Structural changes and product distribution of typical Xinjiang coals and chars during pyrolysis
Bibliographic record
Abstract
Abstract This study used micro‐Raman spectroscopy, gas chromatography–mass spectrometry (GC–MS), and gas chromatography–flame ionization detector/thermal conductivity detector (GC–FID/TCD) to analyze the structure and pyrolysis reactions of nine typical coals and chars from Xinjiang. The study fitted 10 Gaussian bands of typical Xinjiang coal and investigated the changes in coal structure during coalification and pyrolysis. The results indicated that the reduction degree of CO structures in coal during coalification had a rough linear relationship with the Vdaf (dry ash‐free volatile matter) content. During coalification, the condensation of aromatic rings is accompanied by a continuous decrease of CO structures, while the contents of cross‐linking and substitution structures decrease persistently relative to the large aromatic ring structures. The influence of coal type on char yield for typical Xinjiang coal is within 15 wt.%; the influence on tar yield is within 8.5%, with a greater impact on the yield of alkanes and phenols in tar; the influence on CO yield in pyrolysis gas is within 6.3%. The relative content of large aromatic ring structures in coal is relatively stable during pyrolysis, while the relative content of small aromatic ring structures declines as coal transforms into char. The study inferred that small aromatic rings might decompose and transform into tar after pyrolysis reaction, which also resulted in a high selectivity of phenolic products in tar from most coal pyrolysis above 40%. This study revealed the structural changes and pyrolysis product distribution of nine typical coals and chars from Xinjiang, providing useful information for their utilization.
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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 | 0.001 | 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".