Machine learning in polymer and colloid synthesis

This thesis develops an automated platform to produce nanoparticles from polymers. These nanoparticles can act as carriers in drug delivery. The developed system controls mixing of the polymer solutions with water, to generate these particles. Then it measures the particle sizes instantly and analyses data automatically. By changing the flow speeds, and solvent ratios, in a series of runs, it maps the effect of these factors on the particle size and shapes. The system can also adjust itself using smart algorithms to reach a targeted size. Overall, this work enables precise, efficient production of nanoparticles, important for drug delivery applications.