Aeriometallurgical Extraction of Rare Earth Elements from a NdFeB Magnet Utilizing Supercritical Fluids
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
There is a global need for efficient and environmentally sustainable processes to close the life cycle loop of waste electrical and electronic equipment (WEEE) through recycling. Conventional WEEE recycling processes are based upon pyrometallurgy or hydrometallurgy. The former is energy-intensive and generates greenhouse gas (GHG) emissions, while the latter relies on large volumes of acids and organic solvents, thus generating hazardous wastes. Here, a novel “aeriometallurgical” process was developed to recycle critical rare earth elements, namely, neodymium (Nd), praseodymium (Pr), and dysprosium (Dy), from postconsumer NdFeB magnets utilized in wind turbines. The new process utilizes supercritical CO2 as the solvent, which is safe, inert, and abundant, along with the tributyl-phosphate–nitric acid (TBP–HNO3) chelating agent and 2 wt % methanol as a cosolvent. Nd (94%), Pr (91%), and Dy (98%) extraction was achieved with only 62% iron (Fe) coextraction and minimal waste generation. Fundamental investigations into the extraction mechanism demonstrated that metal ion charge has an important impact on the extraction efficiency. Fundamental investigations indicate that extraction proceeds by corrosion of the magnet particle’s surface layer. This work demonstrates that supercritical fluid extraction would find widespread applicability as a cleaner, a more sustainable option to recycle value metals from end-of-life products to enable the circular economy.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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