Abstract: Soft materials functionalized for application-specific mechanical, chemical, and biological properties are ubiquitous in our lives, be it biomedical implants, hydrogel-based drug delivery platforms; industrial formulations such as paints, self-healing coatings, and agricultural sprays; stimuli-responsive grippers in soft robotics; or biological materials such as mucosal fluids, extracellular matrices, and cytoskeletal mimics. Yet, understanding the mechanics – energy, mass, and momentum transport – of these complex material systems with hierarchical multi-scale micro-structures is a persistent challenge, holding back fuller utilization in vital areas of research, development, and application.
My talk will focus on two aspects of past and ongoing research: soft materials formulation and rheological modeling, and their use in non-Newtonian fluid mechanical applications. I will first discuss my doctoral work on the dynamics of complex fluid droplets impacting coated substrates relevant for various environmental and industrial applications, associated scaling relationships for predicting coating behaviors, and soft materials design strategies for controlling the deposition and coating mechanics through control over high-order rheological properties. Secondly, I will discuss details of my current postdoctoral work on the principles of mechano-chemical design and modeling involved in the development of dynamic soft networks with stimuli-responsive and transient transport properties, for use as carrier fluids in environmental sprays and long-term drug delivery platforms.
Firstly, this talk will outline a computational framework based on the electrophysiological activity of a single pain-sensing neuron, which we employed to identify mechanisms of pain sensation mutations and neuropathic pain. Secondly, it will demonstrate a graph-based mathematical model that captures the spectral and spatial features of the brain’s functional activity. This modeling approach revealed biophysical alterations in Alzheimer’s disease, different stages of sleep, and spontaneous fluctuations in electrophysiological functional activity. Together, these results aim to highlight the importance of such modeling techniques in identifying the underlying biophysical mechanisms of neuronal dynamics, which can be intractable to infer using neuroimaging data alone.
Bio: Samya got his B.Tech. degree in Mechanical Engineering from IIT Kharagpur in 2017. He graduated with his Ph.D. in Mechanical Engineering supervised by Prof. Randy Ewoldt in the University of Illinois Urbana Champaign in 2022, working on the formulation, characterization, and rheological modeling of complex fluids and soft materials for droplet impacts, coatings, and direct ink writing applications. He is currently training as a postdoctoral scholar with Prof. Eric Appel in the Department of Materials Science and Engineering at Stanford University, developing hydrogel-based functional soft materials for environmental conservation and drug delivery applications.