Analysis of Aviation Fuel Thermal Oxidative Stability by Electrospray Ionization Mass Spectrometry (ESI–MS)
Bibliographic record
Abstract
In modern gas-turbine aircraft, aviation fuel is routinely used as a coolant to remove waste heat loads from, for example, lubrication and secondary engine systems, in addition to its conventional role as the energy source through combustion. The increase of the fuel temperature activates several liquid-phase reactions involving hydrocarbon molecules, dissolved oxygen, and indigenous heteroatomic (O, N, and S) polar compounds. The result of such reactions is the formation of gums and solid deposits within the fuel lines, which are of concern for the maintenance of gas-turbine engines. In the present paper, the thermal oxidative stability of commercial aviation fuel was investigated by the positive electrospray ionization mass spectrometry (ESI–MS) technique. Several classes of polar species, with different levels of alkylation, were detected in the unstressed jet fuel. The majority of these compounds were characterized by a molecular mass within the range of 90–250 Da. Thermally stressed jet fuel, under different experimental conditions, showed the presence of a broad molecular mass band of polar compounds in the range of 250–400 Da. This class of soluble high-molecular-mass compounds was not detectable when the dissolved oxygen was removed from the fuel by nitrogen sparging. Consistent with previous findings in the literature, we postulate that aggregation processes of these species, through polymerization or clustering reactions, could be responsible for the formation and precipitation of insoluble compounds, which ultimately lead to the formation of the oxidative deposits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".