Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Chinese Optics Letters
  • Vol. 9,
  • Issue 3,
  • pp. 032901-
  • (2011)

Enhancement of light trapping in thin-film solar cells through Ag

Not Accessible

Your library or personal account may give you access

Abstract

Forward-scattering efficiency (FSE) is first proposed when an Ag nanoparticle serves as the light-trapping structure for thin-film (TF) solar cells because the Ag nanoparticle's light-trapping efficiency lies on the light-scattering direction of metal nanoparticles. Based on FSE analysis of Ag nanoparticles with radii of 53 and 88 nm, the forward-scattering spectra and light-trapping efficiencies are calculated. The contributions of dipole and quadrupole modes to light-trapping effect are also analyzed quantitatively. When the surface coverage of Ag nanoparticles is 5%, light-trapping efficiencies are 15.5% and 32.3%, respectively, for 53- and 88-nm Ag nanoparticles. Results indicate that the plasmon quadrupole mode resonance of Ag nanoparticles could further enhance the light-trapping effect for TF solar cells.

© 2011 Chinese Optics Letters

PDF Article
More Like This
Enhanced efficiency of light-trapping nanoantenna arrays for thin-film solar cells

Constantin Simovski, Dmitry Morits, Pavel Voroshilov, Michael Guzhva, Pavel Belov, and Yuri Kivshar
Opt. Express 21(S4) A714-A725 (2013)

Spin-coated Ag nanoparticles for enhancing light absorption of thin film a-Si:H solar cells

Chan Il Yeo, Jang Hun Choi, Joon Beom Kim, Jeong Chul Lee, and Yong Tak Lee
Opt. Mater. Express 4(2) 346-351 (2014)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.