A review of lithium-ion battery recycling for enabling a circular economy
Bibliographic record
Abstract
With the rapid electrification of society, the looming prospect of a substantial accumulation of spent lithium-ion batteries (LIBs) within the next decade is both thought-provoking and alarming. Evaluating recycling strategies becomes a crucial pillar for sustainable resource management. To satisfy the demand for raw materials essential for battery production, harnessing the potential of existing resources within spent batteries is essential. Counting on these sources will expedite and secure the electrification of society and could potentially disrupt the dominance of countries with abundant resources, fostering a more equitable landscape. This review involves a comprehensive understanding of LIB's market and the resource requirements associated with electrification. Furthermore, sustainable urban mining would be accessible. This exploration unveils the barriers hindering efficient recycling despite LIB's pervasive and avoidable integration into various aspects of human life. Analyzing these hindrances facilitates the identification of systemic issues, strategic measures and innovative solutions. Addressing recycling challenges encompasses refining existing processes and even challenging the design of batteries to enhance recyclability. This holistic approach attracts attention from the electronic, automotive industries and encourages the development of more sustainable battery technologies. Review also empowers knowledge of recycling methodologies, which are instrumental in resolving current challenges and driving future advancements.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".