Research on the Damping Performance of Mining Highly Efficient Water-Retaining Colloidal Material Against the Spontaneous Combustion of Coal
Bibliographic record
Abstract
In order to solve the shortcomings of the traditional mining anti-extinguishing gel material such as low strength and poor water retention, a high hydrocolloid anti-extinguishing material was developed with sodium alginate and light calcium carbonate as the base material and gluconolactone as the retarder, which was mixed and reacted. The base material ratio of highly efficient water-retaining colloidal material for coal void filling was determined as 2% SA + 0.5% PCC + 1% GDL with a moulding time of 4.5 min, while the base material ratio of highly efficient water-retaining colloidal material for extinguishing high temperature fires was 2.5% SA + 1% PCC + 1% GDL with a moulding time of 2.5 min. The highly efficient water-retaining colloidal material was found to reduce the concentration of signature gas and delay the characteristic temperature point and increase the activation energy of coal oxidation, which indicates that the highly efficient water-retaining colloidal material can effectively inhibit the spontaneous combustion process of coal at low temperature stage. Infrared spectroscopy experiments were conducted to investigate the microscopic resistance mechanism of the highly efficient water-retaining colloidal material, and the results showed that the highly efficient water-retaining colloidal material mainly reduce the activity of Ar-C-O-, -COO-, -CH3, -CH2 and -OH in coal to inhibit the spontaneous combustion of coal.
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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".