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Record W2031423534 · doi:10.1063/1.3350871

Acoustic phonon strain induced mixing of the fine structure levels in colloidal CdSe quantum dots observed by a polarization grating technique

2010· article· en· W2031423534 on OpenAlexaff
Vanessa M. Huxter, Gregory D. Scholes

Bibliographic record

VenueThe Journal of Chemical Physics · 2010
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDephasingPhononCondensed matter physicsQuantum dotAnisotropyGratingPolarization (electrochemistry)PhysicsMaterials scienceMolecular physicsOpticsOptoelectronicsChemistry

Abstract

fetched live from OpenAlex

Acoustic phonon modes in colloidal semiconductor nanocrystals are of significant interest due to their role in dephasing and as the main component of homogeneous line broadening. Despite their importance, these modes have proven elusive and have only recently been experimentally observed. This paper expands on results presented in our earlier paper [V. M. Huxter, A. Lee, S. S. Lo, et al., Nano Lett. 9, 405 (2008)], where a cross polarized heterodyne detected ultrafast transient grating (CPH-3TG) technique was used to observe the acoustic phonon mode. In the present work, we explain the origin of the observed quantum beat in the CPH-3TG signal. Further experiments are presented that show that the observed quantum beat, which arises from a coherent acoustic phonon mode in the nanocrystals, appears in anisotropy-type signals. The action of this mode induces a periodic strain in the nanocrystal that lowers the symmetry of the unit cell, mixing the fine structure states and their transition dipole moments. This mixing is manifested in anisotropy signals as a depolarization, which periodically modifies the rotational averaging factors. Through observation of the acoustic phonon mode using the CPH-3TG optical technique, it is possible to access its microscopic (atomic-level) basis and to use it as a probe to quantify changing macroscopic (whole particle) material parameters.

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 score0.006
Threshold uncertainty score0.281

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 integrity0.0000.001
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 head0.028
GPT teacher head0.240
Teacher spread0.212 · 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

Citations26
Published2010
Admission routes1
Has abstractyes

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