Automated AFM force curve analysis for determining elastic modulus of biomaterials and biological samples

Yow Ren Chang, Vijay Krishna Raghunathan, Shaun P. Garland, Joshua T. Morgan, Paul Russell, Christopher J Murphy

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


The analysis of atomic force microscopy (AFM) force data requires the selection of a contact point (CP) and is often time consuming and subjective due to influence from intermolecular forces and low signal-to-noise ratios (SNR). In this report, we present an automated algorithm for the selection of CPs in AFM force data and the evaluation of elastic moduli. We propose that CP may be algorithmically easier to detect by identifying a linear elastic indentation region of data (high SNR) rather than the contact point itself (low SNR). Utilizing Hertzian mechanics, the data are fitted for the CP. We first detail the algorithm and then evaluate it on sample polymeric and biological materials. As a demonstration of automation, 64 × 64 force maps were analyzed to yield spatially varying topographical and mechanical information of cells. Finally, we compared manually selected CPs to automatically identified CPs and demonstrated that our automated approach is both accurate (< 10. nm difference between manual and automatic) and precise for non-interacting polymeric materials. Our data show that the algorithm is useful for analysis of both biomaterials and biological samples.

Original languageEnglish (US)
Pages (from-to)209-218
Number of pages10
JournalJournal of the Mechanical Behavior of Biomedical Materials
StatePublished - 2014


  • Atomic force microscopy
  • Biomechanics
  • Contact point
  • Elastic modulus
  • Nano-indentation

ASJC Scopus subject areas

  • Biomaterials
  • Biomedical Engineering
  • Mechanics of Materials
  • Medicine(all)


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