Photonics Research, Volume. 12, Issue 5, 959(2024)
Optical trapping-enhanced probes designed by a deep learning approach
Fig. 1. (a) The schematic of the trapped SSN nanoparticle, which is made of Si (magenta) and
Fig. 2. The relationship between the axial stiffness
Fig. 3. Architecture of the DL network based on the NN-PSO algorithm, where the input is the size parameters and the output is the
Fig. 4. The
Fig. 5. The
Fig. 6. The fabrication process of SSN nanoparticles. (The legend is the color coding for different materials.)
Fig. 7. Characterization of SSN nanoparticles. (a) and (b) SEM images of the sample shown In step (8). (d) Height of the unit cell as measured by AFM.
Fig. 8. To determine the exact dissolution time of the Cr sacrificial layer. (a) 47 s, (b) 80 s, (c) 120 s, (d) 180 s, (e) 7 min. (f) A small drop of DI water is dropped on the surface of the samples.
Fig. 9. The trapping efficiency as a function of the radius of the amorphous
Fig. 10. The relationship between torques and the angular displacement for two shapes of SSN nanoparticles.
Fig. 11. Relationship between rotation angle
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Miao Peng, Guangzong Xiao, Xinlin Chen, Te Du, Tengfang Kuang, Xiang Han, Wei Xiong, Gangyi Zhu, Junbo Yang, Zhongqi Tan, Kaiyong Yang, Hui Luo, "Optical trapping-enhanced probes designed by a deep learning approach," Photonics Res. 12, 959 (2024)
Category: Nanophotonics and Photonic Crystals
Received: Jan. 3, 2024
Accepted: Mar. 8, 2024
Published Online: May. 6, 2024
The Author Email: Guangzong Xiao (xiaoguangzong@nudt.edu.cn)
CSTR:32188.14.PRJ.517547