Volatilization of Potentially Harmful Trace Elements in Coal Pyrite during Heat Treatment in a Tube Furnace at 573–1473 K
Bibliographic record
Abstract
Some potentially harmful trace elements, such as Sb, As, and Hg, are usually associated with pyrite in coals and can be threats to the environment and human health. In this study, the release of 12 selected elements generally associated with pyrite was investigated during heat treatment in a tube furnace to access their behavior during combustion. FactSage has been used to calculate their behavior with the increase in temperature. Nickel, Cu, Zn, Cr, and Cd are more enriched in the samples containing syngenetic pyrite than in other samples. Other elements are generally more enriched in the samples containing epigenetic pyrite than in other samples. The volatilization of elements with a very high volatility, such as Hg and As, was complete at high temperatures in most samples. For other most elements in this study, the volatilization of a specific element in different samples is generally different. During heat treatment, the selected elements, according to their volatilization, can be classified into three groups: low, medium, or high volatility. At the conditions of this study, the volatilization of trace elements is influenced strongly by their inherent volatility, moderately by their modes of occurrence, and weakly by the pyrite types. The volatility-based behavior of the selected trace elements in FactSage calculation is generally consistent with their classification during heat treatment.
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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) | 0.001 | 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".