Restraining effect of nitrogen on coal oxidation in different stages: Non-isothermal TG-DSC and EPR research
Bibliographic record
Abstract
Nitrogen is widely used to prevent the spontaneous combustion of coal in underground coal mines. A spontaneous combustion-prone coal seam was studied to investigate the restraining effect of nitrogen on coal oxidation in different oxidation stages, based on non-isothermal thermogravimetry-differential scanning calorimetry (TG-DSC) and electron paramagnetic resonance (EPR) experiments. We found that the key feature temperatures grow steadily with increasing nitrogen in the oxidation environment, resulting in longer oxidation stages. The most significant finding is that there is a stagnation of the inhibitory effect of nitrogen on coal oxidation in the range of 85.0–95.0% nitrogen in the slow and the rapid oxidation stages, owing to the competitive adsorption of coal by nitrogen and oxygen. However, the restraining effect cannot be reflected by the kinetic parameters of the coal before it reaches the thermal decomposition and combustion stage. Nitrogen can also affect free radical types and free radical concentrations during coal oxidation: the higher the concentration of nitrogen in the oxidation environment, the greater the number of free radical types and the lower the free radical concentration. This experimental study improves the understanding of the restraining effect of nitrogen on coal oxidation in different oxidation stages and provides an important reference for coal fire prevention in spontaneous combustion-prone coal seams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".