A computational and experimental study of Flow-Driven Assembly of Nanoparticles on Polymer Substrates

Achieving rapid, controllable, and scalable assembly of nanomaterials is essential for the advanced manufacturing of functional coatings and devices. In this work, we explore a flow-driven, in situ assembly process of nanoparticles on polymer substrates, focusing on the interplay between fluid dynamics and nanoparticle deposition behavior. A flow chamber system, which is typically used in cellular biomechanics research, is adapted for nanomanufacturing applications. This system was fabricated using SLA 3D printing and equipped with precise fluid control and measurement capabilities, enabling robust regulation of laminar flow conditions during the assembly process. Real-time optical microscopy facilitates direct observation and quantitative analysis of nanoparticle coverage. Complementary computational fluid dynamics (CFD) simulations confirm the uniformity and stability of the flow field, which correlates with highly reproducible and consistent deposition across the substrate. Furthermore, machine learning-based analysis enables rapid, automated assessment of assembly kinetics and optimization of process parameters. This versatile approach is compatible with a wide range of nanomaterials and can be extended to patterned or biologically relevant surfaces.