Tiny particles such as microplastics, pathogens, and other contaminants are increasingly found in groundwater, raising concerns for drinking water quality and ecosystem health. Predicting how these particles move underground remains a major scientific challenge because existing models do not accurately represent how particles behave in natural soils. This project seeks to improve understanding of how very small particles travel through soil and groundwater, which is essential for protecting water resources and improving cleanup strategies. The research will also support education, workforce development, and public awareness. By improving the ability to predict how contaminants spread, this project advances the national interest in safeguarding water quality, public health, and environmental sustainability. This project investigates how nanoscale surface features and repeated particle–surface interactions control particle attachment and transport in porous media. The research integrates laboratory experiments, surface characterization, and modeling to quantify how particles attach after multiple encounters with sediment grains. A new predictive framework will be developed that incorporates nanoscale attraction and interception history into transport models. The work combines theory, simulations, and machine learning to improve prediction of particle retention and mobility across scales. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.