Carbon mineralization with concurrent critical metal recovery from olivine
Bibliographic record
Abstract
Carbon dioxide utilization for enhanced metal recovery (EMR) during mineralization has been recently developed as part of CCUS (carbon capture, utilization, and storage). This paper describes fundamental studies on integrating CO 2 mineralization and concurrent selective metal extraction from natural olivine. Nearly 90% of nickel and cobalt extraction and mineral carbonation efficiency are achieved in a highly selective, single-step process. Direct aqueous mineral carbonation releases Ni 2+ and Co 2+ into aqueous solution for subsequent recovery, while Mg 2+ and Fe 2+ simultaneously convert to stable mineral carbonates for permanent CO 2 storage. This integrated process can be completed in neutral aqueous solution. Introduction of a metal-complexing ligand during mineral carbonation aids the highly selective extraction of Ni and Co over Fe and Mg. The ligand must have higher stability for Ni-/Co- complex ions compared with the Fe(II)-/Mg- complex ions and divalent metal carbonates. This single-step process with a suitable metal-complexing ligand is robust and utilizes carbonation processes under various kinetic regimes. This fundamental study provides a framework for further development and successful application of direct aqueous mineral carbonation with concurrent EMR. The enhanced metal extraction and CO 2 mineralization process may have implications for the clean energy transition, CO 2 storage and utilization, and development of new critical metal resources.
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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".