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Record W4410761387 · doi:10.3390/pr13061680

Potential, Efficiency, and Leakage Risk of CO2 Sequestration in Coal: A Review

2025· review· en· W4410761387 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcesses · 2025
Typereview
Languageen
FieldEngineering
TopicCoal Properties and Utilization
Canadian institutionsUniversity of Regina
FundersNational Natural Science Foundation of China
KeywordsLeakage (economics)Carbon sequestrationEnvironmental scienceNatural resource economicsCoalCarbon leakageWaste managementCarbon dioxideChemistryGreenhouse gasEngineeringEconomicsGeologyEmissions tradingOceanography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.742
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.275
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it