Technospheric Mining of Rare Earth Elements from Bauxite Residue (Red Mud): Process Optimization, Kinetic Investigation, and Microwave Pretreatment
Bibliographic record
Abstract
Abstract Some rare earth elements (REEs) are classified under critical materials, i.e., essential in use and subject to supply risk, due to their increasing demand, monopolistic supply, and environmentally unsustainable and expensive mining practices. To tackle the REE supply challenge, new initiatives have been started focusing on their extraction from alternative secondary resources. This study puts the emphasis on technospheric mining of REEs from bauxite residue (red mud) produced by the aluminum industry. Characterization results showed the bauxite residue sample contains about 0.03 wt% REEs. Systematic leaching experiments showed that concentrated HNO 3 is the most effective lixiviant. However, because of the process complexities, H 2 SO 4 was selected as the lixiviant. To further enhance the leaching efficiency, a novel process based on microwave pretreatment was employed. Results indicated that microwave pretreatment creates cracks and pores in the particles, enabling the lixiviant to diffuse further into the particles, bringing more REEs into solution, yielding of 64.2% and 78.7% for Sc and Nd, respectively, which are higher than the maximum obtained when HNO 3 was used. This novel process of “H 2 SO 4 leaching-coupled with-microwave pretreatment” proves to be a promising technique that can help realize the technological potential of REE recovery from secondary resources, particularly bauxite residue.
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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".