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Record W3128974934 · doi:10.3390/app11041561

Thermal and Electrochemical Properties of Solid Polymer Electrolytes Prepared via Lithium Salt-Catalyzed Epoxide Ring Opening Polymerization

2021· article· en· W3128974934 on OpenAlexafffund
Gabrielle Foran, Nina Verdier, David Lepage, Arnaud Prébé, David Aymé‐Perrot, Mickaël Dollé

Bibliographic record

VenueApplied Sciences · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEpoxideMaterials sciencePolymerPolymerizationElectrolyteLithium (medication)Polymer chemistryChemical engineeringCatalysisOrganic chemistryChemistryElectrodeComposite material

Abstract

fetched live from OpenAlex

Solid polymer electrolytes have been widely proposed for use in all solid-state lithium batteries. Advantages of polymer electrolytes over liquid and ceramic electrolytes include their flexibility, tunability and easy processability. An additional benefit of using some types of polymers for electrolytes is that they can be processed without the use of solvents. An example of polymers that are compatible with solvent-free processing is epoxide-containing precursors that can form films via the lithium salt-catalyzed epoxide ring opening polymerization reaction. Many polymers with epoxide functional groups are liquid under ambient conditions and can be used to directly dissolve lithium salts, allowing the reaction to be performed in a single reaction vessel under mild conditions. The existence of a variety of epoxide-containing polymers opens the possibility for significant customization of the resultant films. This review discusses several varieties of epoxide-based polymer electrolytes (polyethylene, silicone-based, amine and plasticizer-containing) and to compare them based on their thermal and electrochemical properties.

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 score0.006
Threshold uncertainty score0.420

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 integrity0.0000.000
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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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

Citations13
Published2021
Admission routes2
Has abstractyes

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