IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v11y2020i1d10.1038_s41467-020-15745-4.html
   My bibliography  Save this article

Single-shot ultrafast imaging attaining 70 trillion frames per second

Author

Listed:
  • Peng Wang

    (California Institute of Technology)

  • Jinyang Liang

    (California Institute of Technology
    Institut National de la Recherche Scientifique)

  • Lihong V. Wang

    (California Institute of Technology)

Abstract

Real-time imaging of countless femtosecond dynamics requires extreme speeds orders of magnitude beyond the limits of electronic sensors. Existing femtosecond imaging modalities either require event repetition or provide single-shot acquisition with no more than 1013 frames per second (fps) and 3 × 102 frames. Here, we report compressed ultrafast spectral photography (CUSP), which attains several new records in single-shot multi-dimensional imaging speeds. In active mode, CUSP achieves both 7 × 1013 fps and 103 frames simultaneously by synergizing spectral encoding, pulse splitting, temporal shearing, and compressed sensing—enabling unprecedented quantitative imaging of rapid nonlinear light-matter interaction. In passive mode, CUSP provides four-dimensional (4D) spectral imaging at 0.5 × 1012 fps, allowing the first single-shot spectrally resolved fluorescence lifetime imaging microscopy (SR-FLIM). As a real-time multi-dimensional imaging technology with the highest speeds and most frames, CUSP is envisioned to play instrumental roles in numerous pivotal scientific studies without the need for event repetition.

Suggested Citation

  • Peng Wang & Jinyang Liang & Lihong V. Wang, 2020. "Single-shot ultrafast imaging attaining 70 trillion frames per second," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15745-4
    DOI: 10.1038/s41467-020-15745-4
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-020-15745-4
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-020-15745-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhoutian Liu & Lele Wang & Yuan Meng & Tiantian He & Sifeng He & Yousi Yang & Liuyue Wang & Jiading Tian & Dan Li & Ping Yan & Mali Gong & Qiang Liu & Qirong Xiao, 2022. "All-fiber high-speed image detection enabled by deep learning," Nature Communications, Nature, vol. 13(1), pages 1-8, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15745-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.