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Record W2150303737 · doi:10.5539/mas.v4n6p49

Experimental Optimization of growth Parameters of High Quality Green GaN Multiple Quantum Well by Metal-Organic Chemical Vapor Deposition

2010· article· en· W2150303737 on OpenAlexvenueno aff
Feng Wen, Lirong Huang, Liangzhu Tong, Deming Liu

Bibliographic record

VenueModern Applied Science · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsMetalorganic vapour phase epitaxyChemical vapor depositionMaterials sciencePhotoluminescenceMetalDeposition (geology)DiffractionOptoelectronicsWavelengthAnalytical Chemistry (journal)OpticsNanotechnologyChemistryLayer (electronics)EpitaxyMetallurgyPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

The effects of grow parameters such as barrier thickness, flow ratio of group-III sources (TMIn/(TMIn+TMGa)), well temperature on the properties of green multiple quantum well (MQW) are investigated. The samples were grown by metal-organic chemical vapor deposition (MOCVD). High resolution x-ray diffraction (HRXRD) and room temperature photoluminescence (PL) were employed to analyze the results. The PL and HRXRD images show that a green MQW of high quality, at long wavelength 550nm, was obtained by controlling the GaN buffer quality, well growth temperature, and flow ratio of group-III sources (TMIn/(TMIn+TMGa)).

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 score0.053
Threshold uncertainty score0.516

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 head0.013
GPT teacher head0.244
Teacher spread0.231 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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