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Record W2770583294 · doi:10.1021/acs.jpcc.7b09903

Temperature Dependence of Emission Line Widths from Semiconductor Nanocrystals Reveals Vibronic Contributions to Line Broadening Processes

2017· article· en· W2770583294 on OpenAlexafffund
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
FundersHydro-QuébecFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsEmission spectrumExcitonLine (geometry)NanocrystalSurface statesMaterials scienceSemiconductorSurface (topology)PhotoluminescenceElectronic structureCore (optical fiber)Atomic physicsSpectral lineMolecular physicsCondensed matter physicsChemistryPhysicsNanotechnologyOptoelectronicsGeometryQuantum mechanics

Abstract

fetched live from OpenAlex

The emission line widths of semiconductor nanocrystals yield insight into the factors that give rise to their electronic structure, thereby providing a path for utilizing nanocrystals in light emissive applications. Experiment and theory in conjunction reveal the contributions to line broadening to the core and surface emission bands. As nanocrystals become small, broad emission from the surface becomes prominent. In the case of the core emission, we reveal previously unobserved vibronic contributions in addition to the already well-known electronic structure of the band-edge exciton. As the temperature decreases, broad emission from the surface becomes prominent. This surface emission also exhibits vibronic contributions albeit more strongly. Analysis of the surface emission reveals the existence of a previously unobserved electronic structure of the surface in complete parallel to that of the core. The surface is characterized by a bright and dark state as well as a spectrum of bright states.

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)0.003
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 score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.019
GPT teacher head0.294
Teacher spread0.274 · 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

Citations63
Published2017
Admission routes2
Has abstractyes

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