Swimming at microscales encounters stringent physical constraints due to the dominance of viscous forces over inertial forces. Swimming microorganisms have evolved their flexible appendages to overcome these constraints to swim effectively. These natural swimmers also developed versatile navigation strategies to explore their surroundings and search for specific targets. Extensive efforts in the past few decades have sought to elucidate underlying physical principles for cell motility, which has inspired a variety of designs for artificial microrobots. In this talk, I will discuss two problems of microswimmers in biological and artificial systems. I will first discuss the biophysical mechanisms through which swimming microorganisms sense and navigate their surroundings. I will then discuss the application of artificial intelligence in the development of intelligent microrobots that can self-learn how to swim and navigate at the microscale.
Professor Alan Tsang Cheng Hou received his B.Eng. and M.Phil. degree in Mechanical Engineering from the University of Hong Kong and Ph.D. in Mechanical Engineering from University of Southern California. He then continued his research as a post-doctoral research fellow at Stanford University and University of Arizona before he joined HKU. Professor Tsang worked extensively in interdisciplinary research. He employed theoretical tools of applied mathematics, computer modeling and simulations, machine learning and different experimental tools to investigate fundamental and application problems in fluid mechanics and biophysics. His research interests include active matter, microfluidics, micro-swimmers and biological adaptation.
Clean water and clean air are vital for public health. This project focuses on developing high-efficiency and environmentally sustainable filters for removing harmful air/water pollutants. The team has developed novel architectures and functionalities for the filters to achieve high permeance, high removal efficiency, and excellent reusability.
