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Record W4309839059 · doi:10.1149/ma2022-02552069mtgabs

Correlation between Ion Transport and Structural Heterogeneity in Triazole-Based Polymerized Ionic Liquids

2022· article· en· W4309839059 on OpenAlexaff
Javad Jeddi, Joshua Sangoro, Jukka Niskanen, Benoît H. Lessard

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIonic liquidIonMaterials scienceIonic bondingIonic conductivityChemical physicsActivation energyPolymerizationNanoscopic scaleChemistryAnalytical Chemistry (journal)Physical chemistryNanotechnologyOrganic chemistryPolymerElectrodeElectrolyte

Abstract

fetched live from OpenAlex

In this study, the relationship between chemical structure, nanoscale organization, and ion transport in 1,2,3-triazolium polymerized ionic liquids (PILs) was investigated by wide-angle X-ray scattering (WAXS) and broadband dielectric spectroscopy (BDS). Analyzing the WAXS and BDS results indicated that mobile ion types and chemical structure of the pendant groups controlled structural heterogeneity and ion conduction of the PILs below T g . The normalized heterogeneity length extracted from WAXS data was used to correlate the structural heterogeneity and ion conduction activation energy. For the polycation samples, larger TFSI – mobile anion results in a higher packed structure than small Cl – while in the polyanion samples inverse trend was observed. The calculated activation energy of the dc conductivity below T g is quantitatively correlated to the structural heterogeneity obtained from nano structure analysis using WAXS. This suggested increasing the spatial heterogeneity of the PILs results in the reduction of the activation energy of long-range ion motions. These results highlight the role of spatial heterogeneity in designing efficient polymerized ionic liquids

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)0.001
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 score0.189
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.025
GPT teacher head0.270
Teacher spread0.245 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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