equation 5. (C) Concentration of S1, light grey bar, S2, dark grey bar, and total S

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content, black bar. The data in A and B were obtained by using the same column and

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experimental conditions as that in Figure 4. Sulfur content in Sample 155 (with the total


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concentration of 14 ppm) was not detected by GC−FPD under the investigated condition

413

(with 0.14 ng injected on column). The error bars were generated from three replicates.

414 415

Conclusion

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GC×GC−TOFMS, GC×GC−FPD and GC−FPD were applied for analysis of Merox jet fuels.

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Whilst PIONA analysis reveals similar contents of hydrocarbons as the fuels were thermally

418

stressed, relative content of higher polarity sulfur components such as sulfones and

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phenylsulfides in the fuels, increased after thermal stressing and further increased when the

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thermal stressing period was extended. This study developed a simple and reliable method for

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group type analysis of hydrocarbons and sulfur compounds in conventional and alternate jet

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fuels formation which will provide insights into the mechanistic details of insoluble deposit

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formation, and potential routes to mitigate the undesired effects of oxidation.

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ASSOCIATED CONTENT

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Supporting Information

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Sets of nominal m/z values selected for various classes of compounds are given in Table S1,

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and their exact m/z values are given in Table S2. Figure S1 displays extracted ion data (EIC)

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for various classes of compounds using exact mass (± 10 ppm) m/z results for Merox. Figure

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S2 shows the effect of mass filter accuracy (±10 ppm vs ± 20 ppm), and S3 shows n-paraffin

431

data for nominal (± 0.1 amu) vs exact mass (± 10 ppm) for Merox.

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This material is available free of charge via the Internet at http://pubs.acs.org.

433 434

AUTHOR INFORMATION

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Corresponding Author

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*Telephone: +61-3-99059630. Fax: +61-3-99058500. E-mail:

437

[email protected].

438

Notes

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The authors declare no competing financial interest.

440 441

ACKNOWLEDGMENTS

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This research was performed under the task of the Fuel Services Branch, Vice Chief of the

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Defence Force Group. Alternate fuels were kindly supplied by the U.S. Naval Air Systems

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Command (NAVAIR) Fuels Division. This work is supported by the Australian Research


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Council (ARC) Linkage Project LP130100048, and Philip J. Marriott acknowledges the ARC

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Discovery Outstanding Researcher Award (DP130100217).

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References

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1. Watkins, J. M., Jr.; Mushrush, G. W.; Hazlett, R. N., Reactions involving hydroperoxide formation in jet fuels. Prepr. Pap. - Am. Chem. Soc., Div. Fuel Chem. 1987, 32, (1), 513–521. 2. Fodor, G. E.; Naegeli, D. W.; Kohl, K. B., Peroxide formation in jet fuels. Energy Fuels 1988, 2, (6), 729–734. 3. Watkins, J. M., Jr.; Mushrush, G. W.; Hazlett, R. N.; Beal, E. J., Hydroperoxide formation and reactivity in jet fuels. Energy Fuels 1989, 3, (2), 231–236. 4. Heneghan, S. P.; Zabarnick, S., Oxidation of jet fuels and the formation of deposits. Fuel 1994, 73, (1), 35–43. 5. Pickard, J. M.; Jones, E. G., Kinetics of the autoxidation of a Jet-A Fuel. Energy Fuels 1996, 10, (5), 1074–1077. 6. ASTM D1655-11b Standard Specification for Aviation Turbine Fuels. In ASTM International, Ed. West Conshohocken, 2011. 7. ASTM D7655-11a Standard Specification for Aviation Turbine Fuel Containing Synthesized Hydrocarbons. In ASTM International, Ed. West Conshohocken, 2011. 8. Pospíšil, J., Mechanistic action of phenolic antioxidants in polymers-A review. Polym. Degrad. Stab. 1988, 20, (3-4), 181–202. 9. Rawson, P. M.; Stansfield, C. A.; Webster, R. L.; Evans, D., Re-addition of antioxidant to aged MEROX and hydroprocessed jet fuels. Fuel 2014, 139, 652–658. 10. Lissitsyna, K.; Huertas, S.; Quintero, L. C.; Polo, L. M., PIONA analysis of kerosene by comprehensive two-dimensional gas chromatography coupled to time of flight mass spectrometry. Fuel 2014, 116, 716–722. 11. Vendeuvre, C.; Bertoncini, F.; Duval, L.; Duplan, J.-L.; Thiebaut, D.; Hennion, M.-C., Comparison of conventional gas chromatography and comprehensive two-dimensional gas chromatography for the detailed analysis of petrochemical samples. J. Chromatogr. A 2004, 1056, (1-2), 155-162. 12. Webster, R. L.; Rawson, P. M.; Evans, D. J.; Marriott, P. J., Synthetic phenolic antioxidants in middle distillate fuels analyzed by gas chromatography with triple quadrupole and quadrupole time-of-flight mass spectrometry. Energy Fuels 2014, 28, (2), 1097-1102. 13. Jiang, M.; Kulsing, C.; Nolvachai, Y.; Marriott, P. J., Two-dimensional retention indices improve component identification in comprehensive two-dimensional gas chromatography of saffron. Anal. Chem. 2015, 87, (11), 5753–5761. 14. Mitrevski, B.; Marriott, P. J., Evaluation of quadrupole-time-of-flight mass spectrometry in comprehensive two-dimensional gas chromatography. J. Chromatogr. A 2014, 1362, 262–269. 15. Kulsing, C.; Nolvachai, Y.; Rawson, P.; Evans, D. J.; Marriott, P. J., Continuum in MDGC technology: From classical multidimensional to comprehensive two-dimensional gas chromatography. Anal. Chem. 2016, 88, (7), 3529-3538. 16. Nolvachai, Y.; Kulsing, C.; Marriott, P. J., In silico modeling of hundred thousand experiments for effective selection of ionic liquid phase combinations in comprehensive twodimensional gas chromatography. Anal. Chem. 2016, 88, (4), 2125-2131. 17. Gonzalez, L. A.; Kracke, P.; Green, W. H.; Tester, J. W.; Shafer, L. M.; Timko, M. T., Oxidative desulfurization of middle-distillate fuels using activated carbon and power ultrasound. Energy and Fuels 2012, 26, (8), 5164–5176.


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18. Lee, I. C.; Ubanyionwu, H. C., Determination of sulfur contaminants in military jet fuels. Fuel 2008, 87, (3), 312–318. 19. Link, D. D.; Baltrus, J. P.; Rothenberger, K. S.; Zandhuis, P.; Minus, D.; Striebich, R. C., Rapid determination of total sulfur in fuels using gas chromatography with atomic emission detection. J. Chromatogr. Sci. 2002, 40, (9), 500–504. 20. Mitrevski, B.; Amer, M. W.; Chaffee, A. L.; Marriott, P. J., Evaluation of comprehensive two-dimensional gas chromatography with flame photometric detection: Potential application for sulfur speciation in shale oil. Anal. Chim. Acta 2013, 803, 174–180. 21. Timko, M. T.; Schmois, E.; Patwardhan, P.; Kida, Y.; Class, C. A.; Green, W. H.; Nelson, R. K.; Reddy, C. M., Response of different types of sulfur compounds to oxidative desulfurization of jet fuel. Energy Fuels 2014, 28, (5), 2977–2983. 22. Webster, R. L.; Evans, D. J.; Rawson, P. M.; Mitrevski, B. S.; Marriott, P. J., Oxidation of neat synthetic paraffinic kerosene fuel and fuel surrogates: Quantitation of dihydrofuranones. Energy Fuels 2013, 27, (2), 889-897. 23. Wong, Y. F.; Chin, S. T.; Perlmutter, P.; Marriott, P. J., Evaluation of comprehensive two-dimensional gas chromatography with accurate mass time-of-flight mass spectrometry for the metabolic profiling of plant-fungus interaction in Aquilaria malaccensis. J. Chromatogr. A 2015, 1387, 104–115.

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Figure Legends

512

Figure 1. GC×GC−TOFMS results of (A): neat Merox jet fuel, and the corresponding fuels

513

which had undergone oxidation for (B): 113 and (C): 233 min. A polar-nonpolar column set

514

with 1D SUPELCOWAX 10 column and 2D Rxi-5Sil MS column was used.

515

corresponding profiles of n-paraffins, iso-paraffins, olefins, naphthenes and aromatics

516

(PIONA). Response bars (L to R) in D correspond to neat, 113 min, and 233 min oxidation

517

time samples respectively. The oven T was set at 40 °C for 3 min, raised to 250 °C (8 °C/min,

518

hold for 30 min). The 1tR vs 2tR plot for the peaks of different compound classes in C are

519

shown in E, in order to display different chemical classes. The compound classes in both D

520

and E are (a) alkanes, (b) alkenes/ cycloalkanes, (c) dialkenes/dicycloalkanes, (d)

521

trialkenes/tricycloalkanes,

522

dinaphthobenzenes,

523

acenaphthylenes/fluorenes and (k) phenanthrenes. The error bars in D were obtained with n =

524

3 performed on different days.

(e)

(h)

alkylbenzenes, naphthalenes,

(f) (i)

(D):

benzonaphthenes/indanes,

(g)

acenaphthenes/biphenyls,

(j)

525 526

Figure 2 GC×GC−FPD results of (A): neat Merox jet fuel, and the corresponding fuels which

527

had undergone oxidation for (B): 24 min, and (C): 30 min analysed on the nonpolar-polar

528

column set: 1D DB-5ms and 2D SUPELCOWAX 10 columns, and with the corresponding

529

result on the polar-nonpolar column set (same as that in Figure 1) shown in D. GC conditions

530

as in Figure 1.

531


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Figure 3. (A) GC×GC−FPD results of Merox jet fuel which had undergone oxidation for 68

533

min (P24) on the polar-nonpolar column set (same as that in Figure 1) with the corresponding

534

1DGC results on the SUPELCOWAX 10 column shown at the top of panel (A). The oven T

535

was set at 100 °C, raised to 250 °C (4 °C/min, hold for 30 min). (B) Standards 1–12 peak

536

positions in 2D space, with compounds 5 and 6 located within S1, and 9 and 10 in S2. The

537

extended ‘tails’ for the response of the peaks in Figure 3 arise from the detection mechanism

538

of the FPD towards sulfur, and not from column-based peak tailing.20

539 540

Figure 4. (A) Calibration curves (peak area2 vs normalised concentration) of S1 and S2

541

group, dashed and solid lines, respectively and (B) a plot illustrating the variation of %S2

542

with samples prepared with different dilution calculated by using Equation 4 and 5. The data

543

in A and B were obtained by using the P24 sample analysed with 1DGC−FPD on the

544

SUPELCOWAX 10 column. GC conditions as in Figure 3.

545 546

Figure 5. (A) Illustration of the variation of %S2 (relative to total S) with progression of

547

thermal oxidation. (B) %S2 of different jet fuel samples from different sources calculated by

548

using Equation 5. (C) Concentration of S1, light grey bar, S2, dark grey bar, and total S

549

content, black bar. The data in A and B were obtained by using the same column and

550

experimental conditions as that in Figure 4. Sulfur content in Sample 155 (with the total

551

concentration of 14 ppm) was not detected by GC−FPD under the investigated condition

552

(with 0.14 ng injected on column). The error bars were generated from three replicates.


Group-Type Analysis of Hydrocarbons and Sulfur ... - ACS Publications

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