Bioleaching for the recovery of rare earth elements from industrial waste: A sustainable approach
Bibliographic record
Abstract
• Rare Earth Elements (REEs) are vital for modern technology and renewable energy. • Industrial waste is a promising alternative for REEs. • Conventional REE extraction methods are environmentally harmful. • Bioleaching offers a sustainable, eco-friendly method for REEs recovery. • Bioleaching requires optimization for higher REE recovery efficiency. Rare earth elements (REEs) play an important role in various high-tech technologies, including renewable energy systems, electronics, and catalytic converters. The increasing demand for REEs, coupled with their limited and geographically constrained natural deposits, necessitates the exploration of alternative sources. Industrial wastes including electronic waste, phosphogypsum, and coal fly ash are rich in REEs and present a promising reservoir for these critical elements. Typically, REE extraction is carried out using conventional methods such as solvent extraction, roasting, and acid leaching. However, these methods are not eco-friendly and pose environmental challenges, such as dust generation, high energy requirements, and harmful gas emissions. Therefore, there is a pressing need to explore alternative, eco-friendly methods to overcome these challenges. Bioleaching offers a sustainable solution to solubilize REEs from industrial waste, presenting a greener approach to resource recovery. This review comprehensively discusses the bioleaching of REEs from various industrial waste streams. It critically discusses the challenges faced in bioleaching, such as process scalability and efficiency, and explores prospects, emphasizing the potential of bioleaching to revolutionize the REE supply chain.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".