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Record W2885574213 · doi:10.1002/jnm.2478

Effect of injection barrier height on charge transport under bipolar injection

2018· article· en· W2885574213 on OpenAlexaff
Nadia Meftali, S. Bouazabia, I. Fofana, Ali El Maouhab

Bibliographic record

VenueInternational Journal of Numerical Modelling Electronic Networks Devices and Fields · 2018
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsPoisson's equationTransient (computer programming)MechanicsAnodeVoltageElectric fieldCathodeMaterials scienceMathematicsElectrodeMathematical analysisPhysicsElectrical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract In this paper, a numerical model describing the transient phenomena of electric charge in a dielectric because of a double injection at 2 electrodes is proposed. The mathematical model and the methods for its resolution are presented step by step. Thus, the Schottky equation is used to describe the injection of negative and positive charges from the cathode and anode respectively. The equations governing the conduction are the current equation and the Poisson equation coupled to the continuity equation. The adopted model uses the applied voltage instead of the applied electric field. The Poisson equation is solved by finite differences using the Thomas algorithm and the continuity equation by Euler's method. The Thomas algorithm reduces considerably the execution time compared to iterative methods. The time step is not constant; it is calculated from the speed of the charges front. The results show that the established model and the method of resolution reproduce correctly the behaviour of the system under a high voltage. It shows also that knowing the injection barrier height can be used to predict the behaviour of the dielectric under electrical stress. The developed model is a tool for describing the behaviour of the charges for a bipolar injection.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.679
Threshold uncertainty score0.413

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 head0.008
GPT teacher head0.253
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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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