Artificial Laterite from Acid Leaching of Ultramafic Rocks: Mobilization, Enrichment, and Extraction of Critical Metals
Bibliographic record
Abstract
Natural weathering of ultramafic rocks produces laterites that host nickel (Ni) and cobalt (Co) which are critical to a renewable energy transition. Here, we performed sulfuric acid leaching on an ultramafic rock, which produces an iron (Fe)-rich residue that concentrates Ni and Co on laboratory time scales, herein termed artificial laterite . Nickel and Co in the artificial laterite are up to 5 – 7 times enriched relative to the raw material and close to their cutoff grades of natural laterite ores. The most Ni-and Co-rich artificial laterite is dominated by poorly crystalline ferrihydrite as revealed by synchrotron-based powder diffraction (PD) and X-ray absorption spectroscopy (XAS, Fe K-edge). By reacting this artificial laterite with aqueous Fe(II) at circumneutral pH and ambient temperature, release of structurally bound Ni and Co is markedly enhanced. Followed by mild acid extraction, total recovery of Ni and Co is around 80%. Meanwhile, impurities such as coprecipitated silica with ferrihydrite would not negatively impact metal release. Our work demonstrates that acid leaching of ultramafic source rocks can generate artificial laterites that are more reactive than their natural counterparts. Furthermore, leaching of these rocks releases magnesium (Mg), an important cation for carbon mineralization, potentially offsetting carbon emissions during metal extraction.
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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".