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Record W4386505883 · doi:10.1002/adma.202370259

Enabling Future Closed‐Loop Recycling of Spent Lithium‐Ion Batteries: Direct Cathode Regeneration (Adv. Mater. 36/2023)

2023· article· en· W4386505883 on OpenAlexaff
Tingzhou Yang, Dan Luo, Aiping Yu, Zhongwei Chen

Bibliographic record

VenueAdvanced Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceLithium (medication)SustainabilityBattery (electricity)CathodeClosed loopRegeneration (biology)Lithium-ion batteryWaste managementAutomotive engineeringElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Lithium-Ion Battery Recycling Sustainability suggests that the resources used for lithium-ion batteries are finite, and should be used conservatively and wisely. In article 2203218, Zhongwei Chen and co-workers outline and evaluate current direct cathode regeneration methods for industrialized battery recycling, which is anticipated as a flexible guide for closed-loop lithium-ion battery recycling with the aims of high efficiency, high safety, high environmental benefit, high economic return, and high sustainability.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.019
GPT teacher head0.279
Teacher spread0.260 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations82
Published2023
Admission routes1
Has abstractyes

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