Nanophotonics and Single Molecules

  • W. E. Moerner
  • P. James Schuck
  • David P. Fromm
  • Anika Kinkhabwala
  • Samuel J. Lord
  • Stefanie Y. Nishimura
  • Katherine A. Willets
  • Arvind Sundaramurthy
  • Gordon Kino
  • Meng He
  • Zhikuan Lu
  • Robert J. Twieg
Part of the Springer Series in Biophysics book series (BIOPHYSICS, volume 12)

Single emitting molecules are currently providing a new window into nanoscale systems ranging from biology to materials science. The amount of information that can be extracted from each single molecule depends upon the specific photophysical properties of the fluorophore and how these properties are affected by the nearby environment. For this reason, it is necessary to develop single-molecule emitters with as many different reporter functions as possible. The first part of this chapter describes a relatively new class of single-molecule fluorophores which offer tunable photophysical properties and, in turn, improved local reporting functionality on the nanometer length scale. The second part of this chapter presents metallic nanostructures which can address a second issue: the mismatch between the typical size of a single fluorophore (~1 nm along a long dimension) and the wavelength of light (~500 nm). Such nanostructures could lead to more efficient excitation of single molecules, in particular, higher excitation probability as well as reduced backgrounds, and effectively higher spatial resolution. Metallic nanostructures based on two triangles formed into a bowtie shape feature large enhancements of the local electromagnetic field and give rise to strong surface-enhanced Raman scattering of molecules. In future work, enhanced electromagnetic structures can be combined with single-molecule reporters in a variety of applications.


Surface Enhance Raman Scattering Single Molecule Electron Beam Lithography Resonant Wavelength FDTD Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • W. E. Moerner
    • 1
  • P. James Schuck
    • 1
  • David P. Fromm
    • 1
  • Anika Kinkhabwala
    • 1
  • Samuel J. Lord
    • 1
  • Stefanie Y. Nishimura
    • 1
  • Katherine A. Willets
    • 1
  • Arvind Sundaramurthy
    • 2
  • Gordon Kino
    • 2
  • Meng He
    • 3
  • Zhikuan Lu
    • 3
  • Robert J. Twieg
    • 3
  1. 1.Department of ChemistryStanford UniversityStanfordUSA
  2. 2.Department of Electrical EngineeringStanford UniversityStanfordUSA
  3. 3.Department of ChemistryKent State UniversityKentUSA

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