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Record W4406758701 · doi:10.1016/s1003-6326(24)66679-3

Recycling technologies of spent lithium-ion batteries and future directions: A review

2025· review· en· W4406758701 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

VenueTransactions of Nonferrous Metals Society of China · 2025
Typereview
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of Alberta
FundersScience and Technology Program of Hunan ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsLithium (medication)IonMaterials scienceEngineering physicsNanotechnologyProcess engineeringEngineeringPsychologyPhysics

Abstract

fetched live from OpenAlex

Lithium-ion batteries (LIBs) are the most popular energy storage devices due to their high energy density, high operating voltage, and long cycle life. However, green and effective recycling methods are needed because LIBs contain heavy metals such as Co, Ni, and Mn and organic compounds inside, which seriously threaten human health and the environment. In this work, we review the current status of spent LIB recycling, discuss the traditional pyrometallurgical and hydrometallurgical recovery processes, and summarize the existing short-process recovery technologies such as salt-assisted roasting, flotation processes, and direct recycling. Finally, we analyze the problems and potential research prospects of the current recycling process, and point out that the multidisciplinary integration of recycling will become the mainstream technology for the development of spent LIBs.

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 score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.308
Teacher spread0.282 · 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