Over the last few decades, researchers have made significant advances in human-electronic interfaces, reprogramming DNA, sensing pico-molar concentration biomarkers, and biomedical implants. An outstanding challenge preventing these advances from impacting lives is their long-term durability in the human body. The body recognizes any interface with artificial materials as a wound and uses clotting or other methods to heal the interface. That process begins with the nonspecific adsorption of proteins, as seen on the left. Creating mechanically strong, durable, and biocompatible materials which prevent this process is the key to unlocking these novel technologies. In the White Lab, we're designing such materials using new computational methods to accelerate materials design.
Self-assembly is the process of molecules spontaneously organizing into well-defined structure. The structures formed in self-assembly are highly sensitive to the molecular interactions and often result in self-similar hierarchical structures as shown on the left. Modeling and controlling self-assembly is a goal of our research, for both the design of materials and to increase our understanding of protein aggregation diseases. Rationally designed self-assembled materials can lead to more efficient organic polymer photovoltaics, better biomaterials, and high strength materials. Our unique approach to modeling self-assembly is to use experiments, simulations, and coarse-graining in a hierarchical multi-scale modeling approach.
The continued growth of renewable energy sources will lead to grid-level intermittent supply and demand mismatch. There are a number of research and engineering solutions that address this problem including grid-level energy storage and demand response via a smart grid infrastructure. Vehicle-to-grid (V2G) is a promising approach because it uses the existing resource of electric vehicle batteries as the energy storage medium. Electric vehicles charge at night while power is cheap, commute to work, and discharge when demand is high yielding the electric vehicle owner a profit. This simple sounding process has a number of complicating economic issues and we research them by simulating individual's behavior using survey-derived data about work and commute habits. For example, charge/discharge efficiency, battery degradation, and location-based marginal pricing of electricity. This project is part of our department's alternative energy MS degree program and provides student an opportunity to apply principles of simulation to social and engineering challenges.
Undergraduate STEM laboratory sessions (labs) are an integral part of higher-education because of their capability to develop student conceptual understandings, teach the process of inquiry, and generate enthusiasm for a topic. Although there are benefits to creating computer-based STEM labs, they cannot displace tactile, collaborative learning. Augmented reality (AR) labs offer an interesting middle ground because they maintain the tactile interaction and collaboration necessary to engage participants but have the freedom and flexibility of computer-based learning. Our research is in the inclusion of interactive simulation to provide realistic feedback to participants, like when interacting with a molecular in AR. This research is done in collaboration with April Luehmann from the Warner School of Education and Brendan Mort, the director of the Center for Integrated Research Computing.