Automotive Diesel Fuel Internal Stability Testing with the Use of UV and Temperature as Degradation Factors
Bibliographic record
Abstract
Diesel fuel stability can be considered from many points of view, of which the two considered most important are stability in contact with the environment and internal stability. Fuel stability in touch with the environment is often defined as oxidation stability, of which measurement procedures are well developed. The presented paper shows that fuel's internal stability can also be important. The internal stability of diesel fuel with the local use of thermal and ultraviolet radiation (UV) as degradation factors and fluorescence signals as a probe is presented in this paper. We show that the internal degradation of fuel with temperature use differs from that with UV and simultaneous both factors use. Our study shows that using temperature as a degradation factor introduces significant fluorescence fading. Moreover, the fluorescence signal restores significantly later than the sample stabilizes at room temperature. The novelty proposed based on examination is hybrid degradation and an examination cycle that enables the simultaneous use of degradation factors and fluorescence reading. For this purpose, a dedicated measurement setup of signal control and processing was constructed and programmed. The measurement procedure of the data series for specific wavelength enables calculation of signal shifts that allow the internal stability classification of diesel fuel samples in less than 30 min with the cost of a single disposable capillary probe and one polymer plug. Premium and regular fuel examination results show that internal fuel stability can be related to polycyclic aromatic hydrocarbons (PAH) concentrations and can be modified with dedicated additives.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".