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Record W2486758927 · doi:10.1021/acs.jpca.6b05957

Influence of Protonation State on the Excited State Dynamics of a Photobiologically Active Ru(II) Dyad

2016· article· en· W2486758927 on OpenAlexafffund
C. L. Reichardt, Tariq Sainuddin, Maria Wächtler, Susan Monro, Stephan Kupfer, Julien Guthmuller, Stefanie Gräfe, Sherri A. McFarland, Benjamin Dietzek

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2016
Typearticle
Languageen
FieldChemistry
TopicRadical Photochemical Reactions
Canadian institutionsAcadia University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchDeutsche ForschungsgemeinschaftCanada Foundation for InnovationNova Scotia Research Innovation TrustVerband der Chemischen Industrie
KeywordsProtonationChemistryImidazoleRutheniumTime-dependent density functional theoryPhotochemistryExcited stateDeprotonationUltrafast laser spectroscopyLigand (biochemistry)Density functional theorySpectroscopyComputational chemistryIonStereochemistryAtomic physicsCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

The influence of ligand protonation on the photophysics of a ruthenium (Ru) dyad bearing the 2-(1-pyrenyl)-1H-imidazo[4,5-f][1,10]-phenanthroline (ippy) ligand was investigated by time-resolved transient absorption spectroscopy. It was found that changes in the protonation state of the imidazole group led to changes in the electronic configuration of the lowest lying excited state. Formation of the fully deprotonated imidazole anion resulted in excited state signatures that were consistent with a low-lying intraligand (IL) triplet state. This assignment was supported by time-dependent density functional theory (TDDFT) calculations. IL triplet states have been suggested to be potent mediators of photodynamic effects. Thus, these results are of interest in the design of Ru metal complexes as photosensitizers (PSs) for photodynamic therapy (PDT).

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 score0.010
Threshold uncertainty score0.346

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.243
Teacher spread0.235 · 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

Citations33
Published2016
Admission routes2
Has abstractyes

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