Superoleophobic surfaces are characterized by their ability to repel oils, where oil droplets with low surface tension form contact angles greater than 150°. This feature causes the oil droplets to roll off the surface easily, which makes these surfaces especially useful in applications like self-cleaning materials, drag reduction in fluid systems, and preventing biological fouling.

The fabrication of oil-repellent surfaces in air is more challenging than creating superhydrophobic surfaces that repel water, primarily because oils generally have much lower surface tension than water. Despite this, recent findings have provided a new perspective. It has been observed that surfaces displaying superhydrophilic in air can become superoleophobic when submerged in water. This interesting phenomenon can be explained by the underwater Cassie–Baxter state. In this state, water occupies the microstructures on the surface, forming a trapped layer that serves as an effective barrier against oil. Thus, when oil droplets are submerged, they only touch the very tops of these microstructures, resulting in extremely high contact angles and remarkable oil repellency.

Building on these insights, there has been growing interest in surfaces functionalized with hydrophilic polymer brushes. When these brushes are immersed in water, they become highly hydrated, creating a dense hydration layer that acts as a barrier to oil adhesion. This hydration layer minimizes the contact between the oil droplets and the surface, effectively repelling oil and preventing it from spreading.

In our research, we explore the interactions of oil droplets with surfaces grafted with hydrophilic polymer brushes underwater. We utilize a computational method known as Many-Body Dissipative Particle Dynamics (MDPD) to conduct our study. Through this study, we aim to unravel how the unique properties of the polymer brush layer, such as grafting density, chain length, and stiffness, influence its oil-repelling performance in water.

In this project, we use the Many-Body Dissipative Particle Dynamics (MDPD) simulation method, a powerful technique for studying droplet wetting behavior. As an intern, you will have the opportunity to:

• Learn and apply the MDPD simulation method to examine how droplet size affects the wetting behavior of underwater oil droplets.
• Develop and utilize custom code (e.g., in C++) to analyze simulation trajectories.

Additionally, we are developing a multi-GPU DPD simulation program. If you are interested in high-performance computing, you will have the chance to learn GPU programming and how to leverage GPUs to enhance simulation performance.