Potential, Efficiency, and Leakage Risk of CO2 Sequestration in Coal: A Review
Bibliographic record
Abstract
CO2 sequestration in coal is effective for reducing carbon emissions, but related projects have encountered challenges in sustained CO2 injection, highlighting the need for a comprehensive understanding of CO2 sequestration in coal. This study reviews variations in the properties of coal/rock during/after CO2 injection, demonstrating the potential and stability of CO2 sequestration in coal. The coal with a high VL-CO2/VL-CH4 is accompanied by high CO2 sequestration capacity. The matrix swelling and acid corrosion restrict CO2 sequestration efficiency, which can be enhanced by employing coatings and increasing permeability. Long-term CO2–water–rock interactions weaken the integrity of coal/caprocks and decrease the adsorption capacity of coal, leading to the CO2 leakage risk. Three issues are critical in future studies: (1) Increasing CO2 adsorption capacity. (2) Establishing optimal approaches to enhance CO2 injection efficiency. (3) Accurately predicting variations in the adsorption capacity of deep coal and the integrity of coal/caprocks during long-term CO2–water–rock interactions. This review provides foundations for formulating CO2 sequestration strategies in coal.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".