Calcium looping carbon capture: Progress and prospects
Bibliographic record
Abstract
Abstract Carbon capture and utilization/storage is an integral part of a smooth transition to a net‐zero energy portfolio. The distinctive advantages of the calcium looping (CaL) process, including low‐cost and high‐theoretical uptake capacity, make it a promising approach for the decarbonization of fossil fuel power plants and carbon‐intensive industries, including cement and steel. CaL exploits the reversible reaction of CO 2 with CaO to capture and release carbon dioxide in a cyclic process. This paper reviews the fundamentals of the CaL process, the kinetics of the carbonation reaction, and extensive research on the development of sorbent materials with high durability for use in the CaL process. Various optimizing strategies for the improvement of the stability and CO 2 uptake capacity of materials are outlined. Lastly, an overview of bench‐ and pilot‐scale testing facilities around the world is provided. The characteristics, operating conditions, and the main experimental findings of the testing facilities are summarized.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".