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Record W4318034557 · doi:10.1002/anie.202217081

Halide Layer Cathodes for Compatible and Fast‐Charged Halides‐Based All‐Solid‐State Li Metal Batteries

2023· article· en· W4318034557 on OpenAlexafffund
Jianwen Liang, Xiaona Li, Jung Tae Kim, Xiaoge Hao, Hui Duan, Ruying Li, Xueliang Sun

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHalideElectrolyteElectrochemistryIntercalation (chemistry)Materials scienceCathodeMetal halidesInorganic chemistryElectrodeSolubilityLithium (medication)Chemical engineeringChemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Insertion‐type compounds based on oxides and sulfides have been widely identified and well‐studied as cathode materials in lithium‐ion batteries. However, halides have rarely been used due to their high solubility in organic liquid electrolytes. Here, we reveal the insertion electrochemistry of VX3 (X=Cl, Br, I) by introducing a compatible halide solid‐state electrolyte with a wide electrochemical stability window. X‐ray absorption near‐edge structure analyses reveal a two‐step lithiation process and the structural transition of typical VCl3. Fast Li+ insertion/extraction in the layered VX3 active materials and favorable interface guaranteed by the compatible electrode‐electrolyte design enables high rate capability and stable operation of all‐solid‐state Li‐VX3 batteries. The findings from this study will contribute to developing intercalation insertion electrochemistry of halide materials and exploring novel electrode materials in viable energy storage systems.

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 score0.004

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.034
GPT teacher head0.288
Teacher spread0.254 · 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

Citations45
Published2023
Admission routes2
Has abstractyes

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