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Record W1996594041 · doi:10.1021/ja803477h

Wavelength Redistribution and Color Purification Action of a Photonic Crystal

2008· article· en· W1996594041 on OpenAlexaff
Enrico Bovero, Frank C. J. M. van Veggel

Bibliographic record

VenueJournal of the American Chemical Society · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsWavelengthPhotonic crystalCommon emitterOptoelectronicsEmission intensityBand gapChemistryPhotonicsIntensity (physics)OpticsPhotoluminescenceMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Currently, photonic crystals are attracting a lot of interest because of their ability to harvest light from a device into specific directions and wavelengths. In this work we have proven the theoretical prediction that in the case of an emission overlapping with the photonic stop band, the intensity is redistributed at different wavelengths. This prediction has two major consequences: (i) the total QY remains the same and (ii) the intensity increases just outside the band gap. In our case, Eu(2+) is the responsible emitter in a hybrid material based on GaN on silica, which has a fairly broad emission with its maximum at 500 nm. The GaN and Eu(2+) were placed inside an inverse opal of silica (air voids in silica matrix). The size of the holes in the different samples was varied between 300 and 600 nm, in order to tune the stop band in different positions with respect to the Eu(2+) emission. The measured quantum yield was constant for the different samples at about 5%, the lifetime of the Eu(2+) increased in the forbidden range, and its emission intensity was squeezed toward the side of the stop band, with a concomitant decrease of the lifetime. The enhancement of the emission intensity at a certain energy range opens new possibilities for the design of more efficient devices, providing color purification and intensification at whichever wavelength is needed.

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 score0.030
Threshold uncertainty score0.181

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 head0.014
GPT teacher head0.257
Teacher spread0.244 · 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

Citations26
Published2008
Admission routes1
Has abstractyes

Explore more

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