Aeriometallurgical Extraction of Rare Earth Elements from a NdFeB Magnet Utilizing Supercritical Fluids
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 CO 2 as the solvent, which is safe, inert, and abundant, along with the tributyl-phosphate–nitric acid (TBP–HNO 3 ) 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.
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.
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) | 0.000 | 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 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".