On the Comprehension of Mechanical, Thermal and Chemical Evolution of Exhaust Gases after Treatment Catalysts
Bibliographic record
Abstract
The stricter environmental regulations impose a drastic reduction of vehicles emissions and a longer durability of the automotive catalyst. The studies of the catalyst evolution in real conditions are very expensive. It is interesting to elaborate an ageing process to well simulate the real conditions of the automotive catalyst ageing at the laboratory scale. Thermal, chemical and mechanical effects can cause degradations of the catalytic performances. Thermal ageing is the result of high temperature that surges in the catalytic converters. Chemical ageing is due to phosphorus, zinc, magnesium, calcium or sulfur originating from either engine oil additives or fuel contaminants. Mechanical ageing (cracks and detachment) is the consequence of thermal and chemical ageing (thermal and textural stresses). Understanding the formation of crack patterns and spalling of thin films is challenging in fracture mechanics. The need for two conditions, one involving energy and the other one stresses, has recently been shown. The stress condition defines a threshold below which the pattern formation is inhibited. As this threshold is not reached, the energy accumulates. Then, at onset, depending on the strength and toughness of the material, the amount of energy can be sufficiently large to give rise to a more or less dense lattice of cracks. Following, after initiation, when the crack tips are close to the support, the newly created small cells tend to separate from it, thicker the film and more harmful the debonding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".