CO<sub>2</sub> Capture with Mg-, Al-, and Zr- Assisted CaO-Based Sorbents in the Calcium Looping Process Under Mild and Realistic Conditions
Bibliographic record
Abstract
The calcium looping (CaL) process is a promising carbon capture technology for CO 2 capture from point source emitters. A key challenge in the CaL process is the loss of sorbent capacity over successive capture-regeneration cycles due to sintering, which affects long-term stability. This study addresses this issue by a novel approach of incorporating MgO, Al 2 O 3, and ZrO 2 as promoters into calcium-based sorbents synthesized using the solution combustion synthesis (SCS) method. Sorbents were developed in mono-, bi-, and trimetallic configurations using soluble metal nitrates as precursors. Among the tested sorbents, Ca/(Zr–Al) demonstrated the highest CO 2 uptake of 0.46 g of CO 2 /g of sorbent, while Ca/(Mg–Zr–Al) achieved 0.43 g of CO 2 /g of sorbent. Both configurations exhibited exceptional stability, maintaining over 90% of their initial capacity after 50 cycles at elevated temperatures. These results highlight the effectiveness of bi- and trimetallic sorbents in enhancing the performance and durability of the CaL process.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".