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Record W2113877182 · doi:10.1039/c3fd00009e

Designs for molecular circuits that use electronic coherence

2013· article· en· W2113877182 on OpenAlexafffund
Francesca Fassioli, Daniel G. Oblinsky, Gregory D. Scholes

Bibliographic record

VenueFaraday Discussions · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Toronto
FundersU.S. Air ForceNatural Sciences and Engineering Research Council of CanadaDefense Advanced Research Projects Agency
KeywordsCoherence (philosophical gambling strategy)ChromophoreQuantumElectronic circuitEnergy flowQuantum opticsEnergy transferComputer sciencePhysicsOptoelectronicsEnergy (signal processing)Quantum mechanicsChemical physics

Abstract

fetched live from OpenAlex

The mounting evidence of recent years regarding long-lived coherent dynamics of electronic excitations in several light-harvesting antenna proteins suggests the possibility of realizing and exploiting light-initiated quantum dynamics in synthetic molecular devices based on electronic energy transfer. Inspired by the field of molecular logic, we focus this discussion on the prospect of using quantum coherence to control the direction of energy flow in a molecular circuit. As a prototype system we consider a circuit consisting of three chromophores that deliver energy to two trap chromophores. Our aim is to control to which trap the energy is more likely to be delivered. This is achieved by switching one of the circuit chromophores ON and OFF from the system, such that the direction of energy flow substantially changes from the ON and OFF states of the circuit. We find that quantum coherence can allow a significant ability to direct energy transfer in the circuit. However, when realistic levels of noise are added, quantum coherence only slightly improves the ability to direct electronic energy in comparison to a classical hopping mechanism.

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 score0.151
Threshold uncertainty score0.945

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)0.0010.000

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 head0.036
GPT teacher head0.277
Teacher spread0.241 · 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

Citations13
Published2013
Admission routes2
Has abstractyes

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