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Sun X, Wang X, Wang F, Cao Y, Ding X, Dou Y, Gu J, Sun X, Liu H, Lu X, Yu H, Huang C. Reconstruction Filters Improving the Spatial Resolution and Signal-to-Noise Ratio of Surface Plasmon Resonance Microscopy. Anal Chem 2024; 96:636-641. [PMID: 38175158 DOI: 10.1021/acs.analchem.3c05047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Benefitting from high sensitivity, real-time, and label-free imaging, surface plasmon resonance microscopy (SPRM) has become a powerful tool for dynamic detection of nanoparticles. However, the evanescent propagation of surface plasmon polaritons (SPPs) induces interference between scattered and launched SPPs, which deteriorates the spatial resolution and signal-to-noise ratio (SNR). Due to the simplicity and fast processing, image reconstruction based on deconvolution has shown the feasibility of improving the spatial resolution of SPRM imaging. Retrieving the particle scattering from SPRM interference imaging by filters is crucial for reconstruction. In this work, we illustrate the effect of filters extracting SPP scattering of nanoparticles with different sizes and shapes for reconstruction. The results indicate that the optimum filters are determined by the material of nanoparticles instead of particle sizes. The reconstruction of single Au and PS nanospheres as well as Ag nanowires with optimum filters is achieved. The reconstructed spatial resolution is improved to 254 nm, and the SNR is increased by 8.1 times. Our research improves the quality of SPRM imaging and provides a reliable method for fast detection of particles with diverse sizes and shapes.
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Affiliation(s)
- Xiaojuan Sun
- E-health Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xue Wang
- E-health Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fei Wang
- E-health Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yitao Cao
- E-health Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoxi Ding
- E-health Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yingjie Dou
- E-health Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaqi Gu
- E-health Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xuqing Sun
- E-health Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongyao Liu
- E-health Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
| | - Xinchao Lu
- E-health Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
| | - Hui Yu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Chengjun Huang
- E-health Center, Institute of Microelectronics of Chinese Academy of Sciences, Beijing 100029, China
- School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
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