Improvement of Limestone-Based\nCO<sub>2</sub> Sorbents\nfor Ca Looping by HBr and Other Mineral Acids
Bibliographic record
Abstract
The effects of mineral-acid doping on the long-term reactivity\nof limestone-based sorbents for CO<sub>2</sub> capture was investigated\nin this work. Havelock (Canada), Longcliffe (U.K.), and Purbeck (U.K.)\nlimestones were doped with a range of mineral acids (HCl, HBr, HI,\nand HNO<sub>3</sub>), and the effects of concentration were also studied.\nDoped samples were subjected to repeated cycles of carbonation and\ncalcination in a fluidized-bed reactor. The experimental results showed\nthat HBr and HCl as dopants with a 0.167 mol % doping concentration\nsignificantly improved the long-term reactivity of Havelock and Longcliffe\nlimestones (doping with HI marginally improved the reactivity); however,\ndoping Havelock limestone with a similar concentration of HNO<sub>3</sub> reduced its CO<sub>2</sub> uptake. Purbeck limestone was\nnot significantly improved in reactivity by any dopant. Gas adsorption\nanalyses showed that sorbents have a very small surface area: less\nthan 4 m<sup>2</sup>/g. The pore size distribution appears to change\nsignificantly upon doping for those sorbents that are improved by\ndoping, and it is likely that optimizing the pore size distribution\nupon cycling is one reason for the enhanced reactivity observed. The\npore-size distributions of the initially calcined limestones and the\nchanges thereof with cycling and doping explain the differences in\nthe behaviors of the limestones.
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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 | 0.000 |
| 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) | 0.005 | 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".