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Record W2254698181 · doi:10.1021/acs.jpca.5b10312

Laser-Limited Signatures of Quantum Coherence

2015· article· en· W2254698181 on OpenAlexaff
Roel Tempelaar, Alexei Halpin, Philip J. M. Johnson, Jianxin Cai, R. Scott Murphy, Jasper Knoester, R. J. Dwayne Miller, Thomas L. C. Jansen

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of ReginaUniversity of Toronto
Fundersnot available
KeywordsCoherence (philosophical gambling strategy)Excited stateQuantumObservablePhysicsLaserSpectral lineSpectroscopyPhotovoltaicsQuantum stateQuantum mechanicsPhotovoltaic system

Abstract

fetched live from OpenAlex

Quantum coherence is proclaimed to promote efficient energy collection by light-harvesting complexes and prototype organic photovoltaics. However, supporting spectroscopic studies are hindered by the problem of distinguishing between the excited state and ground state origin of coherent spectral transients. Coherence amplitude maps, which systematically represent quantum beats observable in two-dimensional (2D) spectroscopy, are currently the prevalent tool for making this distinction. In this article, we present coherence amplitude maps of a molecular dimer, which have become significantly distorted as a result of the finite laser bandwidth used to record the 2D spectra. We argue that under standard spectroscopic conditions similar distortions are to be expected for compounds absorbing over a spectral range similar to, or exceeding, that of the dimer. These include virtually all photovoltaic polymers and certain photosynthetic complexes. With the distortion of coherence amplitude maps, alternative ways to identify quantum coherence are called for. Here, we use numerical simulations that reproduce the essential photophysics of the dimer to unambiguously determine the excited state origin of prominent quantum beats observed in the 2D spectral measurements. This approach is proposed as a dependable method for coherence identification.

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 score0.011
Threshold uncertainty score0.269

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 head0.017
GPT teacher head0.272
Teacher spread0.254 · 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

Citations22
Published2015
Admission routes1
Has abstractyes

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