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Record W3135423678 · doi:10.1063/5.0040838

The rectification mechanism in polyelectrolyte gel diodes

2021· article· en· W3135423678 on OpenAlexafffund
Kudzanai Nyamayaro, Vasilii Triandafilidi, Parya Keyvani, Jörg Rottler, Parisa Mehrkhodavandi, Savvas G. Hatzikiriakos

Bibliographic record

VenuePhysics of Fluids · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolyelectrolyteRectificationDiodeIonic bondingChemical physicsPhysicsIonElectrochemistryNanotechnologyElectrodeVoltageMaterials scienceOptoelectronicsNuclear magnetic resonanceQuantum mechanicsPolymer

Abstract

fetched live from OpenAlex

Ionic driven devices have been increasingly investigated in the drive to develop flexible and biointegrable electronics. One such device is a polyelectrolyte gel diode capable of rectifying ionic current. However, the underlying mechanism behind the rectification of current in polyelectrolyte gel diodes is not fully understood. Based on experimental data, it has been proposed that the rectification is due to the asymmetric distribution of ions at the interface between two gels doped with a cationic polyelectrolyte on one side and an anionic polyelectrolyte on the other. Additionally, an electrochemical model has been proposed to explain the mechanism quantitatively. Here, we explore the mechanism proposed by the Yamamoto–Doi model and validate it by using experimental data. We show that the diode operates via a physical mechanism that involves the electrochemical generation of proton and hydroxyl ions at the electrodes to generate current. Exponential currents (J) in the forward bias were observed and J=A−V (with A inversely proportional to the gel ionization and V the potential) in the backward bias, which coincides with predictions of the electrochemical Yamamoto–Doi model. Additionally, we also confirm the dependence of the electrochemical model on the dopant concentration in the backward bias regime.

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 score0.032
Threshold uncertainty score0.215

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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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

Citations24
Published2021
Admission routes2
Has abstractyes

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