Unveiling the sensing mechanism of a Nile Red-naphthoquinone H₂S fluorescent probe: theoretical calculation and deep learning prediction.
Journal:
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Published Date:
Jun 22, 2026
Abstract
Hydrogen sulfide (H₂S) is a vital endogenous gasotransmitter implicated in numerous physiological and pathological processes; thus, its precise detection in biological and environmental systems is of great significance. This work is a theoretical computational study aiming to interpret the sensing mechanism of a previously reported Nile Red-naphthoquinone fluorescent probe NR-H₂S. A comprehensive investigation combining systematic theoretical calculations and deep learning prediction was carried out. Theoretical analyses including average local ionization energy (ALIE), density of states (DOS), charge decomposition analysis (CDA) and electron-hole distribution provide theoretical evidence that the fluorescence quenching of NR-H₂S originates from a photoinduced electron transfer (PET) process. Specifically, ALIE identified electrophilic reaction sites of the molecules. DOS and CDA results revealed that the highest occupied molecular orbital (HOMO) is dominated by the Nile Red moiety, while the lowest unoccupied molecular orbital (LUMO) is localized on the 2,3-dichloro-1,4-naphthoquinone unit, which facilitates efficient PET quenching. Furthermore, the FLSF (FLuorescence prediction with fluoroScaFfold-driven model) deep learning model was adopted to quantitatively predict key photophysical properties of NR-H₂S and its reaction product NR-OH. The predictions from FLSF are broadly consistent with the low photoluminescence quantum yield of NR-H₂S caused by PET quenching and the distinct fluorescence enhancement of NR-OH, which agrees well with experimental trends and quantum chemical results. This study not only offers a reliable reference for the rational design of H₂S-selective fluorescent probes, but also demonstrates the complementary value of quantum chemical calculations and artificial intelligence in accelerating the research and development of advanced optical functional materials.
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