Application of Core-Shell-Structured K<sub>2</sub>CO<sub>3</sub>-Based Sorbents in Postcombustion CO<sub>2</sub> Capture: Statistical Analysis and Optimization Using Response Surface Methodology
Bibliographic record
Abstract
This study investigates the effect of core-shell-structured supports prepared with alumina as the core on the CO 2 capture performance of K 2 CO 3 . One main issue in using alumina-based-supported K 2 CO 3 is the high moisture uptake of the sorbent, which converts active sites of K 2 CO 3 to hydrated byproducts with a very low CO 2 capture capacity. To address this issue, the support was shelled with a less hydrophilic material using a core-shell technique. Six core-shell-structured supports were prepared using alumina-based cores (γ-alumina and boehmite), and TiO 2, ZrO 2, and SiO 2 shells. K 2 CO 3 was impregnated on each support and tested in a thermogravimetric analyzer over ten cycles. K 2 CO 3 /boehmite/TiO 2 showed the lowest moisture uptake and the highest surface area, and thus the best CO 2 capture performance. A semiempirical model was developed using a response surface methodology to optimize the CO 2 capture capacity of K 2 CO 3 /boehmite/TiO 2 . The optimal amounts of the operating parameters including carbonation temperature, carbonation time, and H 2 O-to-CO 2 flow rate ratio, were 61 °C, 40 min, and 1.15, respectively. The maximum CO 2 capture capacity at the optimal point was 6.61 mmol CO 2 /g K 2 CO 3, which is equal to 92% of the theoretical value. Therefore, the use of K 2 CO 3 /boehmite/TiO 2 at the obtained optimal condition is proposed as a suitable option for postcombustion 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".