CaO-Based Pellets Supported by Calcium Aluminate Cements for High-Temperature CO<sub>2</sub> Capture
Bibliographic record
Abstract
The development of highly efficient CaO-based pellet sorbents, using inexpensive raw materials (limestones) or the spent sorbent from CO2 capture cycles, and commercially available calcium aluminate cements (CA-14, CA-25, Secar 51, and Secar 80), is described here. The pellets were prepared using untreated powdered limestones or their corresponding hydrated limes and were tested for their CO2 capture carrying capacities for 30 carbonation/calcination cycles in a thermogravimetric analyzer (TGA). Their morphology was also investigated by scanning electron microscopy (SEM) and their compositions before and after carbonation/calcination cycleswere determined by X-ray diffraction (XRD). Pellets prepared in this manner showed superior behavior during CO2 capture cycles compared to natural sorbents, with the highest conversions being > 50% after 30 cycles. This improved performance was attributed to the resulting substructure of the sorbent particles, i.e., a porous structure with nanoparticles incorporated. During carbonation/calcination cycles mayenite (Ca12Al14O33) was formed, which is believed to be responsible for the favorable performance of synthetic CaO-based sorbents doped with alumina compounds. An added advantage of the pellets produced here is their superior strength, offering the possibility of using them in fluidized bed combustion (FBC) systems with minimal sorbent loss due to attrition.
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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) | 0.000 | 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 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".