Multimodal analytical approaches to nanomaterials: TEM, diffraction, image processing, and fractal analysis

Abstract

In the field of materials science, using diverse experimental and computational methods is a well-known approach as the most effective route to comprehensive material characterization. Combining high-resolution transmission electron microscopy (TEM) techniques and X-ray diffraction (XRD) data analysis enables simultaneous integration of multifaceted data, improving research productivity, accuracy and application development. The pathway from qualitative to quantitative results requires advanced data analysis approaches with a capability of extracting meaningful physical parameters from all complex datasets. While there is no universal method presently existing to derive physical properties directly from the chemical composition and TEM images alone, traditional theoretical approaches and modern machine learning (ML) methods, particularly zero-code artificial intelligence AI/ML platforms, seem to be promising in this area. The actual review deals with current cases of TEM data analysis, particularly where combining TEM and diffraction methods enables enhanced nanoparticle characterization, emphasizing parameters like particle size, coherent domain size, agglomeration, and complicated fractal-like shape, all of which could be rather crucial for basic materials science and further industrial applications.