Sustainable recycling of spent Li‐ion batteries through waste pine needle‐assisted carbothermal reduction for lithium recovery
Bibliographic record
Abstract
Abstract As the global dependence on lithium‐ion batteries continues to grow, the challenge of recovering valuable metals from spent batteries has become increasingly crucial. Furthermore, the inappropriate disposal of spent batteries not only affects the loss of critical metals but also poses significant environmental hazards. To address these issues and develop sustainable recycling methods, the use of renewable and environmentally friendly materials is essential. Therefore, a biomass‐based energy‐intensive reduction method is proposed to recover lithium from spent lithium‐ion batteries. Here, waste pine needle was used as a biomass for carbothermal reduction process to convert lithium in the spent cathode powder into Li 2 CO 3 , while the transition metals were reduced to Ni, Co/CoO, and MnO. The effect of carbothermal reduction process parameters like temperature, mass ratio of pine needle and spent cathode powder, and residence time on leaching efficiency and reduction efficiency along with process modelling and optimization, was done using response surface methodology. Overall, this study provides an energy efficient approach to recycle spent LIBs using waste pine needles, enabling selective lithium recovery from spent lithium‐ion batteries.
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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".