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Exploiting dynamic nonlinearity in upconversion nanoparticles for super-resolution imaging
Authors:
Chaohao Chen,
Lei Ding,
Baolei Liu,
Ziqin Du,
Yongtao Liu,
Xiangjun Di,
Xuchen Shan,
Chenxiao Lin,
Min Zhang,
Xiaoxue Xu,
Xiaolan Zhong,
Jianfeng Wang,
Lingqian Chang,
Ben J. Halkon,
Xin Chen,
Faliang Cheng,
Fan Wang
Abstract:
Single-beam super-resolution microscopy, also known as superlinear microscopy, exploits the nonlinear response of fluorescent probes in confocal microscopy. The technique requires no complex purpose-built system, light field modulation, or beam shaping. Here, we present a strategy to enhance spatial resolution of superlinear microscopy by modulating excitation intensity during image acquisition. T…
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Single-beam super-resolution microscopy, also known as superlinear microscopy, exploits the nonlinear response of fluorescent probes in confocal microscopy. The technique requires no complex purpose-built system, light field modulation, or beam shaping. Here, we present a strategy to enhance spatial resolution of superlinear microscopy by modulating excitation intensity during image acquisition. This modulation induces dynamic optical nonlinearity in upconversion nanoparticles (UCNPs), resulting in variations of higher spatial-frequency information in the obtained images. The high-order information can be extracted with a proposed weighted finite difference imaging algorithm from raw fluorescence images, to generate an image with a higher resolution than superlinear microscopy images. We apply this approach to resolve two adjacent nanoparticles within a diffraction-limited area, improving the resolution to 130 nm. This work suggests a new scope for developing dynamic nonlinear fluorescent probes in super-resolution nanoscopy.
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Submitted 2 June, 2022;
originally announced June 2022.
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Upconversion nonlinear structured illumination microscopy
Authors:
Baolei Liu,
Chaohao Chen,
Xiangjun Di,
Jiayan Liao,
Shihui Wen,
Qian Peter Su,
Xuchen Shan,
Zai-Quan Xu,
Lining Arnold Ju,
Fan Wang,
Dayong Jin
Abstract:
Video-rate super-resolution imaging through biological tissue can visualize and track biomolecule interplays and transportations inside cellular organisms. Structured illumination microscopy allows for wide-field super resolution observation of biological samples but is limited by the strong absorption and scattering of light by biological tissues, which degrades its imaging resolution. Here we re…
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Video-rate super-resolution imaging through biological tissue can visualize and track biomolecule interplays and transportations inside cellular organisms. Structured illumination microscopy allows for wide-field super resolution observation of biological samples but is limited by the strong absorption and scattering of light by biological tissues, which degrades its imaging resolution. Here we report a photon upconversion scheme using lanthanide-doped nanoparticles for wide-field super-resolution imaging through the biological transparent window, featured by near-infrared and low-irradiance nonlinear structured illumination. We demonstrate that the 976 nm excitation and 800 nm up-converted emission can mitigate the aberration. We found that the nonlinear response of upconversion emissions from single nanoparticles can effectively generate the required high spatial frequency components in Fourier domain. These strategies lead to a new modality in microscopy with a resolution of 130 nm, 1/7th of the excitation wavelength, and a frame rate of 1 fps.
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Submitted 13 February, 2020;
originally announced February 2020.
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A Game-Theoretic Framework for Autonomous Vehicles Velocity Control: Bridging Microscopic Differential Games and Macroscopic Mean Field Games
Authors:
Kuang Huang,
Xuan Di,
Qiang Du,
Xi Chen
Abstract:
This paper proposes an efficient computational framework for longitudinal velocity control of a large number of autonomous vehicles (AVs) and develops a traffic flow theory for AVs. Instead of hypothesizing explicitly how AVs drive, our goal is to design future AVs as rational, utility-optimizing agents that continuously select optimal velocity over a period of planning horizon. With a large numbe…
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This paper proposes an efficient computational framework for longitudinal velocity control of a large number of autonomous vehicles (AVs) and develops a traffic flow theory for AVs. Instead of hypothesizing explicitly how AVs drive, our goal is to design future AVs as rational, utility-optimizing agents that continuously select optimal velocity over a period of planning horizon. With a large number of interacting AVs, this design problem can become computationally intractable. This paper aims to tackle such a challenge by employing mean field approximation and deriving a mean field game (MFG) as the limiting differential game with an infinite number of agents. The proposed micro-macro model allows one to define individuals on a microscopic level as utility-optimizing agents while translating rich microscopic behaviors to macroscopic models. Different from existing studies on the application of MFG to traffic flow models, the present study offers a systematic framework to apply MFG to autonomous vehicle velocity control. The MFG-based AV controller is shown to mitigate traffic jam faster than the LWR-based controller. MFG also embodies classical traffic flow models with behavioral interpretation, thereby providing a new traffic flow theory for AVs.
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Submitted 10 December, 2020; v1 submitted 14 March, 2019;
originally announced March 2019.