Observation of Ultrafast Interfacial Exciton Formation and Relaxation in Graphene/MoS2 Heterostructure.

Heterostructures constructed from graphene and transition metal dichalcogenides (TMDs) have established a new platform for optoelectronic applications. After a large number of studies, one intriguing debate is the existence of the interfacial exciton in graphene/TMDs. Hereby, by combined optical pump-terahertz probe spectroscopy and transient absorption spectroscopy, we report the observation of the interfacial exciton in graphene/MoS2 heterostructure. With the photon energy well below the band gap of monolayer MoS2, the hot electrons of graphene are transferred to MoS2 within 0.5 ps; subsequently, the relaxation of the holes in graphene and electrons in MoS2 shows an identical time scale of 15-18 ps, which manifests the formation and relaxation of the interfacial exciton in the heterostructure following photoexcitation. Moreover, a model of the carrier heating and photogating effect in graphene is proposed to estimate the amount of transferred charge, which agrees well with the experimental results. Our study provides insights into the dynamics of graphene-based heterostructure interfacial non-equilibrium carriers.

Hydrogen Evolution Prediction for Alternating Conjugated Copolymers Enabled by Machine Learning with Multidimension Fragmentation Descriptors.

Hydrogen evolution by alternating conjugated copolymers has attracted much attention in recent years. To study alternating copolymers with data-driven strategies, two types of multidimension fragmentation descriptors (MDFD), structure-based MDFD (SMDFD), and electronic property-based MDFD (EPMDFD), have been developed with machine learning (ML) algorithms for the first time. The superiority of SMDFD-based models has been demonstrated by the highly accurate and universal predictions of electronic properties. Moreover, EPMDFD-based, experimental-parameter-free ML models were developed for the prediction of the hydrogen evolution reaction, displaying excellent accuracy (real-test accuracy = 0.91). The combination of explainable ML approaches and first-principles calculations was employed to explore photocatalytic dynamics, revealing the importance of electron delocalization in the excited state. Virtual designing of high-performance candidates can also be achieved. Our work illustrates the huge potential of ML-based material design in the field of polymeric photocatalysts toward high-performance photocatalysis.