ArticleNanoscale advances2022
Quantifying nanoscale forces using machine learning in dynamic atomic force microscopy.
Article in Nanoscale advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Advances in nanomechanical property mapping by atomic force microscopy.Nanoscale advances · 2025Review
- High-Throughput Nanorheology of Living Cells Powered by Supervised Machine Learning.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2025Article
- Analysis of biofilm assembly by large area automated AFM.NPJ biofilms and microbiomes · 2025Article
- Nonlinear Harmonics: A Gateway to Enhanced Image Contrast and Material Discrimination.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Characteristics and Functionality of Cantilevers and Scanners in Atomic Force Microscopy.Materials (Basel, Switzerland) · 2023Review
- Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics.Nanoscale advances · 2023Review
- Sensing red blood cell nano-mechanics: Toward a novel blood biomarker for Alzheimer's disease.Frontiers in aging neuroscience · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dynamic atomic force microscopy (AFM) is a key platform that enables topological and nanomechanical characterization of novel materials. This is achieved by linking the nanoscale forces that exist between the AFM tip and the sample to specific mathematical functions through modeling. However, the main challenge in dynamic AFM is to quantify these nanoscale forces without the use of complex models that are routinely used to explain the physics of tip-sample interaction. Here, we make use of machine learning and data science to characterize tip-sample forces purely from experimental data with sub-microsecond resolution. Our machine learning approach is first trained on standard AFM models and then showcased experimentally on a polymer blend of polystyrene (PS) and low density polyethylene (LDPE) sample. Using this algorithm we probe the complex physics of tip-sample contact in polymers, estimate elasticity, and provide insight into energy dissipation during contact. Our study opens a new route in dynamic AFM characterization where machine learning can be combined with experimental methodologies to probe transient processes involved in phase transformation as well as complex chemical and biological phenomena in real-time.
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Registered trials
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