JOURNAL ARTICLE

Polarization-encoded 3D structured light and multifocal spot arrays generation based on metasurface.

  • Published In: Modern Physics Letters B, 2024, v. 38, n. 23. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Zhao, Jiaqi; Ge, Suyang; Li, Yingbo; Liu, Zilei; Yang, Weihua; Li, Siqi 3 of 3

Abstract

Fluorescence microscopy possesses the advantages of high resolution, high sensitivity, molecular specificity and noninvasiveness, providing an important tool in life science research. The multifocal array and 3D structured light are two kinds of important light fields that are often used in scanning fluorescence microscopy systems and wide-field fluorescence microscopy systems. However, traditional methods for generating multifocal arrays and 3D structured light illumination rely on various bulk optical components, making it challenging to achieve compact optical systems. Besides, generating these two types of illumination typically requires two separate and independent optical systems, hindering the integration of different types of fluorescence microscopy systems. Here, a dielectric metasurface is proposed that can achieve the switching between multifocal arrays and 3D structured light through polarization state modulation, greatly simplifying the illumination optics of fluorescence microscopy systems and facilitating the integration of different types of fluorescence microscopy systems. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Modern Physics Letters B. 2024/08, Vol. 38, Issue 23, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:0217-9849
  • DOI:10.1142/S0217984924501860
  • Accession Number:177355896
  • Copyright Statement:Copyright of Modern Physics Letters B is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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