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Record W4402597680 · doi:10.1016/j.xcrp.2024.102213

4.8-V all-solid-state garnet-based lithium-metal batteries with stable interface

2024· article· en· W4402597680 on OpenAlexafffund
Senhao Wang, Stéphanie Bessette, Raynald Gauvin, George P. Demopoulos

Bibliographic record

VenueCell Reports Physical Science · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsLithium metalInterface (matter)Lithium (medication)Materials scienceSolid-stateMetalState (computer science)Engineering physicsMetallurgyChemistryComputer scienceEngineeringComposite materialPhysical chemistryElectrodeAnodeAlgorithmPsychology

Abstract

fetched live from OpenAlex

Summary Garnet-type solid electrolytes with high chemical and electrochemical stabilities are uniquely suitable for high-voltage operation but suffer from poor wettability with electrodes, resulting in large interfacial impedance. Here, we design a highly conductive and interface-friendly garnet-based composite solid electrolyte (CSE) comprising a cubic Li6.1Al0.3La3Zr2O12 porous framework and polyvinylidene difluoride (PVDF) with a three-dimensional continuous structure. Formation of La-N and La-F bonds between ceramic and polymer moieties promotes the dissociation of Li salt and thus leads to highly efficient transport. These coupling effects contribute to a high ionic conductivity (0.437 mS cm−1) and Li transfer number t+ (0.72) at 25°C, while simultaneously enable high electrode/electrolyte interfacial stability. The high-voltage robustness of the developed CSE is demonstrated using TiO2-coated LiNi0.6Co0.2Mn0.2O2/ceramic-based CSE/Li full solid-state batteries, which are stably cycled over 200 times from 3 to 4.8 V with no signs of interfacial instabilities at nanoscale.

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 score0.002
Threshold uncertainty score0.006

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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

Citations7
Published2024
Admission routes2
Has abstractyes

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