Reductive Leaching Investigation of Li-NMC Cathode Material Related to Spent Battery Recycling
Bibliographic record
Abstract
The electrification of transportation and proliferation of portable electronics are rapidly increasing, leading to a significant demand for Li-ionLithium-ion battery batteriesBattery (LIBs)LIB batteries, specifically NMCNMC-type batteriesBattery. This surge in demand for Li, Ni, Mn, and Co elements is unsustainable if it relied solely on mining. Therefore, the need to prioritize lithiumLithium-ionLithium-ion battery battery recyclingBattery recycling in the coming decades is not just important, but urgent. While the hydrometallurgyHydrometallurgy process is considered greener for metal recoveryMetal recovery compared to pyrometallurgy, it requires further enhancements to address sustainabilitySustainability and environmental issues. These include reducing excessive acid usage and concomitant waste minimization (acidic wastewater and Na2SO4 production). To address these issues, the present work focused on improving the leachingLeaching efficiency of spent NMCNMC cathode material using a minimum amount of acid. We carried out the reductive leachingReductive leaching of spent Li-NMCNMC in H2SO4 media in a redox-controlled single feed semi-batch reactor through the regulated addition of H2O2.
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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) | 0.001 | 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".