Recent advances in the approaches to recover rare earths and precious metals from <scp>E</scp> ‐waste: A mini‐review
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
Abstract In the present day, with the rapid rate of advancements in technology, gadgets become obsolete very fast. The chase to keep up with the latest technologies diminishes the gadget's lifespan considerably. Consequently, they are discarded within a short time after their production, resulting in electronic waste (E‐waste) being the fastest growing waste stream globally, with an annual production rate of 2.44 million short tonnes. The metals present in such E‐waste provide several attractive properties, rendering them crucial in several applications as components of electronic and electrical devices. The major roadblock faced by mankind today is an effective technology with high recovery, low cost, and minimal environmental impact to recycle such electronic waste. In this mini‐review, we elucidate the various recycling routes for metal extraction from waste and recent advances in the same. We have attempted to highlight the recent trends adopted by various researchers to recycle and extract valuable metals and rare earths from E‐waste. Finally, the challenges and prospects in the extraction of rare earths and precious metals for E‐waste research have been clearly brought out and suggestions have been made for future work.
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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