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Record W2056276153 · doi:10.1063/1.1345816

Properties of carbon-doped GaN

2001· article· en· W2056276153 on OpenAlexaff
H. Tang, J. B. Webb, J. A. Bardwell, S. Raymond, J. Salzman, C. Uzan-Saguy

Bibliographic record

VenueApplied Physics Letters · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsInstitute for Microstructural Sciences
Fundersnot available
KeywordsMaterials sciencePhotoluminescenceDopingMolecular beam epitaxyOptoelectronicsCarbon fibersLuminescenceElectrical resistivity and conductivityEpitaxyHall effectWide-bandgap semiconductorNanotechnologyComposite materialLayer (electronics)

Abstract

fetched live from OpenAlex

The properties of carbon-doped GaN epilayers grown by molecular-beam epitaxy have been studied by temperature-dependent resistivity, Hall-effect measurements, x-ray diffraction, and by photoluminescence spectroscopy. Carbon doping was found to render the GaN layers highly resistive (>108 Ω cm) and quench the band edge excitonic emissions. Yellow luminescence is still present in carbon-doped GaN layers. The highly resistive state is interpreted as being caused by direct compensation by the carbon acceptors and by the consequently enhanced potential barrier at the subgrain boundaries. Evidence of dislocations joining to form potential barriers along the subgrain boundaries was observed in photoassisted wet etching experiments on electrically conducting GaN layers. GaN films grown on insulating carbon-doped base layers are of excellent transport and optical properties.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.017
GPT teacher head0.210
Teacher spread0.192 · 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 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

Citations149
Published2001
Admission routes1
Has abstractyes

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