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Record W1972450197 · doi:10.1021/nl080126a

Ultrafast Exciton Fine Structure Relaxation Dynamics in Lead Chalcogenide Nanocrystals

2008· article· en· W1972450197 on OpenAlexaff
Justin C. Johnson, Kathrine A. Gerth, Qing Song, James E. Murphy, Arthur J. Nozik, Gregory D. Scholes

Bibliographic record

VenueNano Letters · 2008
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChalcogenideNanocrystalExcitonFemtosecondChemical physicsMaterials scienceQuantum dotRelaxation (psychology)FemtochemistrySpectroscopyUltrafast laser spectroscopyMolecular physicsElectronUltrashort pulseCondensed matter physicsNanotechnologyChemistryOptoelectronicsLaserOpticsPhysics

Abstract

fetched live from OpenAlex

The rates of fine structure relaxation in PbS, PbSe, and PbTe nanocrystals were measured on a femtosecond time scale as a function of temperature with no applied magnetic field by cross-polarized transient grating spectroscopy (CPTG) and circularly polarized pump-probe spectroscopy. The relaxation rates among exciton fine structure states follow trends with nanocrystal composition and size that are consistent with the expected influence of material dependent spin-orbit coupling, confinement enhanced electron-hole exchange interaction, and splitting between L valleys that are degenerate in the bulk. The size dependence of the fine structure relaxation rate is considerably different from what is observed for small CdSe nanocrystals, which appears to result from the unique material properties of the highly confined lead chalcogenide quantum dots. Modeling and qualitative considerations lead to conclusions about the fine structure of the lowest exciton absorption band, which has a potentially significant bearing on photophysical processes that make these materials attractive for practical purposes.

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 score0.003
Threshold uncertainty score0.608

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 head0.016
GPT teacher head0.207
Teacher spread0.192 · 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

Citations45
Published2008
Admission routes1
Has abstractyes

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