I am interested in the interactions between biological organisms and environmental fluids and aerosols. In my PhD work, I investigated the role of eyelashes in keeping the eyes of mammals wet and clean. Through this work, it was discovered that mammals have converged on an optimal eyelash length, of one-third the eye's width, that minimizes the airflow at the ocular surface. I also researched the methods in which honey bees effectively remove pollen from their sensory organs during grooming.
PhD: Mechanical Engineering, Georgia Institute of Technology, GA, 2015
BS: Mechanical & Aerospace Engineering, University of Miami, FL, 2010
Journal of The Royal Society Interface, 14(131):20170134, The Royal Society, June 2017 (article)
Animals using adhesive pads to climb smooth surfaces face the problem of keeping their pads clean and functional. Here, a self-cleaning mechanism is proposed whereby soiled feet would slip on the surface due to a lack of adhesion but shed particles in return. Our study offers an in situ quantification of self-cleaning performance in fibrillar adhesives, using the dock beetle as a model organism. After beetles soiled their pads by stepping into patches of spherical beads, we found that their gait was significantly affected. Specifically, soiled pads slipped 10 times further than clean pads, with more particles deposited for longer slips. Like previous studies, we found that particle size affected cleaning performance. Large (45 μm) beads were removed most effectively, followed by medium (10 μm) and small (1 μm). Consistent with our results from climbing beetles, force measurements on freshly severed legs revealed larger detachment forces of medium particles from adhesive pads compared to a flat surface, possibly due to interlocking between fibres. By contrast, dock leaves showed an overall larger affinity to the beads and thus reduced the need for cleaning. Self-cleaning through slippage provides a mechanism robust to particle size and may inspire solutions for artificial adhesives.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems