Algorithms of Image Processing and XRD Analyses for Characterization of Magnetic Nanostructures

Numerous physical and chemical properties including magnetism are influenced by morphological characteristics such as the shape, structure, and size of nanoparticles. The rapid progress in characterization techniques able to analyze the core-shell magnetic nanomaterials down to the same scale, such as high-resolution transmission electron microscopy (HR-TEM) and powdered X-ray diffraction (PXRD) analyses. Therefore, an innovative method capable of analyzing accurately the morphological properties of synthesized magnetic nanomaterials in a quantitative manner is in high demand. In a joint endeavor with the research group at Cal State LA, we introduce a pioneering algorithm tailored to interpret SEM images and effectively juxtapose these with PXRD elemental analyses. At the heart of our method lies a blend of conventional filtration techniques: from Fourier and inverse FT to node extraction and the novel reconnection algorithm empowered by 2D gaussian mapping. Our spotlight is on nanostructure classification by size, distinguishing between lengths for nanotubes/nanosheets and radii for nanospheres. But we don't stop there. Our exploration extends to understanding porosity, layer sizes, and their consequential magnetic attributes. We rounded it off with a dive into graphitization and amorphism. Our study encompasses a spectrum of synthesized core-shell nanoparticles such as metal (Fe, Ni, Co) and metal-free phthalocyanine and porphyrin, synthesized through intricate processes, all examined under the PPMS and EPR for their magnetic characteristics, to correlate the nanostructure layer design with its magnetic properties.