Machine learning-assisted characterization of electroless deposited Ni-P particles on nano/micro SiC particles

In this experiment, Ni-P nanoparticles were deposited (ED) on SiC micro- and nanoparticles with different parameters. Our goal was to successfully prepare metal deposits and develop an effective method for comparing and evaluating the various procedures. During the experimental work, a three-step electroless Ni-P coating process was applied with different concentrations. The coated SiC particles were examined by scanning electron microscopy (SEM). The mass-specific surface area (SSA) of the coated SiC was measured by the Brunauer-Emmett-Teller (BET) method, while the volumetric-specific surface area (VSSA) was also calculated. The adhesion between the metal and the ceramic particle was analyzed by X-ray photoelectron spectroscopy (XPS). An image processing macroprogram was created (with a machine learning-based Trainable Weka Segmentation algorithm) to segment the SEM images of the ED metal particles to calculate the specific surface area (SV).