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Record W2326153014 · doi:10.1021/acs.jpclett.5b00143

Auger-Limited Carrier Recombination and Relaxation in CdSe Colloidal Quantum Wells

2015· article· en· W2326153014 on OpenAlexafffund
Erfan Baghani, Stephen K. O’Leary, Igor Fedin, Dmitri V. Talapin, Matthew Pelton

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2015
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaBasic Energy SciencesMaterials Research Science and Engineering Center, Harvard UniversityW. M. Keck FoundationU.S. Department of EnergyDivision of Materials ResearchOffice of ScienceSamsungNational Science Foundation
KeywordsAuger effectExcitonQuantum wellRecombinationRelaxation (psychology)AugerPhotoluminescenceElectronAtomic physicsTrionCarrier lifetimeChemistryMolecular physicsCondensed matter physicsMaterials sciencePhysicsOptoelectronicsOpticsSilicon

Abstract

fetched live from OpenAlex

Using time-resolved photoluminescence spectroscopy, we show that two-exciton Auger recombination dominates carrier recombination and cooling dynamics in CdSe nanoplatelets, or colloidal quantum wells. The electron-hole recombination rate depends only on the number of electron-hole pairs present in each nanoplatelet, and is consistent with a two-exciton recombination process over a wide range of exciton densities. The carrier relaxation rate within the conduction and valence bands also depends only on the number of electron-hole pairs present, apart from an initial rapid decay, and is consistent with the cooling rate being limited by reheating due to Auger recombination processes. These Auger-limited recombination and relaxation dynamics are qualitatively different from the carrier dynamics in either colloidal quantum dots or epitaxial quantum wells.

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 score0.004
Threshold uncertainty score0.235

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 science0.0000.000
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 head0.020
GPT teacher head0.232
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

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

Citations79
Published2015
Admission routes2
Has abstractyes

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