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Record W3006652295 · doi:10.1016/j.ssel.2020.01.005

Modeling the negative capacitance effect in dispersive organic materials using modified Drude theory

2019· article· en· W3006652295 on OpenAlexaff
You‐Lin Wu, Jing‐Jenn Lin, H.L. Kwok

Bibliographic record

VenueSolid State Electronics Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicOrganic Light-Emitting Diodes Research
Canadian institutionsUniversity of Victoria
FundersMinistry of Science and Technology, TaiwanMinistry of Science and Technology
KeywordsCapacitanceDrude modelMaterials scienceNegative impedance converterAdmittanceDisplacement currentPhase (matter)Displacement (psychology)Electric fieldOrganic semiconductorDiodeCondensed matter physicsOpticsOptoelectronicsPhysicsThermodynamicsElectrical impedanceQuantum mechanicsElectrode

Abstract

fetched live from OpenAlex

Frequency- and mobility-dependent admittance have been observed in organic polymer light-emitting diodes. In this paper, we developed a model to describe this dispersive behavior using a modified Drude theory. In this model, a phase angle difference between the applied electric field and the average displacement of the charge carriers is introduced rather than using a complex mobility. This newly proposed model successfully describes the dispersive nature, as well as the negative capacitance effect, at low frequencies in organic polymers. The simulation results of this model also fit the negative capacitance data reported in the literature, provided that a suitable phase angle difference is given.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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 designSimulation or modeling
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

Citations2
Published2019
Admission routes1
Has abstractyes

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