Excited-State Symmetry Breaking in a Multiple Multipolar Chromophore Probed by Single-Molecule Fluorescence Imaging and Spectroscopy.
Masaaki MitsuiYasushi TakakuraKazuya HirataYoshiki NiihoriYutaka FujiwaraKenji KobayashiPublished in: The journal of physical chemistry. B (2021)
Excited-state symmetry breaking (ESB) has attracted much attention because it is often observed in symmetric multipolar chromophores designed as two-photon absorption/emission materials. Herein, we report an ensemble and single-molecule fluorescence imaging and spectroscopy investigation of ESB in hexakis[4-(p-dioctylaminostyryl)phenylethynyl]benzene(DB6), a two-photon absorber possessing a C6-symmetric π-D6 structure (π = hexaethynylbenzene, D = (p-dioctylaminostyryl)phenyl group) consisting of three equivalent D-π-D moieties. Ensemble and single-molecule measurements and theoretical calculations revealed that DB6 undergoes a photoabsorption process with two orthogonal transition dipole moments, whereas it fluoresces with a single transition dipole moment after one- or two-step ESB upon photoexcitation, depending on the environmental polarity. In nonpolar solvents and polymer films, one of the three D-π-D sites becomes planar, and the excited state is localized on this moiety: a [Dδ+-πδ--Dδ+]* quadrupolar state is formed. In polar solvents, the symmetry is further broken within the planarized D-π-D moiety, and the excited state is localized on one of the two D-π sites; i.e., a D-[πδ--Dδ+]* dipolar state is generated. Hence, DB6 can behave like a multichromophore with multiple emission sites in the molecule, which was demonstrated by stepwise photobleaching under photon antibunching conditions.
Keyphrases
- single molecule
- fluorescence imaging
- living cells
- photodynamic therapy
- ionic liquid
- atomic force microscopy
- monte carlo
- molecular dynamics simulations
- working memory
- convolutional neural network
- density functional theory
- room temperature
- neural network
- solid state
- single cell
- climate change
- life cycle
- human health
- deep learning