Acceleration of Iron-Rich Olivine CO2 Mineral Carbonation and Utilization for Simultaneous Critical Nickel and Cobalt Recovery
Bibliographic record
Abstract
CO2 mineral carbonation is an important method to sequester carbon dioxide (CO2) in the form of stable mineral carbonates for permanent storage. The slow kinetics of carbonation, especially for iron-rich olivine, is the major challenge for potential application. This work proposes methods to accelerate the mineral carbonation process of different materials in the general mineral grouping of divalent metals–olivine for simultaneous nickel and cobalt recovery. It is found that nickel-olivine is facile for mineral carbonation compared to ferrous and magnesium olivine. Ferrous olivine is the most difficult form of olivine to carbonate as illustrated in both thermodynamics and experimental test results. The increase in iron content in olivine inhibits the CO2 mineral carbonation process by forming an iron-silica-rich passivation interlayer. The use of a reducing gas or reagent can enhance the mineral carbonation of olivine probably through hindering oxidation of Fe(Ⅱ). The addition of sodium nitrilotriacetate (NTA) as a metal complexing agent is much more efficient for the acceleration than usage of a reducing atmosphere. The combination of sodium bicarbonate/CO2 gas supply and NTA can enhance the diffusion of all divalent metal ions from the reacting olivine surface, thereby limiting the formation of the passivation interlayer. Meanwhile, highly selective nickel and cobalt leaching can be simultaneously achieved along with the CO2 mineral carbonation, 94% nickel, and 92% cobalt leaching as well as 47% mineral carbonation versus only 10% iron and 1% magnesium leached in 2 h. This work provides a novel direction to achieve critical metals recovery with accelerated mineral carbonation process.
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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".