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Record W1980133891 · doi:10.1149/06105.0039ecst

(Invited) The Interplay of Particle Size and Doping Concentration on the Electronic Structure of Doped Quantum Dots

2014· article· en· W1980133891 on OpenAlexfundno aff
Joshua Wright, Stuart Lawson, Robert W. Meulenberg

Bibliographic record

VenueECS Transactions · 2014
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsnot available
FundersLaboratório Nacional de Luz SíncrotronNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsXANESDopingQuantum dotMaterials sciencePhotoluminescenceElectronic structureAbsorption spectroscopyAbsorption (acoustics)SpectroscopyParticle sizeAbsorption edgeConduction bandOptoelectronicsNanotechnologyAnalytical Chemistry (journal)Condensed matter physicsBand gapChemistryPhysical chemistryElectronOpticsPhysics

Abstract

fetched live from OpenAlex

We present our efforts towards investigating the effects of copper doping on the electronic structure of CdSe QDs. Both x-ray absorption near edge structure spectroscopy (XANES) and theoretical modeling are used to examine effects of hybridization on the conduction band minimum (CBM) in doped CdSe quantum dots (QDs). Experimentally, Cd M 3 -edge XANES provides evidence for a lowering of the CB minimum for Cu doped CdSe QDs that is dependent on Cu concentration. Theoretical modeling suggests the effects of hybridization between Cu and Cd atoms in the QD can explain our experimental results. The hybridization effect leads to active emissive states below the CBM resulting in tunable near-infrared photoluminescence. Our work shows that a simple chemical model can provide a predictive tool towards probing the effects of hybridization on the CB levels in QDs.

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)0.000
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 score0.008
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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