IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v436y2005i7053d10.1038_nature03977.html
   My bibliography  Save this article

Experimental measurement of the photonic properties of icosahedral quasicrystals

Author

Listed:
  • Weining Man

    (Department of Physics
    Princeton University)

  • Mischa Megens

    (Philips Research Laboratories)

  • Paul J. Steinhardt

    (Department of Physics)

  • P. M. Chaikin

    (Department of Physics
    Princeton University
    New York University)

Abstract

Quasicrystals for photonics Quasicrystalline structures may have optical bandgap properties — frequency ranges in which the propagation of light is forbidden — that will make them well suited for applications in which photonic crystals are normally used. Previous work has focused on one- and two-dimensional quasicrystals for which exact theoretical calculations can be made. But when it comes to three dimensions, computation of the optical properties remains a tough challenge. Man et al. tackled the three-dimensional case experimentally using a large photonic quasicrystal made of plastic. They find that the periodic structure yields surprisingly simple spectra, and the resulting structural insights confirm that quasicrystals are excellent candidates for photonic bandgap materials.

Suggested Citation

  • Weining Man & Mischa Megens & Paul J. Steinhardt & P. M. Chaikin, 2005. "Experimental measurement of the photonic properties of icosahedral quasicrystals," Nature, Nature, vol. 436(7053), pages 993-996, August.
    • Handle: RePEc:nat:nature:v:436:y:2005:i:7053:d:10.1038_nature03977
    DOI: 10.1038/nature03977
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/nature03977
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/nature03977?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Lang Feng & Stefan Natu & Victoria Som de Cerff Edmonds & John J. Valenza, 2022. "Multiphase flow detection with photonic crystals and deep learning," Nature Communications, Nature, vol. 13(1), pages 1-10, 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:nature:v:436:y:2005:i:7053:d:10.1038_nature03977. 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.