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Gold nanoclusters were synthesized and homogeneously distributed on boron nitride nanosheets (BNNSs) to form AuNC@BNNSs nanohybrids. Compared to pure gold nanoclusters, the nanohybrid not only exhibits much better catalytic activity for the reduction of 4‐nitrophenol (4‐NP), but also prevents gold nanoclusters from aggregation. We found that the catalytic performance of AuNC@BNNSs nanohybrid increased with decreasing pH and increasing temperature of the reaction environment. The catalytic mechanism of the nanohybrid was thoroughly explored by density functional theory (DFT). It was concluded that the catalytic activity should be caused by repeated electron transfer on HOMO between 4‐NP and BNNSs, which was mediated by the surface‐bound gold nanoclusters. Furthermore, it was observed that the actual reducing agent is molecular hydrogen rather than borohydride. The present methodology could be generalized to the synthesis of other metallic nanoparticle@BNNSs nanohybrids as promising heterogeneous catalysts for varied catalytic reactions.

Ga-based liquid-metal nanoparticles as a lubricant additive were used for the first time, and the differences of the lubrication mechanisms were clarified between the solid and liquid nanoparticles.

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In industry, defect detection involves two kinds of tasks: defect classification and location, which make it difficult to ensure the accuracy of both, and also make the task still challenging in practical application. Based on the analysis of the advantages and disadvantages of the current defect detection method, this paper proposes a defect detection method based on attention mechanism and multi-scale maxpooling (MSMP). In order to effectively improve the detection accuracy of the model, we use Resnet50 as the pre-training network construct two-stage detection model which is used to be the baseline network, and introduce the attention mechanism and MSMP module on this basis. The attention mechanism can enhance the features of the feature map extracted in each stage of Resnet50, so that the network concentrates on the effective areas for the final detection results, and ignores the background areas that are invalid or even unfavorable for detection. The proposed MSMP can incrementally enhance the receptive field, distinguish the most significant context features, and effectively improve detection precision. The proposed method is used to train and test on the NEU-DET dataset. Compared with the baseline network without any improvement, the proposed method in this paper achieves 3.65% mAP performance improvement. Meanwhile, our method achieves a performance improvement of 3.65% mAP. In addition, compared with the feature fusion mechanism, our method improves 4.03% mAP. Moreover, compared with the attention mechanisms such as spatial attention and SE block, our method improves 1.51%/1.03% mAP. Furthermore, compared with the one-stage detection algorithm SSD/YOLO-V4, the proposed method improves 5.01%/4.92% mAP. In addition, the classification accuracy of our model is as high as 94.73%.

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