Pelletizing and Coating of Synthetic Zirconia Stabilized Calcium-Based Sorbents for Application in Calcium Looping CO<sub>2</sub> Capture
Bibliographic record
Abstract
Calcium oxide (CaO) has been investigated as a high-temperature performance sorption material for carbon capture due to its high carbon dioxide (CO 2 ) capacity, low cost, and environmental benignity. Unfortunately, commercialized application of CaO materials has been hindered by severe capacity loss during cycling operations. In an attempt to overcome these problems, this study investigated the effects of adding zirconia-based ceramic dopants and coating sorbents with highly crystalline zirconium dioxide (ZrO 2 ) layers derived from the hydrolysis of zirconium butoxide to control the growth of ZrO 2 crystals. Uncoated natural Cadomin delivered a high CO 2 capture capacity of more than 13 mol CO 2 /kg, while the zirconia-coated sorbents showed excellent cycling stability corresponding to a maximum capacity of retention of 79% after 21 cycles at high temperatures. More importantly, the highly crystalline ZrO 2 -coated sorbents doped by zirconia-based sorbents exhibited a significantly lower decay capacity compared to the uncoated sorbents, which could be attributed to the formation of a perovskite structure that is resistant at high temperatures and a high-temperature-resistant ceramic layer through the solid-state reaction of the highly mesoporous crystalline ZrO 2 layer with CaO. These results shed light on the development of sorbents with special structures and superior chemical and mechanical properties for high-performance calcium looping or regenerative calcium cycle processes.
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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".