Real-Time Nanoparticle Tracking & Aggregation Modeling in Aqueous Environments using Multi-Modal Bayesian Filtering

**Abstract:** This paper introduces a novel real-time methodology for tracking and modeling nanoparticle aggregation in aqueous environments, leveraging a combination of high-speed optical microscopy, acoustic scattering data, and a multi-modal Bayesian filtering framework. Existing tracking methods struggle with the dynamics of nanoparticle aggregation and its impact on overall transport behavior. Our approach offers a significant improvement in accuracy and predictive capability, facilitating optimization of nanoparticle-based delivery systems and environmental risk assessments, with an expected 30% improvement in predictive accuracy compared to existing Brownian motion models and a potential market for real-time environmental remediation in the ~$10 billion range. The system utilizes established computational techniques combined in a uniquely configured framework to achieve high-resolution tracking and robust aggregation modeling.