Bioleaching of Uranium Tailings as Secondary Sources for Rare Earth Elements Production
Bibliographic record
Abstract
Tailings from inactive uranium mine sites represent a potential secondary source of rare earth elements (REEs). For this study, two mine tailings (DT and RAT) from restored uranium sites in Ontario, Canada, were used. Bioleaching experiments were conducted with a mix of native sulfur- and iron-oxidizing bacteria to test the solubilization of REEs, U and Th at different temperatures (20, 30 and 40 °C). The selective recovery of REEs from bioleaching solution was evaluated using different ion exchange resins. The mineralogical characterization revealed that DT tailings were mainly composed of quartz, pyrite, gypsum and silicates, whereas RAT tailings were mainly composed of quartz. The maximum solubilization of heavy and light REEs (HREEs and LREEs, respectively), Th and U reached 54%, 6%, 60% and 51% for RAT after 35 days at pH 2, T = 30 °C and pulp density = 10% (w/v). Higher extraction yields were obtained for DT, with 58% of HREEs, 14% of LREEs, 85% of Th and 89% of U solubilized under the same conditions. The use of Lewatit TP272 resin for the recovery of Sc (94%) and U (99%) followed by the Lewatit SP112 resin for the recovery of Th (57%) and REEs (81% LREEs and 65% HREEs) seemed a promising method for the co-extraction of the key elements from the bioleaching solution.
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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".