MétaCan
Menu
Back to cohort
Record W2047389826 · doi:10.1063/1.4773835

Dislocation line charge screening within n-type gallium nitride

2013· article· en· W2047389826 on OpenAlexafffund
Erfan Baghani, Stephen K. O’Leary

Bibliographic record

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsDislocationIonizationSpace chargeCharge (physics)Atomic physicsGallium nitridePhysicsRADIUSFormalism (music)ImpurityCondensed matter physicsChemistryMaterials scienceMolecular physicsIonQuantum mechanicsNanotechnologyElectron

Abstract

fetched live from OpenAlex

A revised electrostatic theory for the charged dislocation lines within n-type GaN is formulated, this formalism allowing for the screening of the charge trapped along the dislocation lines, by both free carriers and a partial ionization of the impurities within the space-charge region surrounding the dislocation lines. This goes beyond the abrupt space-charge region assumption of the Read model [W. T. Read, Jr., Philos. Mag. 45, 775 (1954)], where the only screening mechanism considered is a complete ionization of bulk donor atoms within the Read radius. In addition to determining the spatial distribution of the charge enveloping charged dislocation lines, this procedure also provides a solution to the electrostatic potential surrounding the dislocation lines. An iterative, self-consistent numerical approach to the solution of this problem is developed for the purposes of this analysis. A special limit for which the results of this model reduce to that of Read is indicated. The results obtained from our analysis are found to be in satisfactory agreement with experimental results from the literature.

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 score0.020
Threshold uncertainty score0.549

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)0.0010.000

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 head0.019
GPT teacher head0.244
Teacher spread0.225 · 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

Citations5
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied PhysicsSame topicGaN-based semiconductor devices and materialsFrench-language works237,207