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Record W2020354001 · doi:10.1021/nl1018673

Role of Symmetry Breaking on the Optical Transitions in Lead-Salt Quantum Dots

2010· article· en· W2020354001 on OpenAlexaff
Gero Nootz, Lázaro A. Padilha, Peter D. Olszak, Scott Webster, David J. Hagan, Eric W. Van Stryland, Larissa Levina, Vlad Sukhovatkin, Lukasz Brzozowski, Edward H. Sargent

Bibliographic record

VenueNano Letters · 2010
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAnisotropyQuantum dotSpectral linePoint reflectionPhotonAbsorption spectroscopyChemistryIsotropyAbsorption (acoustics)Condensed matter physicsSemiconductorSymmetry breakingMolecular physicsAtomic physicsPhysicsOptoelectronicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

The influence of quantum confinement on the one- and two-photon absorption spectra (1PA and 2PA) of PbS and PbSe semiconductor quantum dots (QDs) is investigated. The results show 2PA peaks at energies where only 1PA transitions are predicted and 1PA peaks where only 2PA transitions are predicted by the often used isotropic k x p four-band envelope function formalism. The first experimentally identified two-photon absorption peak coincides with the energy of the first one photon allowed transition. This first two-photon peak cannot be explained by band anisotropy, verifying that the inversion symmetry of the wave functions is broken and relaxation of the parity selection rules has to be taken into account to explain optical transitions in lead-salt QDs. Thus, while the band anisotropy of the bulk semiconductor plays a role in the absorption spectra, especially for the more anisotropic PbSe QDs, a complete model of the absorption spectra, for both 1PA and 2PA, must also include symmetry breaking of the quantum confined wave functions. These studies clarify the controversy of the origin of spectral features in lead-salt 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)0.001
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 score0.025
Threshold uncertainty score0.356

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)0.0000.000

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 head0.014
GPT teacher head0.225
Teacher spread0.211 · 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

Citations56
Published2010
Admission routes1
Has abstractyes

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