Study on the influence mechanism of carbothermal reduction and selective leaching of valuable metals in spent lithium batteries
Bibliographic record
Abstract
Abstract BACKGROUND Exploring an innovative method for precious metal recovery from spent batteries, our study combines hydrometallurgy and pyrometallurgy, focusing on pivotal carbothermal reduction. This crucial phase enhances the leachability of metals by significantly reducing their valence. To evaluate the effect of novel carbon (C) materials on this reduction process, our study synthesized two types of nanoporous Cs, Al‐PCP‐800 and ZIF‐8‐800, found in electrode materials. Their carbothermal reduction efficacy was compared to activated (A)C using thermogravimetric analysis. RESULTS The experimental findings revealed that ZIF‐8‐800 exhibits superior reducibility, initiating and reaching peak weight loss rate at a lower temperature of 692.01 °C with a peak of 32.27%/°C. Under the optimized conditions (roasting temperature 750 °C, time 3 h, C content 20%, 2.75 mol L−1 H3PO4 concentration, 40 °C leaching temperature, liquid‐to‐solid ratio of 6 mL g−1, leaching time 10 min) the leaching efficiency of lithium (Li) and manganese (Mn) reached 100%, indicating complete extraction with ZIF‐8‐800. However, cobalt (Co) and nickel (Ni) leaching with ZIF‐8‐800 was low (3.22%, 2.06%). Al‐PCP‐800 showed slightly less leaching efficiency for Li and Mn, but higher efficiency for Co and Ni. Activated C led to incomplete Li and Mn extraction, with higher Co and Ni leaching, diminishing the process's selectivity. CONCLUSION The study confirms that the C material in carbothermal reduction significantly impacts metal leaching efficiency and selectivity. ZIF‐8‐800 was the most effective for Li and Mn leaching, outperforming Al‐PCP‐800 and AC in selectivity. This underscores ZIF‐8‐800's potential to improve metal recovery from spent batteries. © 2024 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).
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.
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".