New tools for stimulating dissolution and carbonation of ultramafic mining residues
Bibliographic record
Abstract
The carbonation of chrysotile and nickel mining residues was studied under ambient laboratory conditions to assess their response under various potential field conditions (pore saturation, watering episodes, temperature, CO2 diffusion, and dissolved oxygen) and to a variety of natural and chemical enhancers (sulfide minerals, brucite, chelate ligands, ionic liquids, and carbonic anhydrase enzyme). Watering of the residues to achieve partial pore saturation was critical for optimal ambient CO2 sequestration by mediating magnesium leaching and CO2 absorption, and by favouring diffusion of gaseous CO2 in pores deep inside the residues. Increasing temperature stimulated CO2 uptake whereas dissolved oxygen triggered undesirable oxidative precipitation and passivation by iron (III) hydroxides. The latter effect was attenuated through addition of depassivation chelates, which impeded iron precipitation and enhanced carbonation under ambient conditions. Pyrite and pyrrhotite, as natural acid generators, failed to improve ambient carbonation by fostering iron passivation to the detriment of mining residue dissolution whereas the use of carbonic anhydrase inhibited formation of magnesium carbonates. Ionic liquids were found suitable for magnesium extraction under alkaline conditions but inefficient for inducing carbonate precipitation. The lower rate of carbonation with hydrophobic ionic liquid‐water mixtures was ascribed to a slower gas‐liquid mass transfer of CO2 to the liquid, which primarily prompted a loss of gas‐liquid interfacial area due to viscous effects and bubble coalescence.
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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".