Bioleaching for the recovery of rare earth elements from industrial waste: A sustainable approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it