IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v8y2017i1d10.1038_ncomms15133.html
   My bibliography  Save this article

Band structure engineered layered metals for low-loss plasmonics

Author

Listed:
  • Morten N. Gjerding

    (Center for Atomic-scale Materials Design (CAMD), Technical University of Denmark
    Center for Nanostructured Graphene (CNG), Technical University of Denmark)

  • Mohnish Pandey

    (Center for Atomic-scale Materials Design (CAMD), Technical University of Denmark)

  • Kristian S. Thygesen

    (Center for Atomic-scale Materials Design (CAMD), Technical University of Denmark
    Center for Nanostructured Graphene (CNG), Technical University of Denmark)

Abstract

Plasmonics currently faces the problem of seemingly inevitable optical losses occurring in the metallic components that challenges the implementation of essentially any application. In this work, we show that Ohmic losses are reduced in certain layered metals, such as the transition metal dichalcogenide TaS2, due to an extraordinarily small density of states for scattering in the near-IR originating from their special electronic band structure. On the basis of this observation, we propose a new class of band structure engineered van der Waals layered metals composed of hexagonal transition metal chalcogenide-halide layers with greatly suppressed intrinsic losses. Using first-principles calculations, we show that the suppression of optical losses lead to improved performance for thin-film waveguiding and transformation optics.

Suggested Citation

  • Morten N. Gjerding & Mohnish Pandey & Kristian S. Thygesen, 2017. "Band structure engineered layered metals for low-loss plasmonics," Nature Communications, Nature, vol. 8(1), pages 1-8, April.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15133
    DOI: 10.1038/ncomms15133
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/ncomms15133
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/ncomms15133?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. Shufeng Kong & Francesco Ricci & Dan Guevarra & Jeffrey B. Neaton & Carla P. Gomes & John M. Gregoire, 2022. "Density of states prediction for materials discovery via contrastive learning from probabilistic embeddings," Nature Communications, Nature, vol. 13(1), pages 1-12, 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:8:y:2017:i:1:d:10.1038_ncomms15133. 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.