MétaCan
Menu
Back to cohort
Record W4383375341 · doi:10.1002/adfm.202305186

Eliminating Local Electrolyte Failure Induced by Asynchronous Reaction for High‐Loading and Long‐Lifespan All‐Solid‐State Batteries

2023· article· en· W4383375341 on OpenAlexaff
Hanwen An, Qingsong Liu, Biao Deng, Jian Wang, Menglu Li, Xin Li, Shuaifeng Lou

Bibliographic record

VenueAdvanced Functional Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsCathodeMaterials scienceElectrolyteBattery (electricity)Fast ion conductorNanotechnologyPolymer electrolytesInterface (matter)Battery capacityChemical engineeringComposite materialElectrodeElectrical engineeringIonic conductivityPhysical chemistryEngineeringPower (physics)ChemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract The design of practical cathodes with high areal capacity in polymer‐based all‐solid‐state batteries remains challenged by the absence of an effective guiding principle that prolongs battery life‐span. Unlike liquid batteries, the notorious interface incompatibility between cathodes and electrolytes limited the cycling life of the all‐solid‐state batteries. Herein, this study proposes a dynamically stable cathode design with a fully covered surface, effectively mitigating interface failure and enabling the cyclic time of batteries with a cathode loading of 12.7 mg cm ‒2 over 10 000 h. This study unveils the importance of local state of charge in affecting the interfacial properties of particles through local oxidative‐stability of electrolytes on the interface. This study shows that the phenomena can be strongly influenced by the porosity of the cathode through the perspective of discreteness of ion transport. These insights and approach provide a broader promise for solid batteries for long lifetime.

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)
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 score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.227
Teacher spread0.218 · 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

Citations19
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAdvanced Functional MaterialsSame topicAdvanced Battery Materials and TechnologiesFrench-language works237,207