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Record W4386268922 · doi:10.21203/rs.3.rs-3261393/v1

Cathode regeneration processes enabled transition from spent batteries to lithium-ion alternatives

2023· preprint· en· W4386268922 on OpenAlexafffund
Zhongwei Chen, Tingzhou Yang, Dan Luo, Xinyu Zhang, Shihui Gao, Rui Gao, Qianyi Ma, Hey Woong Park, Tyler Or, Yongguang Zhang

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsUniversity of Waterloo
FundersSuzhou Institute of Nanotechnology, Chinese Academy of SciencesUniversity of WaterlooNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsLithium (medication)Energy storageCathodeBattery (electricity)Electric vehicleSustainable energyWaste managementScalabilityEnvironmental scienceMaterials scienceProcess engineeringNanotechnologyEngineeringComputer scienceElectrical engineeringRenewable energy

Abstract

fetched live from OpenAlex

<title>Abstract</title> The development of electric vehicles is accelerating the world's transition to sustainable energy, but the millions of end-of-life electric vehicles generated over the next decade pose serious waste management challenges, especially the recycling of spent batteries. Here we propose two cathode regeneration processes to enable scalable and affordable recycling of spent lithium-ion batteries (LIBs) into brand-new LIBs and their alternatives, such as sodium-ion batteries (SIBs). The regenerated layered oxide materials deliver a reversible area capacity of up to 2.73 mAh cm<sup>− 2</sup> with excellent structural stability for LIBs, while obtained cyanide complex manifests an 83.7% retention over 2000 cycles for SIBs and robust cycling stability for pouch cells. By contrast, the manufacturing costs for LIBs and SIBs using our regenerated materials have dropped to an all-time low of $47.16 and $37.49 per kWh, with conspicuous reductions in energy consumption, water consumption, and harmful gas emissions. Our sustainable battery recycling designs pave the way for the transition to more sustainable energy storage technologies, enabling post-LIBs with regenerated materials.

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 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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.107
GPT teacher head0.391
Teacher spread0.284 · 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 teacher head, not a consensus.

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

Citations0
Published2023
Admission routes2
Has abstractyes

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