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 CO2 capture performance of K2CO3. One main issue in using alumina-based-supported K2CO3 is the high moisture uptake of the sorbent, which converts active sites of K2CO3 to hydrated byproducts with a very low CO2 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 TiO2, ZrO2, and SiO2 shells. K2CO3 was impregnated on each support and tested in a thermogravimetric analyzer over ten cycles. K2CO3/boehmite/TiO2 showed the lowest moisture uptake and the highest surface area, and thus the best CO2 capture performance. A semiempirical model was developed using a response surface methodology to optimize the CO2 capture capacity of K2CO3/boehmite/TiO2. The optimal amounts of the operating parameters including carbonation temperature, carbonation time, and H2O-to-CO2 flow rate ratio, were 61 °C, 40 min, and 1.15, respectively. The maximum CO2 capture capacity at the optimal point was 6.61 mmol CO2/g K2CO3, which is equal to 92% of the theoretical value. Therefore, the use of K2CO3/boehmite/TiO2 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".