Atomic force microscopy

Atomic force microscopy (AFM) is a powerful technique that enables the imaging of almost any type of surface, including polymers, ceramics, composites, glass, and biological samples. AFM is used to measure and localize many different forces, including adhesion strength, magnetic forces, and mechanical properties. AFM consists of a sharp tip that is approximately 10 to 20 nm in diameter, which is attached to a cantilever. The response of the cantilever when interacting with a surface encodes different properties of the surface and this response is inherently non-linear. Multifrequency lock-in amplifier MLA-3 and the Multifrequency AFM kit can measure up to 32 harmonics of the non-linear response of the cantilever, enabling you to extract properties of the surface faster and with better sensitivity.

Characterising visco-elastic response

Simulated force quadrature curves using the moving surface model show excellent agreement with experimental data. Simulation reveals the surface motion, which can not be measured with the AFM. The time evolution of the envelope of rapid cantilever oscillation (blue) and surface oscillation (orange), shows that the adhesion force lifts the soft surface. The surface does not have time to fully relax between successive taps of the tip, giving rise to a time-average lifted surface and hysteresis in the force quadrature curves.

Challenge: A viscoelastic surface has a characteristic delay time between applied load and material response. When this delay time becomes longer than the period of cantilever oscillation, the force quadrature curves become hysteretic.

Solution: Using the Multifrequency AFM kit and a moving surface model one can extract the characteristic time constants for tip penetration in to the surface, and relaxation of the free surface.

User: Per-Anders Thorén, PhD student at KTH, Sweden

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Machine learning the mechanical response

Atomic force microscopy

A map showing regions of similar mechanical response on a dynamically vulcanized thermal plastic alloy (DVA). Pixels were grouped in to 5 clusters using the k-means algorithm. The x-y location of the pixel is not a feature of the data, so the method blindly reconstructs the map. Force quadrature curves are shown for the pixel closest to the centroid of each cluster, thus giving the characteristic mechanical response of the region with corresponding color on the map.

Challenge: Intermodulation AFM can measure 60 amplitude and phase images, in one scan at normal speed (2 min. for 256 x 256 pixels) gathering information about the surface. Each pixel represents a single point in an abstract 60-dimensional feature space.

Solution: Machine learning attempts to group or cluster the pixels in this space. The ImAFM Software Suite comes with functionality for k-means clustering. Other clustering algorithms are easily programmed using the scripting interface and the Python module scikit-learn.

User: Hailu G. Kassa, University of Mons - UMONS, Belgium

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