A Facile fabrication of mesoporous core–shell CaO-Based pellets with enhanced reactive stability and resistance to attrition in cyclic CO<sub>2</sub>capture
Bibliographic record
Abstract
Highly stabilized mesoporous core–shell-structured CaO-based spheriform CO2 sorbents are fabricated, for the first time, by a novel repeated impregnation coating process combined with the mesoscopic surfactant-templating method. By adopting our established wet-coating strategy along with a sol–gel process, different mesoporous material-shelled sorbents with various shell thicknesses (1–5 μm) and shell compositions (silica/zirconia or pure zirconia) are synthesized. Cyclic CO2 capture performance is tested in a thermogravimetric analyzer with the core–shell pellet sorbents with a ∼1 μm mesoporous zirconia shell exhibiting an unprecedented CO2 uptake capacity of ∼7.2 moles CO2 per kg decarbonated sorbent and the lowest activity loss of only 30.8% after 20 cycles. This is attributed to the unique core–shell coating strategy in which the thermally stable Zr species prevent the aggregation and overgrowth of CaO crystals and sorbent sintering. When comparing the core–shell sorbents with Si-contained mesoporous shells, zirconia shelled ones exhibit significantly more outstanding performance. An attrition study using an air-jet apparatus under the standard test method reveals that the mesoporous zirconia shelled sorbent exhibits enhanced attrition resistance, which is also attributed to the novel core–shell design, offering protection for the reactive core.
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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) | 0.001 | 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) | 0.000 | 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".