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Record W2766519665 · doi:10.1021/acs.jpcc.7b09145

Electron Dynamics at the Surface of Semiconductor Nanocrystals

2017· article· en· W2766519665 on OpenAlexafffund
Samuel Palato, H. Seiler, Lucie McGovern, Timothy G. Mack, Lakshay Jethi, Patanjali Kambhampati

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2017
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsMcGill University
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesCanada Foundation for InnovationNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsExcitonNanocrystalMaterials scienceChemical physicsEmission spectrumSurface statesSemiconductorRelaxation (psychology)Spectral lineElectronSpectroscopyPhotoluminescenceSurface (topology)Molecular physicsNanotechnologyChemistryCondensed matter physicsOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Semiconductor nanocrystals emit light from excitons confined to their core, as well as from their surfaces. Time-resolving the emission from the core yields information on the band edge exciton, which is now well understood. In contrast, the emission from the surface is ill-characterized and remains poorly understood, especially on long time scales. In order to understand the kinetics of charge trapping to the surface and electronic relaxation within the surface, we perform time-resolved emission spectroscopy on CdSe nanocrystals with strong surface emission. The time-resolved spectra reveal a time scale of electron transfer from core to surface much slower than previously thought. These spectra also unveil electron dynamics in the surface band, which gives rise to an average lifetime spectrum. These dynamics are explained by invoking two surface states. This simple model further rationalizes the role of ligands in tuning the surface emission of nanocrystals. These experimental results provide a critical test of our understanding of the electronic structure of the surface.

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 score0.001
Threshold uncertainty score0.239

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 head0.016
GPT teacher head0.257
Teacher spread0.240 · 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

Citations35
Published2017
Admission routes2
Has abstractyes

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