LAPSE:2024.0696
Published Article

LAPSE:2024.0696
A Linear Fit for Atomic Force Microscopy Nanoindentation Experiments on Soft Samples
June 6, 2024
Abstract
Atomic Force Microscopy (AFM) nanoindentation is a powerful technique for determining the mechanical properties of soft samples at the nanoscale. The Hertz model is typically used for data processing when employing spherical indenters for small indentation depths (h) compared to the radius of the tip (R). When dealing with larger indentation depths, Sneddon’s equations can be used instead. In such cases, the fitting procedure becomes more intricate. Nevertheless, as the h/R ratio increases, the force−indentation curves tend to become linear. In this paper the potential of using the linear segment of the curve (for h > R) to determine Young’s modulus is explored. Force−indentation data from mouse and human lung tissues were utilized, and Young’s modulus was calculated using both conventional and linear approximation methods. The linear approximation proved to be accurate in all cases. Gaussian functions were applied to the results obtained from both classic Sneddon’s equations and the simplified approach, resulting in identical distribution means. Moreover, the simplified approach was notably unaffected by contact point determination. The linear segment of the force−indentation curve in deep spherical indentations can accurately determine the Young’s modulus of soft materials at the nanoscale.
Atomic Force Microscopy (AFM) nanoindentation is a powerful technique for determining the mechanical properties of soft samples at the nanoscale. The Hertz model is typically used for data processing when employing spherical indenters for small indentation depths (h) compared to the radius of the tip (R). When dealing with larger indentation depths, Sneddon’s equations can be used instead. In such cases, the fitting procedure becomes more intricate. Nevertheless, as the h/R ratio increases, the force−indentation curves tend to become linear. In this paper the potential of using the linear segment of the curve (for h > R) to determine Young’s modulus is explored. Force−indentation data from mouse and human lung tissues were utilized, and Young’s modulus was calculated using both conventional and linear approximation methods. The linear approximation proved to be accurate in all cases. Gaussian functions were applied to the results obtained from both classic Sneddon’s equations and the simplified approach, resulting in identical distribution means. Moreover, the simplified approach was notably unaffected by contact point determination. The linear segment of the force−indentation curve in deep spherical indentations can accurately determine the Young’s modulus of soft materials at the nanoscale.
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Keywords
disease diagnosis, mathematical modeling, mechanical properties, nanotechnology, scanning probe microscopy, soft materials, Young’s modulus
Subject
Suggested Citation
Kontomaris SV, Malamou A, Zachariades A, Stylianou A. A Linear Fit for Atomic Force Microscopy Nanoindentation Experiments on Soft Samples. (2024). LAPSE:2024.0696
Author Affiliations
Kontomaris SV: Faculty of Engineering and Architecture, Metropolitan College, 15125 Athens, Greece; BioNanoTec Ltd., Nicosia 2043, Cyprus [ORCID]
Malamou A: Independent Power Transmission Operator S.A. (IPTO), 10443 Athens, Greece [ORCID]
Zachariades A: Nicosia Lung Center, Strovolos 2012, Cyprus
Stylianou A: School of Sciences, European University Cyprus, Nicosia 2404, Cyprus; E.U.C. Research Centre, Nicosia 2404, Cyprus [ORCID]
Malamou A: Independent Power Transmission Operator S.A. (IPTO), 10443 Athens, Greece [ORCID]
Zachariades A: Nicosia Lung Center, Strovolos 2012, Cyprus
Stylianou A: School of Sciences, European University Cyprus, Nicosia 2404, Cyprus; E.U.C. Research Centre, Nicosia 2404, Cyprus [ORCID]
Journal Name
Processes
Volume
12
Issue
4
First Page
843
Year
2024
Publication Date
2024-04-22
ISSN
2227-9717
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Original Submission
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PII: pr12040843, Publication Type: Journal Article
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LAPSE:2024.0696
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https://doi.org/10.3390/pr12040843
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Jun 6, 2024
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