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Record W4400298389 · doi:10.1051/e3sconf/202454302008

Recycling of spent electric vehicle (EV) batteries through the biohydrometallurgy process

2024· article· en· W4400298389 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsElectric vehicleEnvironmental scienceProcess (computing)Waste managementAutomotive engineeringEngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.285
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it