Research Advances in Excited State Intramolecular Proton Transfer Fluorescent Probes Based on Combined Fluorescence Mechanism

Lu, Xianlin; He, Wei

doi:10.1016/s1872-2040(20)60078-0

“…In this context, the application of ESIPT-fluorophores for a plethora of sensing purposes has become one of the mainstream interdisciplinary directions fusing together analytical, organic and physical chemistry. [56][57][58][59][60][61][62][63][64][65][66][67][68] Although the molecules of ESIPT-fluorophores can bind metal ions (which is used for the detection of metals), this phenomenon is typically coupled with their deprotonation and the disappearance of ESIPT in the metal complex molecules (Scheme 2, top). [69][70][71][72][73][74][75][76] Nevertheless, the purposeful design of specific sites for metal coordination in the molecules of ESIPT-fluorophores paves the way toward metal ion binding without the deprotonation of ESIPT-fluorophores (Scheme 2, bottom).…”

Section: Introductionmentioning

confidence: 99%

“…In this context, the application of ESIPT-fluorophores for a plethora of sensing purposes has become one of the mainstream interdisciplinary directions fusing together analytical, organic and physical chemistry. 56–68…”

Section: Introductionmentioning

confidence: 99%

Tuning ESIPT-coupled luminescence by expanding π-conjugation of a proton acceptor moiety in ESIPT-capable zinc(ii) complexes with 1-hydroxy-1H-imidazole-based ligands

Shekhovtsov

¹

,

Николаенкова

²

,

Berezin

³

et al. 2022

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“…Also, for the influence of excited-state proton transfer (ESIPT) effect on emission spectra, we also counted typical ESIPT patterns in the fingerprint. [37] Additionally, we used one position to represent the scale of conjugation -the number of aromatic atoms (In this work, we use the one-sixth of the number of aromatic atoms as the approximate number of aromatic rings for readability). Of course, the sp 2 carbon atoms should have been counted as conjugation as well.…”

Section: Design Strategymentioning

confidence: 99%

Bring Chemical Intuition to Chips: Transferable Chemical-intuitive Model to Predict Photophysics of Organic Aggregates

Gong

¹

,

Deng

²

,

Xie

³

et al. 2022

Preprint

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While machine-learning methods indicated good adaptability for machine-learning algorithms in the pan-chemistry field by its breakthroughs in pharmacy. Materials research still benefits from such new techniques fewer due to the inconsistency in the paradigm of study in the diversely different subareas which demand special treatment individually. In this contribution, we proposed an innovative design of the embedding method, which is inspired by chemical intuition, to bring neural networks into the field for modelling photophysics of the organic light-emitting materials in condensed states. We outline this framework and demonstrate its successful implementation in the predictive classification of fluorophores by its mechanisms, direction of spectra shift from solution to solid-state, and regression of spectral features, including emission peaks wavelengths in pristine solid-state and nano-aggregates. Our work may serve as an example for a specific area of materials research to transfer the empirical chemical intuition into machine-learning models and build comprehensive performance-oriented pre-screening systems to develop new compounds with demanded characters.

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“…Also, for the influence of excited-state proton transfer (ESIPT) effect on emission spectra, we also counted typical ESIPT patterns in the fingerprint. [19] Additionally, we used one position to represent the scale of conjugation -the number of aromatic atoms (In this work, we use the one-sixth of the number of aromatic atoms as the approximate number of aromatic rings for readability). Of course, the sp 2 carbon atoms should have been counted as conjugation as well.…”

Section: The Photophysics-oriented Strategy Of Feature Selectionmentioning

confidence: 99%

Bring Chemical Intuition to Chips: Transferable Chemical-intuitive Model to Predict Photophysics of Organic Aggregates

Gong

¹

,

Tang

²

2022

Preprint

0

View full text Add to dashboard Cite

While machine-learning methods indicated good adaptability for machine-learning algorithms in the pan-chemistry field by its breakthroughs in pharmacy. Materials research still benefits from such new techniques fewer due to the inconsistency in the paradigm of study in the diversely different subareas which demand special treatment individually. In this contribution, we proposed an innovative design of the embedding method, which is inspired by chemical intuition, to bring neural networks into the field for modelling photophysics of the organic light-emitting materials in condensed states. We outline this framework and demonstrate its successful implementation in the predictive classification of fluorophores by its mechanisms, direction of spectra shift from solution to solid-state, and regression of spectral features, including emission peaks wavelengths in pristine solid-state and nano-aggregates. Our work may serve as an example for a specific area of materials research to transfer the empirical chemical intuition into machine-learning models and build comprehensive performance-oriented pre-screening systems to develop new compounds with demanded characters.

show abstract

Research Advances in Excited State Intramolecular Proton Transfer Fluorescent Probes Based on Combined Fluorescence Mechanism

Cited by 35 publications

References 63 publications

Tuning ESIPT-coupled luminescence by expanding π-conjugation of a proton acceptor moiety in ESIPT-capable zinc(ii) complexes with 1-hydroxy-1H-imidazole-based ligands

Tuning ESIPT-coupled luminescence by expanding π-conjugation of a proton acceptor moiety in ESIPT-capable zinc(ii) complexes with 1-hydroxy-1H-imidazole-based ligands

Bring Chemical Intuition to Chips: Transferable Chemical-intuitive Model to Predict Photophysics of Organic Aggregates

Bring Chemical Intuition to Chips: Transferable Chemical-intuitive Model to Predict Photophysics of Organic Aggregates

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