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Record W4389933987 · doi:10.1021/acs.nanolett.3c03180

Excitonic Quantum Coherence in Light Emission from CsPbBr<sub>3</sub> Metal-Halide Perovskite Nanocrystals

2023· article· en· W4389933987 on OpenAlexafffund
Dallas Strandell, Carlos Mora Perez, Yifan Wu, Oleg V. Prezhdo, Patanjali Kambhampati

Bibliographic record

VenueNano Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsMcGill University
FundersDivision of ChemistryMcGill UniversityFonds Québécois de la Recherche sur la Nature et les TechnologiesCanada Foundation for InnovationSonyNatural Sciences and Engineering Research Council of Canada
KeywordsBiexcitonPhotoluminescencePerovskite (structure)ExcitonExcited stateDephasingRadiative transferCoherence (philosophical gambling strategy)Quantum dotSpontaneous emissionPhysicsChemistryAtomic physicsMolecular physicsMaterials scienceCondensed matter physicsQuantum mechanicsOptoelectronics

Abstract

fetched live from OpenAlex

The decay of excited states via radiative and nonradiative paths is well understood in molecules and bulk semiconductors but less so in nanocrystals. Here, we perform time-resolved photoluminescence (t-PL) experiments on CsPbBr 3 metal-halide perovskite nanocrystals, with a time resolution of 3 ps, sufficient to observe the decay of both excitons and biexcitons as a function of temperature. The striking result is that the radiative rate constant of the single exciton increases at low temperatures with an exponential functional form, suggesting quantum coherent effects with dephasing at high temperatures. The opposing directions of the radiative and nonradiative decay rate constants enable enhanced brightening of PL from excitons to biexcitons due to quantum effects, promoting a faster approach to the quantum theoretical limits of light emission. Ab initio quantum dynamics simulations reproduce the experimental observations of radiation controlled by quantum spatial coherence enhanced at low temperatures.

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 categoriesMeta-epidemiology (narrow)
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 score0.011
Threshold uncertainty score1.000

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)0.0000.001

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 head0.011
GPT teacher head0.211
Teacher spread0.200 · 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

Citations17
Published2023
Admission routes2
Has abstractyes

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