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Record W2123508655 · doi:10.1021/jp3060872

Azobenzene Photoisomerization under High External Pressures: Testing the Strength of a Light-Activated Molecular Muscle

2012· article· en· W2123508655 on OpenAlexafffund
Thomas A. Singleton, Kevin S. Ramsay, Mirela M. Barsan, Ian S. Butler, Christopher J. Barrett

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2012
Typearticle
Languageen
FieldNeuroscience
TopicPhotoreceptor and optogenetics research
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotoisomerizationAzobenzeneIsomerizationPhotostationary stateChromophorePhotochemistryMaterials scienceChemistryPolymerOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

The photoinduced isomerization and thermal back relaxation of an azobenzene-functionalized polymer poly(disperse red 1 acrylate) were investigated at increasing external pressures up to 1.5 GPa inside a diamond-anvil spectroscopic cell. The thermal cis-trans isomerization was monitored by laser pump-probe spectroscopy, which demonstrated an increase in the half-life of the isomerization process with increasing pressure. Additionally, the cis content of the photostationary state gradually decreased as a function of pressure, with complete arrest of the trans-cis photoisomerization above 1.5 GPa. The fact that the photoswitching behavior however could still be observed beyond 1 GPa is remarkable and is effectively a measure of the strength of the azobenzene chromophore as an artificial muscle. The changes in the Raman shifts of both trans- and cis-azobenzene were also investigated from ambient pressure up to 4 GPa, and no discontinuities were observed in the pressure vs wavenumber plots indicating no change in phase. The cis-trans photoisomerization of azobenzene was shown however to still be inducible at all the pressures investigated, confirming the suitability of these molecules for high-efficiency light actuation.

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)0.001
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 score0.001
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.034
GPT teacher head0.291
Teacher spread0.256 · 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

Citations49
Published2012
Admission routes2
Has abstractyes

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