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您的检索式：作者名="Yinjun Tang"
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| 1 | Metal atom doping-induced S-scheme heterojunction boosts the photoelectric response显示文摘Carrier migration path and driving forces are two crucial factors for charge separation of heterojunction with efficient photoelectric response from the thermodynamic and kinetic perspectives,respectively.Constructing the S-scheme heterojunction and achieving an efficient migration path for space charge separation have aroused great interest,while a thorough insight into tuning interfacial band bending for S-scheme heterojunction is absent.Herein,we report a class of Zn atom-doped CeO_(2)/g-C_(3)N_(4) heterostructure for achieving a new carrier migration path conversion from inferior type-II to advanced S-scheme.Zn-dependent volcano-type plot for Zn-CeO_(2) is established to tune the Fermi level of CeO_(2).The built-in electric field for carrier flow dynamics strengthens when coupling with g-C_(3)N_(4),which significantly boosts the photoelectric response.Based on the intrinsic enzymelike activity of Zn-CeO_(2),we further demonstrate that the Zn-CeO_(2)/g-C_(3)N_(4) S-scheme heterojunction can be explored for constructing a sensitive nanozymatic photoelectrochemical biosensor for the detection of acetylcholinesterase. | Mingwang Liu Jing Wen Ying Qin Jinli Li Yinjun Tang Lei Jiao Yu Wu Qie Fang Lirong Zheng Xiaowen Cui Wenling Gu Chengzhou Zhu Liuyong Hu Shaojun Guo | 2023 | Science China Chemistry2023，66，4： | 0 |
| 2 | Bearings Intelligent Fault Diagnosis by 1-D Adder Neural Networks显示文摘Integrated with sensors,processors,and radio frequency(RF)communication modules,intelligent bearing could achieve the autonomous perception and autonomous decision-making,guarantying the safety and reliability during their use.However,because of the resource limitations of the end device,processors in the intelligent bearing are unable to carry the computational load of deep learning models like convolutional neural network(CNN),which involves a great amount of multiplicative operations.To minimize the computation cost of the conventional CNN,based on the idea of AdderNet,a 1-D adder neural network with a wide first-layer kernel(WAddNN)suitable for bearing fault diagnosis is proposed in this paper.The proposed method uses the l1-norm distance between filters and input features as the output response,thus making the whole network almost free of multiplicative operations.The whole model takes the original signal as the input,uses a wide kernel in the first adder layer to extract features and suppress the high frequency noise,and then uses two layers of small kernels for nonlinear mapping.Through experimental comparison with CNN models of the same structure,WAddNN is able to achieve a similar accuracy as CNN models with significantly reduced computational cost.The proposed model provides a new fault diagnosis method for intelligent bearings with limited resources. | Jian Tang Chao Wei Quanchang Li Yinjun Wang Xiaoxi Ding Wenbin Huang | 2022 | Journal of Dynamics, Monitoring and Diagnostics2022，1，3： | 0 |
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