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Record W2331403627 · doi:10.1021/jp501700t

Excitation of Biomolecules with Incoherent Light: Quantum Yield for the Photoisomerization of Model Retinal

2014· article· en· W2331403627 on OpenAlexaff
Timur V. Tscherbul, Paul Brumer

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2014
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhotoisomerizationConical intersectionQuantumQuantum yieldIsomerizationExcited statePhysicsRelaxation (psychology)Yield (engineering)ExcitationChemistryMolecular physicsQuantum mechanicsAtomic physicsFluorescence

Abstract

fetched live from OpenAlex

Cis-trans isomerization in retinal, the first step in vision, is often computationally studied from a time-dependent viewpoint. Motivation for such studies lies in coherent pulsed laser experiments that explore the isomerization dynamics. However, such biological processes take place naturally in the presence of incoherent light, which is expected to excite a nonevolving mixture of stationary states. Here the isomerization problem is considered from the latter viewpoint and applied to a standard two-state, two-mode linear vibronic coupling model of retinal that explicitly includes a conical intersection between the ground and first excited electronic states. The calculated quantum yield at 500 nm agrees well with both the previous time-dependent calculations of Hahn and Stock (0.63) [ J. Phys. Chem. B 2000, 104, 1146-1149 ] and with experiment (0.65 ± 0.01), as does its wavelength dependence. Significantly, the effects of environmental relaxation on the quantum yield in this well-established model are found to be negligible. The results make clear the connection of the photoisomerization quantum yield to properties of stationary eigenstates, providing alternate insights into conditions for yield optimization.

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 score0.221
Threshold uncertainty score0.133

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 head0.039
GPT teacher head0.304
Teacher spread0.265 · 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

Citations32
Published2014
Admission routes1
Has abstractyes

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