Enhanced CO2 mineralization and selective critical metal extraction from olivine and laterites
Bibliographic record
Abstract
CO 2 mineralization is a significant method for effective CO 2 emission control because of formation of stable carbonate products and the availability of abundant mineral resources. The economic concern of the direct aqueous CO 2 mineralization prevents the application to global warming mitigation. This work discovers that the CO 2 mineralization of olivine can be significantly enhanced by using nitriloacetate (NTA) salt due to the competitive complexation and extraction of nickel and cobalt versus carbonate precipitation. The highly selective critical metal extraction can be also achieved and even considerably enhanced by using NTA compared to using ethylenediaminetetraacetate (EDTA) salt. Nearly 90% CO 2 mineralization, nickel and cobalt extraction can be achieved and is suitable for both nickel-poor olivine and nickel-rich saprolite and limonite laterites. The recovery of nickel and cobalt makes the CO 2 mineralization process favourable in both economics and technical outcome. The enhanced CO 2 mineralization process can potentially earn $29, $557, and $221 net benefits for each t olivine, saprolite, and limonite, respectively. The success of the CO 2 mineralization in technical and economic feasibility can potentially make the CO 2 mineralization widely applicable and can make significant contributions to sustainable minerals engineering and resources management, and enhanced supply of critical metals and CO 2 storage.
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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".