Wenkai Chen

Abstract Developing Type‐I photosensitizers is considered as an efficient approach to overcome the deficiency of traditional photodynamic therapy (PDT) for hypoxic tumors. However, it remains a challenge to design photosensitizers for generating reactive oxygen species by the Type‐I process. Herein, we report a series of α,β‐linked BODIPY dimers and a trimer that exclusively generate superoxide radical (O 2 −. ) by the Type‐I process upon light irradiation. The triplet formation originates from an effective excited‐state relaxation from the initially populated singlet (S 1 ) to triplet (T 1 ) states via an intermediate triplet (T 2 ) state. The low reduction potential and ultralong lifetime of the T 1 state facilitate the efficient generation of O 2 −. by inter‐molecular charge transfer to molecular oxygen. The energy gap of T 1 ‐S 0 is smaller than that between 3 O 2 and 1 O 2 thereby precluding the generation of singlet oxygen by the Type‐II process. The trimer exhibits superior PDT performance under the hypoxic environment.

Polymeric carbon nitride is reported to be a promising candidate in environmental catalysis for NO decomposition carried out at elevated temperature. Theoretical calculations support a mechanism where Lewis basic site of g-C(3)N(4) can donate electrons to the adsorbed NO, decreasing the bond order of N-O thus facilitating the reaction.

In this work we show that deep learning (DL) can be used for exploring complex and highly nonlinear multistate potential energy surfaces of polyatomic molecules and related nonadiabatic dynamics. Our DL is based on deep neural networks (DNNs), which are used as accurate representations of the CASSCF ground- and excited-state potential energy surfaces (PESs) of CH2NH. After geometries near conical intersection are included in the training set, the DNN models accurately reproduce excited-state topological structures; photoisomerization paths; and, importantly, conical intersections. We have also demonstrated that the results from nonadiabatic dynamics run with the DNN models are very close to those from the dynamics run with the pure ab initio method. The present work should encourage further studies of using machine learning methods to explore excited-state potential energy surfaces and nonadiabatic dynamics of polyatomic molecules.

A novel organic semiconductor photocatalyst mimicking natural light-harvesting antenna complexes in photosynthetic organisms, a disulfide (–S–S–) bridged C3N3S3polymer, was designed and developed to generate hydrogen from water under visible light irradiation. The artificial conjugated polymer shows high H2-producing activity from the half-reaction of water splitting without the aid of a sacrificial electron donor. The H2-producing efficiency and photo-stability of the catalyst could be improved greatly using Ru and single-wall carbon nanotubes as cocatalysts or by adding a sacrificial donor. The results represent a potential and prospective application of the C3N3S3polymer in solar energy conversion and offer significant guidance to develop more stable and efficient photocatalytic systems based on organic semiconductors.