Experimental investigation on wetting mechanism for coal dust with different metamorphic degree based on infrared spectrum and <sup>13</sup> C‐NMR
Bibliographic record
Abstract
Respirable coal dust accounts for heart and respiratory diseases of coal miners such as asthma, pneumoconiosis, and black lung disease. What is more, coal dust explosion seriously affects coal mine safety production and coal miners' life safety. Generally, dust suppressants are commonly applied in coal mines. However, current dust suppressants are not working effectively. To develop a better dust suppressant, we attempt to explore the factors affecting the wettability of coal dust under different metamorphic levels from the essence of coal dust wetting mechanism in this paper. Specifically, we use Fourier transform infrared (FTIR) spectroscopy and nuclear magnetic resonance (NMR) to reveal the microstructure of coal from two aspects of functional group and carbon skeleton structure and obtain the micro information of the surface functional group types, quantity, and carbon structure of coal with different degrees of metamorphism, as well as the change rule of functional group of coal sample with coal rank and the law of carbon increase and deoxidization of coal metamorphism. After that, we acquired the structural parameters of coal by the NMR experiments and fitted the quantitative mathematical relationship between the microstructure parameters and wettability of coal through SPSS and ORIGIN software. Finally, this paper constructs an evaluation model for the influencing factors of coal dust wettability, explains the influence degree of different coal dust structure on coal dust wettability, improves the coal dust wettability mechanism, and provides more quantitative research ideas and methods for the control of coal dust.
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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 | 0.001 |
| Open science | 0.000 | 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".