MétaCan
Menu
Back to cohort
Record W2117264908 · doi:10.5539/apr.v5n2p14

Monte Carlo Study of the Dynamic Screening Effect in Doped GaN

2013· article· en· W2117264908 on OpenAlexvenueno aff
F. M. Abou El‐Ela, A. Z. Mohamed

Bibliographic record

VenueApplied Physics Research · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsMonte Carlo methodCondensed matter physicsElectronPhononScattering rateScatteringDopingFermi–Dirac statisticsPlasmonDrift velocityAtomic physicsQuantum mechanics

Abstract

fetched live from OpenAlex

Monte Carlo simulations of hot electron transport in n-type GaN when the interaction of the electrons with polar optical phonons is dynamically screened shows the effects of antiscreening at carrier densities of (1-5)x1024 m-3. At these densities full coupling between the Plasmon-phonon systems could be ignored to a first approximation together with degeneracy. Our calculations used the Lindhard formalism with Fermi-Dirac distribution and with neglecting the collisional damping when dynamic screened electron phonons scattering rates calculated. The screened scattering rate is strongly enhanced at low electron temperature and high carrier concentrations due to antiscreening property of inverse dielectric function. Antiscreening delays runaway and intervalley transfer to higher valleys. The peak drift velocity is enhanced and as result, so is the peak valley ratio.

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)0.001
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.333
Teacher spread0.298 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueApplied Physics ResearchSame topicGaN-based semiconductor devices and materialsFrench-language works237,207