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Record W4403138339 · doi:10.1021/acs.jpcc.4c05494

Navigating Solvent Chemistry and Microstructures: Toward Mechanically Enhanced Ceramic-Rich Composite Electrolytes

2024· article· en· W4403138339 on OpenAlexafffund
Lingzi Sang, Mauricio Ponga, Michael D. Fleischauer, Runqi Wu

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsNational Institute for NanotechnologyUniversity of AlbertaUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesCanada Foundation for Innovation
KeywordsComposite numberElectrolyteMicrostructureCeramicSolventChemical engineeringChemistryMaterials scienceComposite materialOrganic chemistryPhysical chemistryEngineering

Abstract

fetched live from OpenAlex

Ultrathin ceramic-rich solid composite electrolytes provide a safer and potentially higher energy density alternative to liquid electrolytes used in today’s lithium-ion batteries. Producing ultrathin composites with ceramic-like ionic conductivity requires the incorporation of a polymeric binder for enhanced ductility. In this Perspective, we discuss two key aspects that must be considered when designing composite electrolytes: (1) the mechanical properties of the composite and their correlation with the ceramic and polymer microstructure and (2) the chemistry between the ceramic electrolytes, polymers, and solvents used to process the composites. We highlight the importance of understanding (1) the ceramic structure, crystallinity, and particle size upon solvent processing and (2) the ceramic/polymer interface chemistry and its correlation with the microstructure of the composites. We present opportunities in fabricating ultrathin support structures for composites, optimizing ceramic particle packing parameters, and routes toward mechanically enhanced, compact, composite-based solid electrolytes.

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 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 score0.003
Threshold uncertainty score0.455

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.230
Teacher spread0.226 · 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.

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

Citations1
Published2024
Admission routes2
Has abstractyes

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