Enhanced CO<sub>2</sub> Adsorption on CeO<sub>2</sub>/SBA‐15: The Key Role of Oxygen Vacancies
Bibliographic record
Abstract
This study investigates the role of oxygen vacancies in the CO2 adsorption and desorption dynamics of SBA‐15:CeO2 nanocomposites synthesized by direct (DS) and postsynthesis (PS) methods. Physicochemical analyses reveal that the DS method increases the concentration of oxygen vacancies and structural defects within the CeO2 framework, which significantly boosts CO2 adsorption capacity and strengthens the gas‐surface interactions. Among the materials, S_Ce4.a demonstrates the highest adsorption capacity, reaching 29.4 mg g−1 at 25 °C and 10.7 mg g−1 at 70 °C. These results indicate a physisorption mechanism governed by both thermal conditions and oxygen vacancies. Furthermore, S_Ce4.a and S_Ce10.a. exhibit remarkable stability over 20 adsorption–desorption cycles. The findings suggest that a lower cerium oxide content provides more accessible adsorption sites, making these materials promising candidates for high‐performance. Overall, this work highlights synergistic interplay between oxygen vacancies and mesoporous structures, paving the way for the rational design of advanced materials for CO2 capture technologies.
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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".