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Record W2996154797 · doi:10.1021/acs.jpcc.9b08006

Hot-Carrier Relaxation in CdSe/CdS Core/Shell Nanoplatelets

2019· article· en· W2996154797 on OpenAlexafffund
Matthew Pelton, Yana Wang, Igor Fedin, Dmitri V. Talapin, Stephen K. O’Leary

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2019
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 CanadaChina Scholarship CouncilU.S. Department of EnergyDivision of Materials ResearchOffice of ScienceNational Science Foundation
KeywordsRelaxation (psychology)Auger effectPhononPhotoluminescenceExcitonAugerCoupling (piping)Materials scienceShell (structure)Molecular physicsSpectroscopyElectronQuantum dotCondensed matter physicsChemical physicsAtomic physicsChemistryPhysicsNanotechnologyOptoelectronics

Abstract

fetched live from OpenAlex

We present time-resolved photoluminescence (PL) spectroscopy of a series of colloidal CdSe/CdS core/shell nanoplatelets with different core and shell thicknesses. Exciton numbers are determined from the integrated PL intensities, and carrier temperatures are determined from the high-energy exponential tail of the PL spectra. For times between 10 and 1000 ps, the measured carrier relaxation dynamics are well described by a simple model of Auger reheating: biexcitonic Auger recombination continually increases the average energy of the carriers (while decreasing their number), and this reheating sets a bottleneck to cooling through electron–phonon coupling. For times between 1 and 10 ps, the relaxation dynamics are consistent with electron–phonon coupling, where the bottleneck is now the decay of the longitudinal optical phonon population. Comparison of relaxation dynamics to recombination dynamics reveals changes in the carrier–phonon coupling for shell thicknesses greater than 4 monolayers.

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 score0.002
Threshold uncertainty score0.633

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)0.0010.000

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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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

Citations14
Published2019
Admission routes2
Has abstractyes

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