Effect of Surface Area and Micropore Volume of Activated Carbons from Coal by KOH, NaOH and ZnCl2 Treatments on Methane Adsorption
Bibliographic record
Abstract
Abstract This paper investigates the methane adsorption characteristics of activated carbons produced from coal by activation with KOH, NaOH and ZnCl2 treatments at different agent to coal ratios (1:1–6:1) and temperatures (600–800 °C) under N2 flow. CH4 adsorption capacities and desorption behaviors of the activated carbons were examined at 0 °C and 25 °C. The relationship between CH4 adsorption characteristics and surface properties of activated carbons, such as BET surface area determined by N2 adsorption at −196 °C, and micropore volume determined by CO2 adsorption at 0 °C were investigated. Optimal results for CH4 adsorption at 0 °C and 25 °C were obtained for the activated carbon samples obtained with KOH treatment at 800 °C at 4:1 ratio, as 2.67 and 1.12 mmol/g, respectively. The results have shown that CH4 adsorption increased proportionally with micropore volume of activated carbons, whereas BET surface area does not exhibit an exact consistency. CH4 adsorption-desorption isotherms at 25 °C have shown that an increase in mesopore formation caused a decrease in adsorption; but allowed desorption to be reversible. Higher methane adsorption capacities were obtained from activated carbons produced from coal by various treatments in this study than most of the reported results in literature at the similar conditions, indicating the suitability of the evaluated materials for industrial applications of methane storage.
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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".