Synergistic inhibition effect and mechanism of an inhibitor for entire process inhibition of coal spontaneous combustion
Bibliographic record
Abstract
Traditional coal spontaneous combustion (CSC) inhibitors, while effective, have limitations such as frequent application or short-term efficacy. To this end, we developed a slow-release and water-soluble synergistic inhibitor (SWSI) to achieve long-term CSC inhibition. The SWSI was formulated by integrating synergistic antioxidants (SA) composed of ascorbic acid (AsA) and Fe-superoxide dismutase (Fe-SOD) dissolved in a super absorbent polymer (SAP) encapsulated within hydrogel microcapsules. The effectiveness of SWSI was evaluated through simultaneous thermal analysis and in-situ free radical tests. The formulation optimization demonstrated that AsA and Fe-SOD had the maximum synergistic inhibition effect at a 1:1 molar ratio. The optimal SA: SAP ratio of 3:1 achieved the lowest oxygen consumption rate and highest inhibition rate. The encapsulation formulation, consisting of 3% SA, 6% PAM, 1% CaCl 2 , and 0.5% citric acid, generated the highest swelling ratio. Simultaneous thermal analysis and in-situ free radical tests indicated that SWSI greatly increased feature temperatures and the apparent activation energy, lowered reaction heat, and substantially reduced free radical concentration in the full oxidation process. The newly developed SWSI offers a significant advancement in long-term CSC prevention by integrating physical and chemical inhibition, which provides sustained and effective inhibition throughout the entire oxidation process.
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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 | 0.000 | 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) | 0.000 | 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 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".