Release Behaviors of Arsenic in Fine Particles Generated from a Typical High-Arsenic Coal at a High Temperature
Bibliographic record
Abstract
To make an accurate assessment on transformation behaviors of arsenic in coal at a high temperature, a typical high-arsenic coal collected from southwest China has been chosen in the study and a series of high-temperature experiments in different atmospheres have been conducted with the help of a lab-scale drop-tube furnace. Fine particulate matter were collected by a low-pressure impactor, for obtaining their mass size distributions and further quantifying for arsenic distributions and speciation in fine particles of different sizes. The results indicated that the bleeding ratios of arsenic in air combustion, CO 2 gasification, and N 2 pyrolysis were 85, 65, and 45%, respectively, at 1300 °C. The ratio was found to remain relatively constant when the temperature increased from 1200 to 1400 °C. Organically associated arsenic would be more inclined to vaporize in N 2 pyrolysis, while both organic and inorganic associated arsenic would vaporize in CO 2 gasification and air combustion. Pyrite-associated arsenic would vaporize together with sulfur in pyrite inclusions. The decomposition of pyrite followed the principle of an unreacted core model and was mostly controlled by the surface sulfur vapor pressure. Mass size distributions of fine particulate matter generated from coal gasification presented a bimodal distribution, and two major peaks appeared at 0.4 and 5 μm. Particles in the size range of 5 μm were presented as a round shape with pores and cracks on the surface, while particles in the size range of 0.4 μm were confirmed to be soot. Arsenic was obviously enriched in fine particles with a size of around 0.1–0.2 μm in both combustion and gasification. The major speciation of arsenic identified in fine particles generated from coal combustion was As 2 O 5 and Ca 3 (AsO 4 ) 2, while that in fine particles generated from coal gasification was As 2 O 5, As, AsO, and Ca 3 (AsO 4 ) 2 .
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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".