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Record W1986446780 · doi:10.1002/adfm.200601236

Bright Blue Photo‐ and Electroluminescence from Eu<sup>2+</sup>‐Doped GaN/SiO<sub>2</sub> Nanocomposites

2007· article· en· W1986446780 on OpenAlexafffund
Venkataramanan Mahalingam, Mingqian Tan, Prabhakaran Munusamy, Joe B. Gilroy, Mati Raudsepp, Frank C. J. M. van Veggel

Bibliographic record

VenueAdvanced Functional Materials · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanocompositeEuropiumMaterials scienceElectroluminescenceDopingLuminescenceExcited stateQuantum yieldElectron paramagnetic resonancePhotochemistryOptoelectronicsNanotechnologyOpticsNuclear magnetic resonanceFluorescenceLayer (electronics)ChemistryAtomic physics

Abstract

fetched live from OpenAlex

Abstract In this article we demonstrate the synthesis of Eu2+‐doped GaN/SiO2 nanocomposites using a simple solid state reaction and their use in light‐emitting devices. The nanocomposite exhibits a bright blue luminescence when excited in the UV region (quantum yield = 23 %). The origin of the blue emission is attributed to the presence of europium ions in the +2 oxidation state in the GaN/SiO2 nanocomposites. Analysis of the EPR spectrum of europium‐doped GaN/SiO2 nanocomposites confirms the existence of Eu2+ in the nanocomposites. Various control experiments show that the blue emission arises from these europium ions and that the interface of GaN and silica plays a crucial role. The Eu2+‐doped GaN/SiO2 nanocomposite also exhibits a bright blue electroluminescence. Furthermore, the nanocomposites can be coated with a polymer to tune their dispersibility in organic medium.

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 score0.001
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 head0.007
GPT teacher head0.210
Teacher spread0.204 · 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

Citations24
Published2007
Admission routes2
Has abstractyes

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