Reconstruction of Energy Surfaces from Friction Force Microscopy Measurements with the Jarzynski Equality

  • Ronen Berkovich
  • Joseph Klafter
  • Michael Urbakh
Chapter
Part of the NanoScience and Technology book series (NANO)

Abstract

Free energy is one of the most fundamental thermodynamic functions, determining relative phase stability and serving as a generating function for other thermodynamic quantities. The calculation of free energies is a challenging enterprise. In equilibrium statistical mechanics, the free energy is related to the canonical partition function. The partition function itself involves integrations over all degrees of freedom in the system and, in most cases, cannot be easily calculated directly. In 1997, Jarzynski proved a remarkable equality that allows computing the equilibrium free-energy difference between two states from the probability distribution of the nonequilibrium work done on the system to switch between the two states. The Jarzynski equality provides a powerful free-energy difference estimator from a set of irreversible experiments. This method is closely related to free-energy perturbation approach, which is also a computational technique for estimating free-energy differences. The ability to map potential profiles and topologies is of major significance to areas as diverse as biological recognition and nanoscale friction. This capability has been demonstrated for frictional studies where a force between the tip of the scanning force microscope and the surface is probed. The surface free-energy corrugation produces a detectable friction forces. Thus, friction force microscopy (FFM) should be able to discriminate between energetically different areas on the probed surface. Here, we apply the Jarzynski equality for the analysis of FFM measurements and thus obtain a variation of the free energy along a surface.

Keywords

Helmholtz Free Energy Periodic Potential Rupture Force Single Asperity Force Trace 
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 2012

Authors and Affiliations

  • Ronen Berkovich
    • 1
  • Joseph Klafter
    • 2
  • Michael Urbakh
    • 3
  1. 1.Department of Biological SciencesColumbia UniversityNew YorkUSA
  2. 2.Orenstein 209, School of ChemistryTel-Aviv UniversityTel AvivIsrael
  3. 3.Orenstein 207, School of ChemistryTel-Aviv UniversityTel AvivIsrael

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