High-throughput grain mapping with sub-nanometer spatial resolution is demonstrated using scanning nanobeam electron diffraction (also known as 4D scanning transmission electron microscopy, or 4D-STEM) combined with high-speed direct-electron detection. An electron probe size down to 0.5 nm in diameter is used and the sample investigated is a gold–palladium nanoparticle catalyst. Computational analysis of the 4D-STEM data sets is performed using a disk registration algorithm to identify the diffraction peaks followed by feature learning to map the individual grains. Two unsupervised feature learning techniques are compared: principal component analysis (PCA) and non-negative matrix factorization (NNMF). The characteristics of the PCA versus NNMF output are compared and the potential of the 4D-STEM approach for statistical analysis of grain orientations at high spatial resolution is discussed.

Atomic force microscopy (AFM) is now well established among the tools of choice for the analysis and characterization of materials.Applications of AFM span many industries including chemicals, plastics, pharmaceuticals, and semiconductors.Advancements in AFM instrumentation over the last five years have expanded the range of application of this technology to investigate thermal and mechanical properties of complex materials at high spatial resolution as well as structural and morphological characterization of materials subjected to thermal and mechanical stresses.In particular, this has been an enabling technology for an improved understanding of structure–property relationships in polymeric materials including homopolymers, blends, impact-modified polymer systems, porous polymer systems, and semicrystalline polymers.Practical examples illustrate applications of contact, tapping-mode, phase-imaging, hot-stage, and scanning thermal methods for the characterization of modern industrial polymer materials.