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Photocatalytic reduction of CO2with H2O is thought as an environment friendly approach for sustainable development. It is highly desirable but remains challenging to develop photocatalyst with metal-free site for CO2reduction. Here, we fabricated a heterostructure by integrating azine-based COF with ZnIn2S4, where the azine unit in COF acts as metal-free sites for CO2adsorption and reduction. The as-formed interfused heterointerface in the heterostructure ensures the effective electron transfer from ZnIn2S4across the interface to the COF, resulting in a spatial separation of redox sites for CO2reduction and water oxidation. The hybrid catalyst exhibits remarkably enhanced photocatalytic activity for CO2reduction to CO, which is 8 and 6 times than the COF and ZnIn2S4, respectively, and is even comparable with some metal-catalyzed CO2reduction systems. This study provides a new paradigm to mimic natural photosynthesis by using metal-free site for CO2reduction.

Magnetic arc blow during the welding of submersible pressure hulls can severely impact the weld quality. Real-time estimation of the arc posture by the teaching-replay welding robot is a crucial and challenging step for further correction of the magnetic blow. This paper proposes a lightweight deep learning model capable of real-time estimation of the welding arc posture. The model takes arc images as input and outputs the angle of arc magnetic blow end-to-end. Specifically, to enable the model to learn the prior knowledge of human perception of arc magnetic blow angles, a training strategy utilizing keypoint detection as an auxiliary task has been proposed. This approach enhances the estimation accuracy of the model without incurring additional inference costs. Additionally, an efficient multi-scale attention (EMA) mechanism was integrated into the angle prediction branch to facilitate the learning of long-range feature dependencies. To confirm the effectiveness of the model, an arc magnetic blow image dataset was constructed for training and testing. The experimental results show that the model achieves a cumulative score (CS3) of 98.47 % and a mean absolute error (MAE) of 0.9985° during testing. The model achieves an inference speed of 70.49 FPS on an Intel® Core™ i7-8750H CPU, which satisfies the criteria for real-time monitoring.