Plasmon-enhanced single molecular optical trapping detected by super-resolution imaging

2021 ◽  
Author(s):  
Yasutaka Kitahama
Keyword(s):  
Optical Trapping ◽  
Super Resolution ◽  
Resolution Imaging

Optical Trapping, Sensing, and Imaging by Photonic Nanojets

Photonics ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 434
Author(s):  
Heng Li ◽  
Wanying Song ◽  
Yanan Zhao ◽  
Qin Cao ◽  
Ahao Wen
Keyword(s):  
Optical Tweezers ◽  
Optical Trapping ◽  
Near Field ◽  
Super Resolution ◽  
Diffraction Limit ◽  
Research Progress ◽  
Optical Forces ◽  
Resolution Imaging ◽  
Photonic Nanojets ◽  
Near Field Scanning

The optical trapping, sensing, and imaging of nanostructures and biological samples are research hotspots in the fields of biomedicine and nanophotonics. However, because of the diffraction limit of light, traditional optical tweezers and microscopy are difficult to use to trap and observe objects smaller than 200 nm. Near-field scanning probes, metamaterial superlenses, and photonic crystals have been designed to overcome the diffraction limit, and thus are used for nanoscale optical trapping, sensing, and imaging. Additionally, photonic nanojets that are simply generated by dielectric microspheres can break the diffraction limit and enhance optical forces, detection signals, and imaging resolution. In this review, we summarize the current types of microsphere lenses, as well as their principles and applications in nano-optical trapping, signal enhancement, and super-resolution imaging, with particular attention paid to research progress in photonic nanojets for the trapping, sensing, and imaging of biological cells and tissues.

scholarly journals Selecting a Proper Microsphere to Combine Optical Trapping with Microsphere-Assisted Microscopy

2020 ◽  
Vol 10 (9) ◽  
pp. 3127 ◽  
Author(s):  
Xi Liu ◽  
Song Hu ◽  
Yan Tang ◽  
Zhongye Xie ◽  
Junbo Liu ◽  
...  
Keyword(s):  
Image Quality ◽  
Optical Trapping ◽  
Super Resolution ◽  
Light Illumination ◽  
Label Free ◽  
Research Fields ◽  
Immersion Medium ◽  
Silicon Nanostructure ◽  
Resolution Imaging ◽  
The Difference

Microsphere-assisted microscopy serves as an effective super-resolution technique in biological observations and nanostructure detections, and optical trapping is widely used for the manipulation of small particles like microspheres. In this study, we focus on the selection of microsphere types for the combination of the optical trapping and the super-resolution microsphere-assisted microscopy, by considering the optical trapping performances and the super-resolution imaging ability of index-different microspheres in water simultaneously. Finally, the polystyrene (PS) sphere and the melamine formaldehyde (MF) sphere have been selected from four typical index-different microspheres normally used in microsphere-assisted microscopy. In experiments, the optically trapped PS/MF microsphere in water has been used to achieve super-resolution imaging of a 139 nm line-width silicon nanostructure grating under white light illumination. The image quality and the magnification factor are affected by the refractive index contrast between the microspheres and the immersion medium, and the difference of image quality is partly explained by the photonic nanojet. This work guides us in selecting proper microspheres, and also provides a label-free super-resolution imaging technique in many research fields.

scholarly journals Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan
Keyword(s):  
Neural Network ◽  
Continuous Wave ◽  
Super Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Range Estimation ◽  
Resolution Image ◽  
Resolution Imaging

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

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