Study of Al<sub>2</sub>O<sub>3</sub> addition to synthetic Ca‐based sorbents for CO<sub>2</sub> sorption capacity and stability in cyclic operations
Bibliographic record
Abstract
Abstract Synthetic CaO sorbents were prepared using alumina as a sintering inhibitor via a simple precipitation method. The effects of three mixing procedures on the physical properties and CO2 capture performance of the sorbents were examined. The cyclic CO2 capture performance of the sorbent derived from the precipitation of calcium salts over colloidal alumina (highly dispersed alumina gel) showed the best performance of the three mixing methods. It was found that variation of alumina‐to‐CaO ratios did not significantly change the sintering influence on the sorbent capacity in cyclic operations. CaO particles homogeneously mingled with alumina at higher ratios. Sintering prevention, however, was not observed. This important observation indicates that alumina appeared to merely act as a binder for the fabrication of mechanically enhanced strength particles that are suitable for large‐scale operations. It was determined that CO2 uptake was not dependent on either the mixing technique or the type of synthetic materials incorporated into a sorbent in cyclic operation. The sorbent derived from the precipitation of calcium salts over colloidal alumina with an alumina‐to‐CaO ratio of 20:80 achieved the highest CO2 uptake of 13.1 moles/kg sorbent for half an hour of carbonation in the first cycle and retained a sorption capacity of 6.5 moles/kg sorbent after 17 successive cycles (50 % activity loss), which is in agreement with the reported results. It was demonstrated that the quantity of CO2 uptake increased moderately with decreasing sorbent particle sizes. The effect of pressure on sorbent CO2 uptake was insignificant.
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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 | 0.001 |
| 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".