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Record W4212800491 · doi:10.1126/sciadv.abl8219

Enhanced emission directivity from asymmetrically strained colloidal quantum dots

2022· article· en· W4212800491 on OpenAlexaff
Song Yang, Ruixiang Liu, Zhibo Wang, Huaiyu Xu, Yong Ma, Fengjia Fan, Oleksandr Voznyy, Jiangfeng Du

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsQuantum dotDirectivityPhotonPhysicsPopulationQuantumDipoleOptoelectronicsMaterials scienceOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Current state-of-the-art quantum dot light-emitting diodes have reached close to unity internal quantum efficiency. Further improvement in external quantum efficiency requires more efficient photon out-coupling. Improving the directivity of the photon emission is considered to be the most feasible approach. Here, we report improved emission directivity from colloidal quantum dot films. By growing an asymmetric compressive shell, we are able to lift their band-edge state degeneracy, which leads to an overwhelming population of exciton with in-plane dipole moment, as desired for high-efficiency photon out-coupling. The in-plane dipole proportion determined by back-focal plane imaging method is 88%, remarkably higher than 70% obtained from conventional hydrostatically strained colloidal quantum dots. Enhanced emission directivity obtained here opens a path to increasing the external quantum efficiencies notably.

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 score0.002

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 head0.016
GPT teacher head0.266
Teacher spread0.249 · 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

Citations35
Published2022
Admission routes1
Has abstractyes

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