CO<sub>2</sub> Capture Performance of Core/Shell CaO-Based Sorbent Using Mesostructured Silica and Titania in a Multicycle CO<sub>2</sub> Capture Process
Bibliographic record
Abstract
Carbon dioxide (CO 2 ) capture is a process that can significantly reduce the amount of CO 2 in the atmosphere. In this study, several solid sorbents were examined for their CO 2 capturing performance over 30 carbonation–calcination cycles. The sorbents included natural calcined Cadomin limestone (denoted as CD), hydrated calcined Cadomin pellets (denoted as CP), core/shell sorbets with CD and CP as cores, and mesostructured silica (denoted as CD@Si and CP@Si, respectively) and titania (denoted as CD@Ti and CP@Ti) as shells. The core/shell sorbents were prepared with a protective porous shell using the mesoporous silica and titania layers. The surface morphology and porosity of all sorbents were qualified using scanning electron microscopy and were quantified using nitrogen physisorption. X-ray diffraction was also used to identify the crystal phase composition of the sorbents before and after calcination. The CP@Ti pellets showed the best performance in the retention of CO 2 uptake over 30 cycles with an activity loss of 50.9%. This is attributed to the formation of a protective layer of thermally stable mesoporous titania using a sol–gel method, which prevented the aggregation of CaO crystals and sorbent sintering. Although the modified core/shell sorbents exhibited an improvement in maintaining the stability of the cyclic operation compared to natural limestone, further study is needed to understand the core/shell sintering phenomenon at high temperatures using other novel materials.
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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".