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Record W1975728089 · doi:10.1103/physrevb.80.035118

Excitation-energy dependence of transient grating spectroscopy in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>β</mml:mi></mml:math>-carotene

2009· article· en· W1975728089 on OpenAlexaff
Mitsuru Sugisaki, Masazumi Fujiwara, Selvakumar V. Nair, Harry E. Ruda, Richard J. Cogdell, Hideki Hashimoto

Bibliographic record

VenuePhysical Review B · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Toronto
FundersNissan Global Foundation
KeywordsExcitationPhysicsExcited stateEnergy (signal processing)Atomic physicsGratingAbsorption (acoustics)Ultrafast laser spectroscopyCoherence (philosophical gambling strategy)Relaxation (psychology)SpectroscopyOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Transient grating (TG) signals from $\ensuremath{\beta}$-carotene were measured at various excitation energies (wavelengths). Clear coherent oscillations with a period of a few tens of femtoseconds were observed when the excitation energy was tuned in the vicinity of the absorption edge. It was found that the TG signal is very sensitive to the excitation energy. When the TG signal is measured near the absorption maximum, following the coherent spike at the origin of the time axis, weak coherent oscillations are observed superimposed on a slowly varying background. The rise time of this slowly varying background is 0.4 ps and is followed by the slow decay with a 5 ps time constant. As the excitation energy is decreased the coherent oscillations become more prominent. The coherent oscillations and the slowly varying background become very weak again when the excitation energy is lower than the absorption edge. The TG signals were calculated using two sets of energy and Feynman diagrams to investigate possible pathways of the electronic internal conversion and vibronic decoherence processes. The modeling indicates that the vibronic coherence, initially established by pumping into the excited state ${\text{S}}_{2}$, instantaneously decays due to relaxation into an intermediate state ${\text{S}}_{\text{x}}$, which is located between ${\text{S}}_{2}$ and ${\text{S}}_{1}$. Possible interpretation of the nature of the ${\text{S}}_{\text{x}}$ state is discussed.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.881

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 head0.015
GPT teacher head0.276
Teacher spread0.261 · 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 designTheoretical or conceptual
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

Citations21
Published2009
Admission routes1
Has abstractyes

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