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Record W2334210916 · doi:10.1103/physrevb.88.045432

Effects of dopants on the band structure of quantum dots: A theoretical and experimental study

2013· article· en· W2334210916 on OpenAlexfundno aff
Joshua Wright, Robert W. Meulenberg

Bibliographic record

VenuePhysical Review B · 2013
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsnot available
FundersLaboratório Nacional de Luz SíncrotronNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Science Foundation
KeywordsX-ray absorption spectroscopyQuantum dotDopantMaterials scienceBand gapDopingCondensed matter physicsSpectroscopyPotential wellConduction bandMolecular physicsAbsorption spectroscopyPhysicsNanotechnologyElectronOptoelectronicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

In this article, we present a theoretical framework that provides predictable results on band gap modifications due to the addition of dopants into CdSe quantum dots (QDs). A theoretical model is developed that predicts a lowering of the conduction band minimum (CBM) due to hybridization. We then use x-ray absorption spectroscopy (XAS) at the Cd ${M}_{3}$-edge to determine the effects of chemical doping on the conduction band (CB) of the QD. Analysis of the XAS onset energy provides evidence for a lowering of the CBM, with our calculations yielding results comparable to experiment within 0.02 eV for tested materials. Also present in the XAS data is a distinct shift of the Cd ${M}_{3}$-edge peak maximum as a function of particle size, suggesting this peak can be used as a tracer to probe the angular momentum resolved shifts in the CB states due to quantum confinement. Our theoretical model can model a variety of dopants and theoretically predict the shift in the energy levels, and should be generalizable towards predicting similar behavior in other materials.

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 score0.035
Threshold uncertainty score0.543

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 head0.011
GPT teacher head0.278
Teacher spread0.266 · 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

Citations9
Published2013
Admission routes1
Has abstractyes

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