ERI: Meniscus-Guided Blade Coating of Ordered Silica Arrays on Al/HDPE, with PFAS-Free Superhydrophobicity as an Application

Water‑repellent coatings help surfaces stay clean, resist icing, and reduce corrosion across a wide range of applications, including those in the biomedical, aerospace, and energy industries. However, producing effective water repellent coatings on target surfaces using existing techniques (e.g., chemical methods) remains challenging. Existing patterning techniques are costly, intricate, and not easily scalable, while lower cost methods tend to produce disordered patterns with defects and limited durability. This award will overcome these limitations by developing a simple, scalable coating approach for creating uniform surface patterns on materials such as aluminum and plastic. The research will examine how silica nanoparticles can be organized into controlled patterns and then combined with safe, fluorine-free surface treatments to enhance water repellency. Insights from this project will inform the design of practical water repellent coatings that can be produced more reliably and at larger scales. The project will also support research training for undergraduate and graduate students and will engage students in grades 10–12 through STEM outreach activities. This award will establish a scalable convective self-assembly process on aluminum and high-density polyethylene surfaces and determine particle properties, substrate characteristics, and deposition conditions govern ordered nanoparticle assembly and superhydrophobic performance using advanced non-fluorinated chemistries. Meniscus-guided blade coating will be used to produce close-packed and non-close-packed silica nanoparticle patterns by tuning coating speed, particle concentration, solvent composition, temperature, humidity, particle size, particle surface chemistry, and substrate surface condition. The work will quantify how these factors affect spacing, order, domain size, and defects, and develop process–structure–property maps and control rules to target ordered textures. Fundamental interaction modeling will be used to clarify how particle–particle and particle–substrate interactions influence assembly, spacing, and defect formation. Selected ordered textures will then be functionalized using innovative non-fluorinated alkyl- and PDMS-based chemistries and evaluated for superhydrophobicity and durable performance relative to spray-coated controls. These efforts will provide the first in-depth understanding of self-assembled ordered nanopatterns on aluminum and HDPE and to establish an initial design framework for scalable, high-performance functional coatings on practical substrates. 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.