NdFeB Magnets Recycling via High-Pressure Selective Leaching and the Impurities Behaviors
Bibliographic record
Abstract
Abstract Global concerns about climate change are driving increased demand of electric vehicles for sustainable transportation and turbines in emerging energy solutions, where permanent magnets (PMs) and rare earth elements (REEs) play a critical role. However, global REEs recycling rates are only 3% and 8% for light and heavy REEs, respectively. This work proposes an effective approach to separate the REEs and iron via high-pressure selective leaching by low-concentrated nitric acid from the end-of-life NdFeB magnet and investigates the impurities behavior during the leaching and precipitation steps. The results from the optimized leaching conditions demonstrated over 95% REEs leaching efficiency with less than 0.3% Fe dissolution. Approximately 70% of Al and B were leached as well, while other elements (Co, Ni, Cu) had leaching efficiencies below 40%, leaving a hematite rich residue. Adjusting the pH removes Al and Fe in leachate but minimally affects Cu, Co, and Ni. Na 2 S addition is more effective against transition metals, but both methods result in around 10% REEs loss. Direct oxalate precipitation is suggested for the obtained leachate, which can yield over 97.5% REEs oxides with approximately 1.0% alumina, which is acceptable for magnet remanufacturing due to the aluminum content commonly found in magnets. The technology developed in this study offers opportunities for closed-loop recycling and remanufacturing of PMs, benefiting the environment, economy, and supply chain security. Graphical Abstract
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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) | 0.004 | 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".