Improvement of Limestone-Based CO<sub>2</sub> Sorbents for Ca Looping by HBr and Other Mineral Acids
Bibliographic record
Abstract
The effects of mineral-acid doping on the long-term reactivity of limestone-based sorbents for CO 2 capture was investigated in this work. Havelock (Canada), Longcliffe (U.K.), and Purbeck (U.K.) limestones were doped with a range of mineral acids (HCl, HBr, HI, and HNO 3 ), and the effects of concentration were also studied. Doped samples were subjected to repeated cycles of carbonation and calcination in a fluidized-bed reactor. The experimental results showed that HBr and HCl as dopants with a 0.167 mol % doping concentration significantly improved the long-term reactivity of Havelock and Longcliffe limestones (doping with HI marginally improved the reactivity); however, doping Havelock limestone with a similar concentration of HNO 3 reduced its CO 2 uptake. Purbeck limestone was not significantly improved in reactivity by any dopant. Gas adsorption analyses showed that sorbents have a very small surface area: less than 4 m 2 /g. The pore size distribution appears to change significantly upon doping for those sorbents that are improved by doping, and it is likely that optimizing the pore size distribution upon cycling is one reason for the enhanced reactivity observed. The pore-size distributions of the initially calcined limestones and the changes thereof with cycling and doping explain the differences in the behaviors of the limestones.
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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".