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Record W2088909565 · doi:10.1071/ch11297

Using Quenching Kinetics and Thermodynamics of Amino-Fluorophores as Empirical Tools for Predicting Boronic Acid Sensors Suitable for Use in Physiological Conditions

2011· article· en· W2088909565 on OpenAlexafffund
Nicholas G. S. McGregor, Christophe Pardin, W. G. Skene

Bibliographic record

VenueAustralian Journal of Chemistry · 2011
Typearticle
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsUniversity of OttawaBombardier (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaAlexander von Humboldt-Stiftung
KeywordsChemistryExergonic reactionBoronic acidQuenching (fluorescence)KineticsElectron transferPhenylboronic acidFlufenamic acidFluorophoreIminePhotochemistryFluorescenceOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

A series of water-soluble 1-amino-naphthalenes and 2-amino-fluorenes are prepared. These serve as model fluorophores for measuring the thermodynamics and kinetics of fluorescence quenching with phenylboronic acids and aliphatic amines. Steady-state and time-resolved fluorescence quenching kinetics are investigated using the Stern–Volmer method. Diffusion limited quenching constants and exergonic thermodynamics of electron transfer are derived for the 5-amino-1-napthol and 2-aminofluorene derivatives with phenylboronic acid and/or an aliphatic imine. No quenching and endergonic thermodynamics or electron transfer are observed for 5-sulfonamide, 5-sulfonic acid, or 5-hydroxy-7-sulfonic acid aminonaphthalene derivatives. Boronic acid sensors synthesized from these aminofluorophores by reductive amination with 2-formylphenylboronic acid undergo fluorescence revival in the presence of saccharides only when the fluorophore demonstrates diffusion limited quenching kinetics and exergonic thermodynamics of electron transfer with the boronic acid or imine quenchers. Thus, these two properties are suitable empirical tools for predicting saccharide-induced fluorescence revival of boronic acid sensors.

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 score0.005
Threshold uncertainty score0.573

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 head0.117
GPT teacher head0.324
Teacher spread0.208 · 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

Citations7
Published2011
Admission routes2
Has abstractyes

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