CO<sub>2</sub> Looping Cycle Performance of a High-Purity Limestone after Thermal Activation/Doping
Bibliographic record
Abstract
The influence of thermal pretreatment on the performance of a high-purity limestone (La Blanca) during CO 2 capture cycles is investigated in this paper. This limestone was chosen for more detailed investigation because, in earlier research, it failed to show any favorable effect as a result of thermal pretreatment. Here, the original sample, with a particle size of 0.4−0.6 mm, and ground samples were thermally pretreated at 1000−1200 °C, for 6−24 h, and then subjected to several carbonation/calcination cycles in a thermogravimetric analyzer (TGA). This work shows that thermal pretreatment failed to produce a significant self-reactivation effect during CO 2 cycles, despite the use of a wide range of conditions during pretreatment (grinding, temperature, and pretreatment duration) as well as during cycling (CO 2 concentration and duration of the carbonation stage). Additional doping experiments showed that both high Na content and lack of Al in La Blanca limestone cause poor self-reactivation performance after thermal pretreatment. Scanning electron microscope−energy-dispersive X-ray (SEM−EDX) analyses also confirmed more pronounced sintering and loss of activity, which we believe are caused by the relatively high Na content. However, stabilization of sorbent particle morphology by Al can allow this limestone to show self-reactivation performance and higher conversions over a longer series of CO 2 cycles.
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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".