Simultaneous topography imaging and broadband nanomechanical mapping on atomic force microscope

Tianwei Li, Qingze Zou

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

In this paper, an approach is proposed to achieve simultaneous imaging and broadband nanomechanical mapping of soft materials in air by using an atomic force microscope. Simultaneous imaging and nanomechanical mapping are needed, for example, to correlate the morphological and mechanical evolutions of the sample during dynamic phenomena such as the cell endocytosis process. Current techniques for nanomechanical mapping, however, are only capable of capturing static elasticity of the material, or the material viscoelasticity in a narrow frequency band around the resonant frequency(ies) of the cantilever used, not competent for broadband nanomechanical mapping, nor acquiring topography image of the sample simultaneously. These limitations are addressed in this work by enabling the augmentation of an excitation force stimuli of rich frequency spectrum for nanomechanical mapping in the imaging process. Kalman-filtering technique is exploited to decouple and split the mixed signals for imaging and mapping, respectively. Then the sample indentation generated is quantified online via a system-inversion method, and the effects of the indentation generated and the topography tracking error on the topography quantification are taken into account. Moreover, a data-driven feedforward-feedback control is utilized to track the sample topography. The proposed approach is illustrated through experimental implementation on a polydimethylsiloxane sample with a pre-fabricated pattern.

Original languageEnglish (US)
Article number505502
JournalNanotechnology
Volume28
Issue number50
DOIs
StatePublished - Nov 23 2017

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • General Chemistry
  • General Materials Science
  • Mechanics of Materials
  • Mechanical Engineering
  • Electrical and Electronic Engineering

Keywords

  • Kalman filter
  • atomic force microscope imaging
  • iterative learning control
  • nanomechanical mapping

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