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Record W2970166281 · doi:10.1088/1361-6463/ab3f78

Vacancy-assisted core transformation and mobility modulation of a-type edge dislocations in wurtzite GaN

2019· article· en· W2970166281 on OpenAlexafffund
Cheng Chen, Fanchao Meng, Huicong Chen, Pengfei Ou, Guoqiang Lan, Bing Li, Qiwen Qiu, Jun Song

Bibliographic record

VenueJournal of Physics D Applied Physics · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsMcGill University
FundersChina Scholarship CouncilUniversité du Québec à MontréalNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsWurtzite crystal structureEnhanced Data Rates for GSM EvolutionVacancy defectCore (optical fiber)Materials scienceModulation (music)Transformation (genetics)DislocationCrystallographyCondensed matter physicsChemistryPhysicsHexagonal crystal systemComposite materialTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

Abstract In this study, core structure dependent dislocation dynamics of a-type edge dislocation in three slip systems (basal, prismatic and pyramidal) of wurtzite GaN have been investigated using classical molecular dynamics simulations. All potential a-type edge dislocation cores in the shuffle and glide planes of the three slip systems have been identified, and the corresponding dislocation dynamics were examined. Our calculations reveal that for all of the three slip systems, all of the shuffle cores are planar glissile and mobile, while being non-planar sessile and immobile for all of the glide cores. We further show that vacancy can be used to activate the motion of glide cores via core transition from glide to shuffle, which is also valid for AlN and InN. The critical shear stresses for the motion of glide cores are also determined at various vacancy concentrations. Our study clarifies core structure dependent dislocation dynamics characteristics and provides ways in tuning dislocation motions in wurtzite crystals.

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 score0.386
Threshold uncertainty score0.596

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 head0.022
GPT teacher head0.258
Teacher spread0.237 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

Explore more

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