Understanding the impact dynamics of nanoparticles on liquid surfaces via molecular simulations and machine learning

The National Science Foundation Division of Chemical, Bioengineering, Environmental, and Transport Systems awarded $449,879 to Missouri University of Science & Technology on September 1, 2026, under the Engineering program (CFDA 47.041) to conduct molecular dynamics simulations and machine learning analysis of nanoparticle impacts on liquid surfaces through August 31, 2029.

The research combines molecular dynamics modeling with machine learning to understand how nanoparticle and liquid properties—including liquid surface tension, viscosity, nanoparticle size, geometry, and impact speed—affect impact behavior. The project will track time evolution of nanoparticle position, velocity, and force while capturing liquid surface geometry and contact line positions during impact, providing microscopic details inaccessible through current experimental methods. Machine learning will classify impact behaviors and perform sensitivity analyses, requiring smaller datasets and lower computational cost than conventional parameter sweeps. The research applies to air pollution remediation, advanced materials fabrication, and sensor design. Educational resources including visual animations from the computer models will be integrated into STEM programs to attract precollege students, and the work will provide research participation opportunities for students in thermal-fluid sciences. Work is performed at the University of Missouri System campus in Rolla, Missouri.

Abstract

The impact of particles on liquid surfaces is well understood for particles that are millimeter size or larger. However, the dynamics of nanoparticles impacting liquid surfaces are not so well understood. The tiny size of the nanoparticles and the fast collision speeds make IT difficult to study impact even with advanced instrumentation. Nevertheless, the interactions of nanoparticles with liquid surfaces is important in many applications including air pollution remediation, advanced materials fabrication, and sensor design. This project will combine advanced computer simulations with theoretical modeling to uncover the fundamental physics that govern nanoparticle impacts on liquid surfaces. Results from the project, such as visual animations generated from the computer models, will be integrated into educational resources to attract precollege students to stem programs. IT will also provide students with opportunities to participate in research in thermal-fluid sciences. The impact of nanoparticles on liquid surfaces is a highly complex process influenced by many particle and liquid properties, such as liquid surface tension, viscosity, nanoparticle size, geometry, impact speed, etc. This project will utilize molecular dynamics modeling to determine how each property affects the impact behavior. Moreover, molecular dynamics simulations will track the time evolution of nanoparticle position, velocity, and force, while capturing liquid surface geometry and contact line positions during the impact process. These microscopic details are inaccessible in current experiments. In addition, the research will incorporate machine learning to classify impact behaviors and perform sensitivity analyses. This approach, which requires a smaller dataset and lower computational cost than conventional parameter sweeps, will enhance the understanding of how various parameters affect impact dynamics. The computational framework will identify the critical conditions required for transitions between distinct nanoparticle-liquid impact modes. IT will also isolate the key parameters that dictate the impact dynamics under different circumstances, detect where traditional continuum-level predictions fail, and establish design rules to optimize a wide variety of industrial, and biomedical applications. 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.- subawards are not planned for this award.