MétaCan
Menu
Back to cohort
Record W4411266090 · doi:10.1021/acs.macromol.5c01200

Salt-Solvent-Polymer Interactions Influence Lithium Salt Distribution in Thermoplastic Vulcanizate Electrolytes

2025· article· en· W4411266090 on OpenAlexafffund
Gabrielle Foran, Joseph Chidiac, Caroline St‐Antoine, Paul Nicolle, Léa Caradant, Arnaud Prébé, Mickaël Dollé

Bibliographic record

VenueMacromolecules · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsUniversité de Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsSalt (chemistry)Lithium (medication)SolventPolymer chemistryThermoplasticElectrolytePolymerChemistryMaterials scienceChemical engineeringOrganic chemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Lithium ion mobility in solvent-doped solid polymer electrolytes depends on interactions between the salt, the polymer and absorbed solvents. This work presents novel electrolytes based on thermoplastic vulcanizates which have been shown to possess the mechanical and electrochemical stability of the elastomeric phase along with the mobility and processability of the thermoplastic phase. Although modest ionic conductivities were obtained for the as-prepared electrolytes, the addition of lithium salt-containing solvents improved conductivity by about 2 orders of magnitude. The presence of multiple lithium salt dissociating phases raises questions regarding the distribution of lithium salt in the electrolyte and the participation of each phase in ion conduction. Ionic conductivity, NMR spectroscopy measurements of salt distribution and ion mobility and vibrational spectroscopy of lithium-coordinating functional groups showed that lithium salts exist in a solvent-swelled polymer phase wherein ionic conductivity depends on a combination of lithium salt distribution, solvent-polymer interactions and local-scale polymer mobility.

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 score0.030
Threshold uncertainty score0.664

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 head0.004
GPT teacher head0.217
Teacher spread0.213 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueMacromoleculesSame topicAdvanced Battery Materials and TechnologiesFrench-language works237,207