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Photocatalytic CO 2 reduction using solar energy is a promising way to obtain renewable-energy sources for replacing fossil fuels. Through a hydrothermal process, we successfully designed and synthesized three-dimensional (3D) flower-like structured SnS 2 with a sheet-like structured quasi-hexagon as the building block. The 3D hierarchical structure is conducive to light capture and absorption, the sheet structure can shorten the transmission path and promote separation of the carriers, and the self-supporting effect can effectively prevent catalyst agglomeration during the catalytic reaction. Therefore, when used in photocatalytic CO 2 reduction, SnS 2 with a flower-like structure showed excellent photocatalytic performance compared with SnS 2 nanoparticles (NPs) under visible-light irradiation with a gas−solid reaction system.

Emotional support conversation aims at reducing the emotional distress of the help-seeker, which is a new and challenging task. It requires the system to explore the cause of help-seeker's emotional distress and understand their psychological intention to provide supportive responses. However, existing methods mainly focus on the sequential contextual information, ignoring the hierarchical relationships with the global cause and local psychological intention behind conversations, thus leads to a weak ability of emotional support. In this paper, we propose a Global-to-Local Hierarchical Graph Network to capture the multi-source information (global cause, local intentions and dialog history) and model hierarchical relationships between them, which consists of a multi-source encoder, a hierarchical graph reasoner, and a global-guide decoder. Furthermore, a novel training objective is designed to monitor semantic information of the global cause. Experimental results on the emotional support conversation dataset, ESConv, confirm that the proposed GLHG has achieved the state-of-the-art performance on the automatic and human evaluations.

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