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Investigation of Nanopatterned Functional Polymer Surfaces by AFM in Pulsed Force Mode

  • Olivier Soppera
  • Ali Dirani
  • Safi Jradi
  • Vincent Roucoules
  • Hamidou Haidara
Chapter
Part of the NanoScience and Technology book series (NANO)

Abstract

Achieving topography and chemistry control at the nanoscale of polymer surfaces constitutes a highly challenging objective in nanotechnology. Advances in this field suppose the development of characterization methodology with sub-100-nm resolution. Many imaging techniques based on scanning probe microscopy (SPM) were recently developed to achieve this goal [1]. Among them, pulsed force mode (PFM) atomic force microscopy (AFM), which has been proposed firstly by Marti [2], is still a method of interest since this nonresonant mode designed to allow approach curves being recorded along the scanning path provides the topography of the sample and a direct and simple local characterization of adhesion and stiffness.This chapter is aimed at demonstrating the interest of this technique to investigate polymer surfaces patterned with photochemical methods. Both topography and chemical contrast at the sub-100-nm scale can be probed, which gives new insights into photoinduced processes at the nanoscale.After an introduction focusing on the main techniques used for the analysis of the chemical contrast at micro- and nanopatterned polymer surfaces, the first part will deal with the utility of AFM in the investigation of photopolymer surfaces.In the second part, the principle of PFM and its interest in polymer surface analysis will be detailed.The third part will focus on a recent application dealing with the nanopatterning of plasma polymer surfaces using DUV photolithography techniques. Analysis of interactions between the AFM tip and the polymer surface allows acquiring relevant information on the light-induced modifications at the nanoscale.

Keywords

Atomic Force Microscopy Polymer Surface Plasma Polymer Holographic Data Storage Dropwise Condensation 
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

  • Olivier Soppera
    • 1
  • Ali Dirani
    • 1
  • Safi Jradi
    • 1
  • Vincent Roucoules
    • 1
  • Hamidou Haidara
    • 1
  1. 1.Institut de Sciences des Materiaux de Mulhouse – IS2M, CNRS LRC 7228MulhouseFrance

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