Recycling of spent electric vehicle (EV) batteries through the biohydrometallurgy process
Bibliographic record
Abstract
Lithium-ion batteries constitute a primary component of electric vehicles (EV). The proliferation of EV on a global scale is expected to result in a rise in the quantity of spent EV batteries. The spent EV batteries comprise various heavy metals that possess a higher content than naturally available ores. These metals are valuable and have the potential to adversely affect the environment and human health if not managed appropriately. Conventional recycling techniques, such as pyrometallurgical and hydrometallurgical processes, have proven to be effective in the recovery of precious metals from used EV batteries. These techniques are used to recycle wasted EV batteries. Nonetheless, it should be noted that these processes are associated with a considerable cost, require high levels of energy consumption, present challenges in terms of regulation, and produce byproducts that can be classified as secondary pollutants. Biohydrometallurgy is a component of the discipline of hydrometallurgy that is widely recognized or thought of as an ecologically friendly and cost-effective extraction metallurgical technique as an alternative of extracting and recovering valuable metals from spent EV batteries. This approach involves the utilization of microorganisms. The present study employs a consortium of microorganisms comprising fungi, chemolithotrophic bacteria, mixotrophic bacteria, and acidophilic bacteria. These microorganisms have demonstrated their proficiency in metal recovery by generating acids and biosurfactants and utilizing ferrous ions and sulfur as energy sources. This article presents a review of biohydrometallurgical techniques as potential strategies for cost-effective and environmentally friendly technologies for the recycling of spent EV batteries. These techniques encompass the fundamental principles of biohydrometallurgy, in addition to the roles that microbes play in biohydrometallurgy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".