Reaction model and thermodynamic properties between sulfur-containing active groups and oxygen during coal self-heating
Bibliographic record
Abstract
To further study the mechanism of coal self-heating, the reaction sequences and thermodynamic properties between sulfur-containing groups and oxygen during coal self-heating were analyzed. The benzyl mercaptan and diphenyl sulfide were selected as typical sulfur-containing structures existing in coal. Their structural parameters, frontier orbital characteristics, and thermodynamic parameters were analyzed through quantum chemistry calculation and their detailed reaction sequences with oxygen were proposed. The results indicate that the thiol structure in coal can easily react with oxygen at low temperatures and release large amounts of heat (146.70 kJ/mol) during coal self-heating, providing active free radicals and energy for subsequent chain reactions of coal spontaneous combustion. The oxidation reaction between the thioether structure and oxygen cannot occur at room temperature. With the accumulation of heat, thioether gradually becomes active and reacts with oxygen to form sulfoxide and release an enormous amount of heat (248.09 kJ/mol), which can be further oxidized to sulfone with an increase in temperature. The reaction models of thiol and thioether groups during coal self-heating were proposed, which involves eight main reaction sequences (R1∼R8). It indicates that the reactions of thiol and thioether groups play crucial roles during the evolution of coal self-heating, with a slow oxidation stage at low temperatures and an accelerated oxidation stage at high temperatures.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".