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Record W2947741307 · doi:10.1149/2.0401910jes

Improved Composite Solid Electrolyte through Ionic Liquid-Assisted Polymer Phase for Solid-State Lithium Ion Batteries

2019· article· en· W2947741307 on OpenAlexafffund
Jiahua Ou, Gaoran Li, Zhongwei Chen

Bibliographic record

VenueJournal of The Electrochemical Society · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrolyteMaterials scienceIonic conductivityCrystallinityIonic liquidComposite numberLithium (medication)Chemical engineeringElectrochemistryBattery (electricity)Quasi-solidFast ion conductorPlasticizerIonChemistryComposite materialElectrodeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Organic-inorganic solid-state composite electrolyte is of great promise to support the high-performance and safe energy storage applications. Herein we developed an ionic liquid-assisted PEO-LAGP-EMITFSI composite electrolyte (PLE) for advanced solid-state lithium ion batteries (LIBs). The EMITFSI ionic liquid was employed as the plasticizer to effectively lower the crystallinity of the composite and thus significantly enhance the ionic conductivity up to 8.85 × 10 −4 S cm −1 at 60°C. Combined with its good electrochemical stability and decent ion transference selectiveness, the optimized PLE electrolyte contributed to an excellent battery performance in LiFePO 4 /PLE/Li configuration with high capacity retention of 114.7 mAh g −1 after 120 cycles and good rate capability of 112.2 mAh g −1 at 1C, indicating its great potential in developing high-performance solid-state LIBs.

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 score0.003

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 science0.0000.000
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 head0.009
GPT teacher head0.270
Teacher spread0.262 · 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

Citations30
Published2019
Admission routes2
Has abstractyes

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