Accelerated Carbonation of Brucite in Mine Tailings for Carbon Sequestration
Bibliographic record
Abstract
Atmospheric CO(2) is sequestered within ultramafic mine tailings via carbonation of Mg-bearing minerals. The rate of carbon sequestration at some mine sites appears to be limited by the rate of CO(2) supply. If carbonation of bulk tailings were accelerated, large mines may have the capacity to sequester millions of tonnes of CO(2) annually, offsetting mine emissions. The effect of supplying elevated partial pressures of CO(2) (pCO(2)) at 1 atm total pressure, on the carbonation rate of brucite [Mg(OH)(2)], a tailings mineral, was investigated experimentally with conditions emulating those at Mount Keith Nickel Mine (MKM), Western Australia. Brucite was carbonated to form nesquehonite [MgCO(3) · 3H(2)O] at a rate that increased linearly with pCO(2). Geochemical modeling indicated that HCO(3)(-) promoted dissolution accelerated brucite carbonation. Isotopic and aqueous chemistry data indicated that equilibrium between CO(2) in the gas and aqueous phases was not attained during carbonation, yet nesquehonite precipitation occurred at equilibrium. This implies CO(2) uptake into solution remains rate-limiting for brucite carbonation at elevated pCO(2), providing potential for further acceleration. Accelerated brucite carbonation at MKM offers the potential to offset annual mine emissions by ~22-57%. Recognition of mechanisms for brucite carbonation will guide ongoing work to accelerate Mg-silicate carbonation in tailings.
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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".