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Record W1966585347 · doi:10.1063/1.4826618

Spectral and spatial contributions to white light generation from InGaN/GaN dot-in-a-wire nanostructures

2013· article· en· W1966585347 on OpenAlexafffund
Y. Kamali, Brenna Walsh, Jonathan Mooney, Hieu Pham Trung Nguyen, Colin Brosseau, R. Leonelli, Zetian Mi, Patanjali Kambhampati

Bibliographic record

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversité de MontréalMcGill University
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesMcGill University
KeywordsPhotoluminescenceHeterojunctionMaterials scienceNanowireRaman spectroscopyOptoelectronicsSpectral lineResonance (particle physics)Molecular beam epitaxyDiodeNanostructureEpitaxyCondensed matter physicsMolecular physicsOpticsNanotechnologyAtomic physicsChemistryPhysics

Abstract

fetched live from OpenAlex

We analyze epitaxially grown InGaN/GaN dot-in-a-wire heterostructures to relate growth and design properties to trends seen in photoluminescence (PL) and resonance Raman spectra. Temperature-dependent PL measurement of these dot-in-a-wire heterostructures illustrate an expected decrease in integrated PL emission and an unusual narrowing of peak width with increasing temperature. Information extracted from Resonance Raman spectra was utilized in a time-dependent model to analyze and to simulate PL spectra. These spectra were found to be in good agreement with the experimental PL data and provided insight into the broadening mechanisms affecting the samples. PL measurements were taken as a function of position on the sample and radial variation of peak energies was observed. This variation was attributed to the radial temperature gradient present during nanowire growth. These additional characteristics of the nanowire heterostructures will allow for increased understanding of these systems potentials for applications in white light emitting diodes.

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 score0.055
Threshold uncertainty score0.493

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 head0.007
GPT teacher head0.225
Teacher spread0.218 · 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

Citations4
Published2013
Admission routes2
Has abstractyes

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