Three-Dimensional Fluorescence Spectra of Thermally Stressed Commercial Jet A-1 Aviation Fuel in the Autoxidative Regime
Bibliographic record
Abstract
In this study, the thermal oxidative stability of a kerosene-type Jet A-1 commercial aviation fuel has been investigated using a three-dimensional (3D) excitation/emission matrix fluorescence (EEMF) method. The fuel was thermally stressed in flow test conditions over a range of temperatures in the autoxidative regime. To determine the effect of dissolved oxygen on aviation fuel thermal stability, the measurements were conducted for both air-saturated and fully deoxygenated fuel samples. The increase in the fuel temperature results in a large red shift of the fluorescence signals for the air-saturated fuel; however, fully deoxygenated fuel showed no difference in the fluorescence spectra with respect to neat fuel. The observed increase in the emission wavelengths of the collected spectra may be attributed to the formation of high-molecular-mass compounds within the liquid fuel. These species are formed as a consequence of the chemical reactions activated during the thermal stressing process. The use of 3D fluorescence spectra for aviation fuel analysis is shown to be a fast, suitable, and easily implementable tool for establishing and verifying fuel quality. Also, the level of fuel thermal degradation may be ascertained using this method, which should be of great interest for a better thermal management control of turbine engines.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".