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Record W4413110330 · doi:10.1021/acs.jpcb.5c03367

Photobasicity-Triggered Twisted Intramolecular Charge Transfer of Push–Pull Chromophores

2025· article· en· W4413110330 on OpenAlexafffund
Austin Pounder, Matteo Pavlovic, Darren Chow, Keenan T. Regan, Aicheng Chen, Stacey D. Wetmore, Richard A. Manderville

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2025
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsUniversity of GuelphUniversity of Lethbridge
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAlberta InnovatesGovernment of OntarioUniversity of Lethbridge
KeywordsProtonationChromophoreIntramolecular forcePhotochemistryFluorescenceChemistryAcceptorExcited stateSolventProtonAbsorption (acoustics)Materials scienceStereochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Fluorogenic probes that undergo excited-state proton transfer (ESPT) and twisted intramolecular charge transfer (TICT) offer tunable fluorescence properties for bioimaging and sensing applications. However, the relationship between ESPT and TICT remains poorly understood in push-pull chromophores. Despite extensive research on photoacids, photobases remain underutilized as fluorescence modulators, and the roles of solvent polarity, acidity, and donor-acceptor strength in governing photobasicity and TICT activation are not well established. We conducted photophysical experiments and (TD)-DFT calculations to explore how protonation and solvent interactions influence fluorescence behavior. Our findings reveal that while protonation consistently induces red-shifted absorption and emission, ESPT efficiency and TICT formation vary widely depending on molecular structure and solvent environment. This work provides new insights into photobasicity-driven fluorescence modulation, offering a foundation for designing next-generation probes with enhanced sensitivity to local acidity, viscosity, and microenvironmental factors.

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 score0.016
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.006
GPT teacher head0.233
Teacher spread0.227 · 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

Citations5
Published2025
Admission routes2
Has abstractyes

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