Accelerating Mineral Carbonation Using Carbonic Anhydrase
Bibliographic record
Abstract
Carbonic anhydrase (CA) enzymes have gained considerable attention for their potential use in carbon dioxide (CO2) capture technologies because they are able to catalyze rapidly the interconversion of aqueous CO2 and bicarbonate. However, there are challenges for widespread implementation including the need to develop mineralization process routes for permanent carbon storage. Mineral carbonation of highly reactive feedstocks may be limited by the supply rate of CO2. This rate limitation can be directly addressed by incorporating enzyme-catalyzed CO2 hydration. This study examined the effects of bovine carbonic anhydrase (BCA) and CO2-rich gas streams on the carbonation rate of brucite [Mg(OH)2], a highly reactive mineral. Alkaline brucite slurries were amended with BCA and supplied with 10% CO2 gas while aqueous chemistry and solids were monitored throughout the experiments (hours to days). In comparison to controls, brucite carbonation using BCA was accelerated by up to 240%. Nesquehonite [MgCO3·3H2O] precipitation limited the accumulation of hydrated CO2 species, apparently preventing BCA from catalyzing the dehydration reaction. Geochemical models reproduce observed reaction progress in all experiments, revealing a linear correlation between CO2 uptake and carbonation rate. Data demonstrates that carbonation in BCA-amended reactors remained limited by CO2 supply, implying further acceleration is possible.
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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".