My thesis, “Synthesis and characterization of Dysprosium based nanoparticles via chemical precipitation method for potential biomedical application,” explored Dy-based nanoparticles for potential MRI applications. I investigated Fe/Mn-doped Dy₂O₃ and Dy-loaded mesoporous silica, characterizing the nanoparticles using DLS and photoluminescence spectroscopy.
In my current project, “ML-Based Prediction of r₂ Relaxivity in MRI Contrast Agents,” I developed a machine-learning workflow to predict relaxivity in lanthanide-based MRI contrast agents. I performed data preprocessing, feature engineering, and regression modelling using Python.
I'm also developing an ML framework to screen nanoparticle–photosensitizer combinations for potential glioblastoma therapy, applying materials descriptors for computational candidate screening. My other engineering projects include synthesizing MXene for potential energy-storage applications and investigating grain refinement in Al–Si alloy through strontium modification.
During my internship at SOAN Enterprises, I gained experience in aluminium casting, forging, extrusion, and heat treatment, and observed direct-chill casting, homogenization, ageing, and quenching. I also learned industrial quality assurance and metrology practices.
