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Record W4383198450 · doi:10.1063/5.0150919

<i>In situ</i> cathode-electrolyte interphase enables high cycling stability of Co-free Li-rich layered cathodes

2023· article· en· W4383198450 on OpenAlexafffund
Parisa Vahdatkhah, S.K. Sadrnezhaad, Oleksandr Voznyy

Bibliographic record

VenueAPL Energy · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaIran National Science FoundationH2020 European Research CouncilGovernment of OntarioUniversity of TorontoCanada Foundation for InnovationOntario Research Foundation
KeywordsCathodeElectrolyteMaterials scienceSpinelChemical engineeringCoatingBattery (electricity)InterphaseElectrodeNanotechnologyMetallurgyChemistry

Abstract

fetched live from OpenAlex

Despite the extensive research in Li-rich layered oxides (LLOs), which are promising candidates for high-energy density cathodes, their cycle life still cannot meet the real-world application requirements. The poor cycle performance arises from the electrolyte decomposition at high voltage, resulting in damage and subsequent surface-initiated conversion of the cathode from layered to spinel phase. This problem is even more challenging for Co-free LLO cathodes. Here, we report a one-pot synthesis of in situ carbonate-coated nanostructured Co-free LLO (Li2CO3@LLO) through a polyol-assisted method. This inorganic coating suppresses oxygen release, provides good Li–ion transport, and protects the cathode from adverse reactions with the electrolyte. The obtained material exhibits excellent long-term stability, with 76% capacity retention after 1000 cycles at a 0.2 C rate without any Co addition, demonstrating a path forward for using LLOs as a next-generation Li–ion battery cathode.

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)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.016
GPT teacher head0.259
Teacher spread0.243 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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