Sustainable Recycling of Lithium-Ion Battery Cathodes: Life Cycle Assessment, Technologies, and Economic Insights
Bibliographic record
Abstract
Rapid growth of electric vehicles has increased demand for lithium-ion batteries (LIBs), raising concerns regarding their end-of-life management. This study comprehensively evaluates the closed-loop recycling of cathode materials from spent LIBs by integrating life cycle assessment (LCA), technoeconomic analysis, and technological comparison. Typical approaches-including pyrometallurgy, hydrometallurgy, and other processes such as organic acid leaching and in situ reduction roasting-are systematically reviewed. While pyrometallurgy offers scalability, it is hindered by high energy consumption and excessive greenhouse gas emissions. Hydrometallurgy achieves higher metal recovery rates with better environmental performance but requires complex chemical and wastewater management. Emerging methods and regeneration techniques such as co-precipitation and sol-gel synthesis demonstrate potential for high-purity material recovery and circular manufacturing. LCA results confirm that recycling significantly reduces GHG emissions, especially for high-nickel cathode chemistry. However, the environmental benefits are affected by upstream factors such as collection, disassembly, and logistics. Technoeconomic simulations show that profitability is strongly influenced by battery composition, regional cost structures, and collection rates. The study highlights the necessity of harmonized LCA boundaries, process optimization, and supportive policy frameworks to scale environmentally and economically sustainable LIB recycling, ensuring long-term supply security for critical battery materials.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".