Sustainable extraction and purification of REE and other metals from unsorted battery waste
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
• 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).
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 | 0.000 | 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 it