Sustainable Extraction of Critical Minerals from Waste Batteries: A Green Solvent Approach in Resource Recovery
Bibliographic record
Abstract
This strategic review examines the pivotal role of sustainable methodologies in battery recycling and the recovery of critical minerals from waste batteries, emphasizing the need to address existing technical and environmental challenges. Through a systematic analysis, it explores the application of green organic solvents in mineral processing, advocating for establishing eco-friendly techniques aimed at clipping waste and boosting resource utilization. The escalating demand for and shortage of essential minerals including copper, cobalt, lithium, and nickel are comprehensively analyzed and forecasted for 2023, 2030, and 2040. Traditional extraction techniques, including hydrometallurgical, pyrometallurgical, and bio-metallurgical processes, are efficient but pose substantial environmental hazards and contribute to resource scarcity. The concept of green extraction arises as a crucial step towards ecological conservation, integrating sustainable practices to lessen the environmental footprint of mineral extraction. The advancement of green organic solvents, notably ionic liquids and deep eutectic solvents, is examined, highlighting their attributes of minimal toxicity, biodegradability, and superior efficacy, thus presenting great potential in transforming the sector. The emergence of organic solvents such as palm oil, 1-octanol, and Span 80 is recognized, with advantageous low solubility and adaptability to varying temperatures. Kinetic (mainly temperature) data of different deep eutectic solvents are extracted from previous studies and computed with machine learning techniques. The coefficient of determination and mean squared error reveal the accuracy of experimental and computed data. In essence, this study seeks to inspire ongoing efforts to navigate impediments, embrace technological advancements including artificial intelligence, and foster an ethos of environmental stewardship in the sustainable extraction and recycling of critical metals from waste 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".