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Record W4285400060 · doi:10.1149/ma2022-01201079mtgabs

(Invited) Realizing Narrow Bandwidth Visible Photoluminescence from Colloidal Silicon Quantum Dots

2022· article· en· W4285400060 on OpenAlexaff
Jonathan G. C. Veinot

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhotoluminescenceOptoelectronicsQuantum dotMaterials scienceLuminescenceLight-emitting diodeQuantum yieldNanotechnologyDiodeSiliconLight emissionVisible spectrumFluorescenceOpticsPhysics

Abstract

fetched live from OpenAlex

Quantum dot light-emitting diodes (QD-LEDs) are an attractive alternative to organic light-emitting diodes (OLEDs) that have attracted attention for next generation optoelectronic devices due to their high colour-saturated photoluminescence, processability, and stability. Existing QD-LEDs often employ toxic or rare metals (e.g., Cd, Pb, In, etc.). Although colloidal silicon quantum dots (SiQDs) are an attractive alternative because of their abundance, biocompatibility and tailorable surface chemistry, tuning their luminescence throughout the visible spectrum can be challenging. Furthermore, SiQD emission shows a comparatively wide bandwidth that is a consequence of the inherent properties of the Si band structure. As a result the narrowing SiQD luminescence is not readily achieved by straightforward size selection methods used for other quantum dots. One alternative approach is to prepare optical cavities that preferentially select specific emission wavelengths using optical structures. This presentation will outline our investigations targeted on realizing bright, high quantum yield, narrow bandwidth luminescence from SiQD-polymer hybrids.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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.

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
Published2022
Admission routes1
Has abstractyes

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