Advancing the circular economy by driving sustainable urban mining of end-of-life batteries and technological advancements
Bibliographic record
Abstract
This paper provides sustainable solutions for the urban mining of end-of-life (EOL) batteries and highlights their significant role in advancing the circular economy. Influenced by geopolitics and investment strategies, establishing a sustainable supply chain can create cost-saving opportunities while meeting the rising demand for battery materials. Urban mining, by recycling valuable metals from EOL batteries, can considerably reduce reliance on new raw materials by providing sustainable resources, thereby facilitating a cleaner energy transition. The research also emphasizes the importance of traceability and emerging innovations, such as the battery passport, which enhance transparency in the supply chain. Additionally, it explores the recycling industry's potential through techno-economic assessments to improve lithium-ion battery (LIB) recycling. Despite the challenges faced by different segments of the battery value chain, commercialization and technological advancements present promising opportunities for future development. The emergence of new battery systems or chemistries, such as sodium-ion, solid-state, and lithium-iron-phosphate batteries, must be considered in the further adaptation of existing plants. In conclusion, this paper discusses how the circular economy and urban mining can drive a sustainable, profitable, and resilient future for the LIB industry, ensuring an efficient and environmentally sound approach to the battery revolution.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".