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Record W2762343173 · doi:10.1021/acs.nanolett.7b03294

Nonmonotonic Dependence of Auger Recombination Rate on Shell Thickness for CdSe/CdS Core/Shell Nanoplatelets

2017· article· en· W2762343173 on OpenAlexafffund
Matthew Pelton, Jordan J. Andrews, Igor Fedin, Dmitri V. Talapin, Haixu Leng, Stephen K. O’Leary

Bibliographic record

VenueNano Letters · 2017
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersDivision of ChemistryNatural Sciences and Engineering Research Council of CanadaOffice of ScienceDivision of Materials ResearchU.S. Department of EnergyNational Science Foundation
KeywordsAuger effectAugerNanocrystalQuantum dotPhotoluminescenceRecombinationMaterials scienceMonolayerShell (structure)Core (optical fiber)ColloidExcitationDiodeSemiconductorMolecular physicsOptoelectronicsAtomic physicsChemistryNanotechnologyPhysicsPhysical chemistryComposite material

Abstract

fetched live from OpenAlex

Nonradiative Auger recombination limits the efficiency with which colloidal semiconductor nanocrystals can emit light when they are subjected to strong excitation, with important implications for the application of the nanocrystals in light-emitting diodes and lasers. This has motivated attempts to engineer the structure of the nanocrystals to minimize Auger rates. Here, we study Auger recombination rates in CdSe/CdS core/shell nanoplatelets, or colloidal quantum wells. Using time-resolved photoluminescence measurements, we show that the rate of biexcitonic Auger recombination has a nonmonotonic dependence on the shell thickness, initially decreasing, reaching a minimum for shells with thickness of 2-4 monolayers, and then increasing with further increases in the shell thickness. This nonmonotonic behavior has not been observed previously for biexcitonic recombination in quantum dots, most likely due to inhomogeneous broadening that is not present for the nanoplatelets.

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)0.000
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 score0.003
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.045
GPT teacher head0.279
Teacher spread0.234 · 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

Citations54
Published2017
Admission routes2
Has abstractyes

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