Recovery of critical metals from carbonatite-type mineral wastes: Geochemical modeling investigation of (bio)hydrometallurgical leaching of REEs
Bibliographic record
Abstract
Abstract Rare earth elements (REEs) are typically found in low concentrations within natural rocks that make up mine tailings, such as carbonates in association with silicates within carbonatite igneous rocks, so it is of interest to develop (bio)hydrometallurgical ways to liberate them from the silicate matrix. This work investigated, through geochemical modeling, the extraction of europium and ytterbium carbonates from rocks containing one of four silicates (chrysotile, forsterite, montmorillonite, and phlogopite) via chemical (mineral acid) or biological (organic acid) leaching. The results indicated conditions that led to either congruent or incongruent dissolution of the mineral phases and the formation of transient mineral phases. Chemical leaching models suggest that REE carbonates are recoverable in one-step leaching from forsterite and chrysotile rocks, while they are recoverable in a secondary leaching step from montmorillonite and phlogopite rocks. Gibbsite as a transient phase is shown to complicate REE recovery, potentially requiring reactive extraction. REEs have the potential to be recovered from silicate rocks via chemoorganotrophic bioleaching, but the process configuration would differ depending on the predominant minerals that make up the rock, and the type of REE present in it.
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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 | 0.001 |
| 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 | 0.001 | 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".