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Record W4362528545 · doi:10.1002/adma.202203218

Enabling Future Closed‐Loop Recycling of Spent Lithium‐Ion Batteries: Direct Cathode Regeneration

2023· review· en· W4362528545 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced Materials · 2023
Typereview
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of Waterloo
FundersWaterloo Institute for Nanotechnology, University of WaterlooNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsMaterials scienceRegeneration (biology)CathodeLithium (medication)IonClosed loopNanotechnologyWaste managementForensic engineeringElectrical engineeringEngineeringControl engineering

Abstract

fetched live from OpenAlex

The rapid proliferation of electric vehicles equipped with lithium-ion batteries (LIBs) presents serious waste management challenges and environmental hazards for recyclers after scrap. Closed-loop recycling contributes to the sustainable development of batteries and plays an important role in mitigating raw material shortages and supply chain risks. Herein, current direct cathode regeneration methods for industrialized recycling are outlined and evaluated. Different regeneration methods for spent cathode materials are summarized, which provide a new perspective for realizing closed-loop recycling of LIBs. A reference recycling route for retrofitting existing cathode production lines is proposed and minimizes the costs. In addition to promoting the industrialization of direct cathode recycling, the environmental, economic, and political benefits of battery recycling are also highlighted.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.052
GPT teacher head0.339
Teacher spread0.288 · 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