Sustainable extraction and purification of REE and other metals from unsorted battery waste
Bibliographic record
Abstract
• Rare earth elements and metals leaching from unsorted battery waste. • REE recovery by selective precipitation. • Separation of zinc, manganese, cadmium, nickel, and cobalt by solvent extraction. • Zinc and cadmium recovery by electrodeposition. • Manganese, nickel and cobalt recovery by selective precipitation. Recycling the metals in household batteries usually requires a sorting stage, as the batteries are processed according to their chemistry. The aim of the present work was to define a technological process for extracting and recovering rare earth elements (REE) and other metals (Zn, Mn, Cd, Co, Ni) from unsorted battery waste. Two consecutive non-selective leaching steps using 1.34 M H 2 SO 4 and 0.45 g Na 2 S 2 O 5 /g powder with 100 g/L battery powder resulted in the solubilization of 84 % REE, 100 % Fe, 100 % Zn, 100 % Cd, 100 % Mn, 100 % Ni and 97 % Co. The REE is then recovered by precipitating double sulfates of REE, followed by a step of re-precipitating the REE as oxalate and calcining it to form a rare earth oxide concentrate (95 % purity). Iron is then removed by hydroxide precipitation at pH 4, while zinc is separated by solvent extraction (30 % Cyanex 272 + 5 % TBP, O/A ratio = 0.4, pH 2.5–2.8) and electrodeposited (99.96 % purity) at pH 2. Next, Cd and Mn are separated from the other metals by solvent extraction (30 % D2EHPA + 5 % TBP, O/A ratio = 2, pH 2.7–2.9) and selectively precipitated as CdS (83 % purity) at pH 7–8 and MnCO 3 at pH 9.5–10.5, which is then calcined to produce MnO (96 % purity). Cobalt is then separated from nickel by solvent extraction (10 % Cyanex 272 + 5 % TBP, O/A ratio = 1, pH 5.7–6.5) and both metals are recovered by oxalate formation and subsequently calcined to form cobalt oxide (79 % purity) and nickel oxide (97 % purity).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".