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Opportunities to Improve Recycling of Automotive Lithium Ion Batteries

2015· article· en· W406925390 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

VenueProcedia CIRP · 2015
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsQueen's University
Fundersnot available
KeywordsAutomotive industryLithium (medication)Automotive engineeringIonMaterials scienceManufacturing engineeringEngineeringAerospace engineeringChemistryPsychology

Abstract

fetched live from OpenAlex

A high recovery of lithium from recycled lithium ion batteries (LIBs) is essential to ensure the growth and sustainability of the electrical vehicle market. Without recycling, lithium demand is predicted to outstrip supply in 2023. Current industrial processes are focused on recovering cobalt and other valuable metals because, given lithium's current low price, it is economically unfavorable to recover it. As part of our efforts to create a process where the recovery of lithium is economically viable we have analyzed the current industrial processes. We have determined that, when applied to recycling automotive LIBs, they are needlessly energy intensive and complicated. In these processes whole LIBs are incinerated, cryogenically cooled, or shredded under an inert atmosphere in order to make their cells safe to open. Instead of such extreme measures, LIBs can be disassembled by automated processes, which recovers valuable electronics for reuse, their cells can be discharged, which recovers residual energy, and then can be opened safely in air.

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 score0.359
Threshold uncertainty score0.415

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)0.0000.000

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 head0.060
GPT teacher head0.276
Teacher spread0.216 · 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