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Electrically modulated near-field energy transfer between quantum dots and perovskite nanocrystals

2025· article· en· W4407615099 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOptics & Laser Technology · 2025
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of CalgaryUniversity of Waterloo
Fundersnot available
KeywordsNanocrystalQuantum dotPerovskite (structure)Energy transferMaterials scienceField (mathematics)QuantumEnergy (signal processing)OptoelectronicsNanotechnologyEngineering physicsPhysicsQuantum mechanicsChemical engineeringEngineering

Abstract

fetched live from OpenAlex

Electrically controlling resonance energy transfer of optical emitters provides a novel mechanism to switch nanoscale light sources on and off individually for optoelectronic applications. Cesium lead halide nanocrystals have emerged as a candidate for optoelectronic applications, seamlessly blending the favorable advantages of perovskites and quantum dots. Here, we demonstrate nonradiative energy transfer between CsPbBr 3 nanocrystals and colloidal quantum dots in a heterostructure device. We fabricate devices with semiconducting, chemically synthesised cesium lead bromide (CsPbBr 3 ) perovskite nanocrystals (PerNCs) as an electrostatically gated donor and core–shell quantum dots (QDs) as an acceptor. With the help of a bottom-gate electrode and hafnium oxide (HfO 2 ) dielectric layer, the Förster resonance energy transfer (FRET) efficiency can be modulated. Thicknesses of the dielectric, donor, and acceptor layers were fine-tuned and the optimized device configuration exhibits up to 80% modulation of photoluminescence intensity, making it suitable for potential applications in optoelectronic devices and energy conversion.

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.

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 score0.042
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.219
Teacher spread0.212 · 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