Adsorption Behavior of CO<sub>2</sub>in Coal and Coal Char
Bibliographic record
Abstract
Recent interest in sequestration of carbon dioxide (CO 2 ) in gasified coal seam (i.e., post-underground coal gasification sites) has created a need to understand the coal properties, specifically, the adsorption behavior of CO 2 on gasified coal. In the present study, the CO 2 excess adsorption isotherms were determined for four coal samples of different characteristics based on the volumetric method. Further, coal chars from a coking coal and a non-coking coal (within the studied samples) were investigated for their CO 2 adsorption capacity. The coal samples of size 22–32 mm were pyrolyzed in a drop-tube furnace at 800 and 1000 °C with a heating rate of approximately 2.5 °C s –1 under an inert atmosphere. Measurements were performed up to a pressure of 65 bar for all of the studied samples. Experiments were carried out at an isothermal temperature of 45.5 °C. The influence of coal properties on adsorption was also studied and compared to the literature data. Behavior of adsorption capacities was analyzed as a function of coal properties, such as vitrinite content, coal rank, volatile matter, ash content, and surface area. Results indicated that the adsorption capacity of coal char is much higher in comparison to the virgin coal samples. It was understood from the surface area analysis that there is a significant increase in surface area when coal is pyrolyzed. In addition, for coal samples, the trend of adsorption isotherms was in good agreement with the literature data.
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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".