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Record W4310640604 · doi:10.1002/adma.202207234

Manipulating Charge‐Transfer Kinetics of Lithium‐Rich Layered Oxide Cathodes in Halide All‐Solid‐State Batteries

2022· article· en· W4310640604 on OpenAlexafffund
Ruizhi Yu, Changhong Wang, Hui Duan, Ming Jiang, Anbang Zhang, Adam Fraser, Jiaxuan Zuo, Yanlong Wu, Yipeng Sun, Yang Zhao, Jianwen Liang, Jiamin Fu, Sixu Deng, Zhimin Ren, Guohua Li, Huan Huang, Ruying Li, Ning Chen, Jiantao Wang, Xifei Li, Chandra Veer Singh, Xueliang Sun

Bibliographic record

VenueAdvanced Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of TorontoCanadian Light Source (Canada)Western University
FundersNatural Sciences and Engineering Research Council of CanadaNingbo University of Technology
KeywordsMaterials scienceElectrolyteCathodeKineticsLithium (medication)Chemical engineeringOxideElectrodeElectron transferRedoxCoatingFast ion conductorNanotechnologyPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Employing lithium‐rich layered oxide (LLO) as the cathode of all‐solid‐state batteries (ASSBs) is highly desired for realizing high energy density. However, the poor kinetics of LLO, caused by its low electronic conductivity and significant oxygen‐redox‐induced structural degradation, has impeded its application in ASSBs. Here, the charge transfer kinetics of LLO is enhanced by constructing high‐efficiency electron transport networks within solid‐state electrodes, which considerably minimizes electron transfer resistance. In addition, an infusion‐plus‐coating strategy is introduced to stabilize the lattice oxygen of LLO, successfully suppressing the interfacial oxidation of solid electrolyte (Li 3 InCl 6 ) and structural degradation of LLO. As a result, LLO‐based ASSBs exhibit a high discharge capacity of 230.7 mAh g −1 at 0.1 C and ultra‐long cycle stability over 400 cycles. This work provides an in‐depth understanding of the kinetics of LLO in solid‐state electrodes, and affords a practically feasible strategy to obtain high‐energy‐density ASSBs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.117
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)0.0010.000

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 head0.021
GPT teacher head0.265
Teacher spread0.244 · 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

Citations111
Published2022
Admission routes2
Has abstractyes

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