NdFeB Magnets Recycling via High-Pressure Selective Leaching and the Impurities Behaviors
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 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
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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