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Record W2771335916 · doi:10.1021/acsnano.7b06363

Small-Band-Offset Perovskite Shells Increase Auger Lifetime in Quantum Dot Solids

2017· article· en· W2771335916 on OpenAlexafffund
Rafael Quintero‐Bermudez, Randy P. Sabatini, Marc Lejay, Oleksandr Voznyy, Edward H. Sargent

Bibliographic record

VenueACS Nano · 2017
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAuger effectQuantum dotAugerOptoelectronicsMaterials sciencePassivationLaserLasing thresholdPerovskite (structure)Ultrafast laser spectroscopyCarrier lifetimeOpticsNanotechnologyChemistryAtomic physicsPhysicsWavelengthSilicon

Abstract

fetched live from OpenAlex

Colloidal quantum dots (CQDs) enable low-cost, high-performance optoelectronic devices including photovoltaics, photodetectors, LEDs, and lasers. Continuous-wave lasing in the near-infrared remains to be realized based on such materials, yet a solution-processed NIR laser would be of use in communications and interconnects. In infrared quantum dots, long-lived gain is hampered by a high rate of Auger recombination. Here, we report the use of perovskite shells, grown on cores of IR-emitting PbS CQDs, and we thus reduce the rate of Auger recombination by up to 1 order of magnitude. We employ ultrafast transient absorption spectroscopy to isolate distinct Auger recombination phenomena and study the effect of bandstructure and passivation on Auger recombination. We corroborate the experimental findings with model-based investigations of Auger recombination in various CQD core-shell structures. We explain how the band alignment provided by perovskite shells comes close to the optimal required to suppress the Auger rate. These results provide a step along the path toward solution-processed near-infrared lasers.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.040
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.036
GPT teacher head0.259
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; both teacher heads agree on what is shown here.

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

Citations36
Published2017
Admission routes2
Has abstractyes

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