Optimization of metals and rare earth elements leaching from spent Ni-MH batteries by response surface methodology
Why this work is in the frame
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Bibliographic record
Abstract
The rechargeable battery market has almost doubled in 15 years. Regardless of the type of batteries, their limited lifespan means that sooner or later they will constitute a mass of waste whose management is problematic as their content is high in elements and metals of high economic interest, but also toxic to the environment. This project is to optimize the solubilization conditions for rare earth elements (REEs) and other metals from waste nickel-metal hydride (Ni-MH) batteries. The Ni-MH battery powder used contained the following main elements: Ni (548 g/kg), La (45 g/kg), Co (32 g/kg), Zn (22 g/kg), Nd (15 g/kg), Sm (12 g/kg), and Ce (11 g/kg). The metals were solubilized in the presence of sulfuric acid. Acid concentration, solids concentration, leaching time, and temperature were optimized using the Box–Behnken design methodology. The optimal conditions identified are an H2SO4 concentration of 2 M, a S:L ratio of 10% (w:v), a leaching temperature of 60°C and a reaction time of 90 min. These conditions make it possible to solubilize 81% Ni, 99% Co, and 70% REEs, while the mathematical model predicted 83% Ni, 100% Co, and 80% REEs respectively. The process was also operated in counter-current leaching mode with the optimal parameters. The high solubilized yields obtained after five loops for all metals, REE and the significant reduction of water consumption confirm that this process leaching can be apply for industrial application.
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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 | 0.000 |
| 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