Automated Image-Based Particle Tracking for Drug Delivery Systems: From Earth to Microgravity Environments

Nanofluidic represents a critical frontier in understanding fluid behavior at nanoscale dimensions, where conventional macroscale principles fail to describe particle motion. At these microscopic scales, emerging forces alter fluid dynamics, necessitating novel theoretical frameworks for comprehending particle movement and interaction. Understanding these nanoscale phenomena is essential for developing sophisticated implantable drug delivery devices. By examining the relationships between pH, molecular dimensions, and channel characteristics, the study demonstrates a pathway to developing highly personalized therapeutic strategies. The experimental approach is based on a specialized device with a macroscale input channel connected to microchannels, designed to investigate particle diffusion dynamics. Notably, the experiments were conducted both on the International Space Station and in terrestrial environments, enabling a comprehensive analysis of gravitational influences on transport mechanisms. This work introduces a custom image processing algorithm capable of segmenting and tracking particles, even across noisy microscopy images, addressing the significant challenge of managing complex nanoscale datasets. The findings validate the experimental approach’s reproducibility across microgravity and terrestrial conditions and demonstrate the effectiveness of our theoretical model in predicting particle concentration distribution. Build on these promising findings, the research eagerly anticipates future in vivo studies that will further refine and expand the understanding of drug release mechanisms. The study opens innovative pathways for designing targeted pharmaceutical formulations capable of precise, environment-specific drug diffusion. This research holds potential to transform treatment strategies across various pathological conditions, marking a significant advancement in personalized medical technology.