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, CO 2 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 CO 2 sequestration by mediating magnesium leaching and CO 2 absorption, and by favouring diffusion of gaseous CO 2 in pores deep inside the residues. Increasing temperature stimulated CO 2 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 CO 2 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 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) | 0.000 | 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 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".