Selective leaching of neodymium from <scp>NdFeB</scp> carbonyl residues using hydrochloric acid
Bibliographic record
Abstract
Abstract An efficient recovery method of valuable metals from NdFeB carbonyl residues with ultra‐low rare earth content was developed. Selective leaching of NdFeB carbonyl residues was carried out using hydrochloric acid, and the leaching conditions were investigated and optimized. The experimental results showed that the optimum operating conditions for hydrochloric acid leaching were as follows: concentration of 2.5 mol/L, leaching temperature of 60°C, leaching time of 0.5 h, leaching solid–liquid ratio of 1:2 (g/mL), and rotational speed of 600 rpm during the leaching process. At this time, the leaching rate of neodymium in the carbonyl residues of NdFeB was 46.67%, and the leaching rate of iron was around 0.01%. The use of hydrochloric acid leaching had a high selectivity for Nd and Fe in the carbonyl residues. Neutralization and removal of impurities from the leach solution was carried out using NaOH and the pH of the leach solution was set to 4.0, at which time the removal of Fe was 76.52%. The neodymium in the leach solution was directly recovered by oxalic acid precipitation method, and the dosage of oxalic acid was 1.2 times of the theoretical dosage of oxalic acid, at which the recovery rate of neodymium was 96.38%, and the purity of the rare earth oxides obtained was 99.95%.
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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) | 0.000 | 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".