ArticleJournal of the mechanical behavior of biomedical materials2014
Automated AFM force curve analysis for determining elastic modulus of biomaterials and biological samples.
Article in Journal of the mechanical behavior of biomedical materials, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Bridging nanomechanics and bioenergetics of single mitochondria by atomic force microscopy.Life science alliance · 2026Article
- Material-Induced Nuclear Deformation Controls Chromatin Architecture in Adipose Stem Cells.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Automated atomic force microscopy analysis using convolutional and recurrent neural networks.Biophysical journal · 2025Article
- Biomechanical properties of the trabecular meshwork in intraocular pressure regulation: a review.International ophthalmology · 2025Review
- Mechanical property estimation of sarcoma-relevant extracellular vesicles using transmission electron microscopy.Journal of extracellular biology · 2024Article
- Morphology and Mechanics of Star Copolymer Ultrathin Films Probed by Atomic Force Microscopy in the Air and in Liquid.Materials (Basel, Switzerland) · 2024Article
- Preparation and Microscopic Mechanical Characterization of L-Methionine-Based Polyphosphazene Fibrous Mats for Vascular Tissue Engineering.Pharmaceutics · 2023Article
- Characterization of extracellular matrix deposited by segmental trabecular meshwork cells.Experimental eye research · 2023Article
- Type II Diabetes Mellitus Causes Extracellular Matrix Alterations in the Posterior Cornea That Increase Graft Thickness and Rigidity.Investigative ophthalmology & visual science · 2023Article
- Mice Deficient in TAZ (Wwtr1) Demonstrate Clinical Features of Late-Onset Fuchs' Endothelial Corneal Dystrophy.Investigative ophthalmology & visual science · 2023Article
- Characterization of extracellular matrix deposited by segmental trabecular meshwork cells.bioRxiv : the preprint server for biology · 2023Article
- Determination of the radii of coated and uncoated silicon AFM sharp tips using a height calibration standard grating and a nonlinear regression function.Beilstein journal of nanotechnology · 2023Article
- Locating critical events in AFM force measurements by means of one-dimensional convolutional neural networks.Scientific reports · 2022Article
- Micromanipulation and Automatic Data Analysis to Determine the Mechanical Strength of Microparticles.Micromachines · 2022Article
- An ex vivo model of human corneal rim perfusion organ culture.Experimental eye research · 2022Article
- Extracellular Matrix Stiffness and TGFβ2 Regulate YAP/TAZ Activity in Human Trabecular Meshwork Cells.Frontiers in cell and developmental biology · 2022Article
- Review
- Click-functionalized hydrogel design for mechanobiology investigations.Molecular systems design & engineering · 2021Article
- Inflammatory signaling sensitizes Piezo1 mechanotransduction in articular chondrocytes as a pathogenic feed-forward mechanism in osteoarthritis.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Determination by Relaxation Tests of the Mechanical Properties of Soft Polyacrylamide Gels Made for Mechanobiology Studies.Polymers · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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 (< 10nm 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.