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Record W4400528642 · doi:10.1021/acsami.4c01045

Electrostatic Correlations Lead to High Capacitance in Zwitterion-Containing Thin Films

2024· article· en· W4400528642 on OpenAlexafffund
Simranjeet Kaur, Renita M. D’Souza, Timothy L. Kelly, Vance E. Williams, Loren G. Kaake

Bibliographic record

VenueACS Applied Materials & Interfaces · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsUniversity of SaskatchewanSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceZwitterionCapacitanceLead (geology)Thin filmNanotechnologyOptoelectronicsElectrodePhysical chemistryMoleculeOrganic chemistry

Abstract

fetched live from OpenAlex

A novel zwitterion composed of an imidazolium tethered to an anionic sulfonyl(trifluoromethane sulfonyl)imide group was prepared as an alternative dielectric material to traditional ionic liquids. The zwitterion not only melted below 100 °C but also proved to be nonhygroscopic. High-capacitance organic dielectric materials were obtained by blending this compound with poly(methyl methacrylate) over a range of concentrations and thicknesses. Above a specific temperature and concentration, films exhibit a capacitance nearly equivalent to that of an electrostatic double layer, approximately 10 μF/cm 2, regardless of their thickness. Grazing-incidence wide-angle X-ray scattering experiments suggest that the zwitterions adopt a lamellar ordering at their surface above a critical concentration. The observed ordering is correlated with a 1000-fold increase in capacitance. The behavior suggests that the zwitterions exhibit strong electrostatic correlations throughout the film bulk, pointing the way toward a novel class of organic dielectric materials.

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 score0.002

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 communication0.0000.000
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 head0.012
GPT teacher head0.238
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 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

Citations3
Published2024
Admission routes2
Has abstractyes

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