Deep learning to classify and establish structure property predictions with PeakForce QNM atomic force microscopy

22 Jun 2023

Machine learning, is a powerful tool to establish the presence (or absence) of correlations between microstructure and bulk properties with its ability to flesh out relationships and trends that are difficult to establish otherwise. In this application note from Bruker, explore the use of deep learning tools, such as convolutional neural nets (CNNs), to explore atomic force microscopy (AFM) phase and PeakForce QNM® images of impact copolymers, a polymer blend of polypropylene with micro-sized domains of rubber.

Links

Tags

Atomic Force Microscopy / Scanning Tunneling MicroscopyAtomic force microscopes (AFM) and scanning tunneling microscopes (STM) are high-resolution forms of scanning probe microscope (SPM) used to generate topological information of a sample down to the atomic scale. Instruments can generate an image of the surface topology, manipulate objects and reveal information on localized properties such as Young’s modulus, conductivity, and magnetism. High-quality STM and AFM probes optimized for your application are available, as well as other SPM-based instruments such as scanning ion conductance microscopes (SICM) & near-field scanning optical microscopes (NSOM). Find the best AFM and STM equipment in our peer-reviewed product directory: compare products, check customer reviews and receive pricing direct from manufacturers.AFM
Deep learning to classify and establish structure property predictions with PeakForce QNM atomic force microscopy