Anna C. Balazs (University of Pittsburgh)

            Using computational modeling, we will design materials systems that integrate biomimetic chemo-mechanical transduction and the “instruction set” provided by reaction networks to devise “autonomous” synthetic systems that both fuel and direct their own life-like actions. Currently, there exist a range of hard active materials that transduce chemical energy into mechanical motion. While biochemical reactions networks that control the spatiotemporal distribution of energy are highly complex, some networks can be broken down into modular feedback loops, which utilize just a few chemical components. By integrating biomimetic energy transduction in soft active materials with the regulation provided by feedback loops, we can design synthetic materials that begin to mimic the autonomy of living systems. In particular, this combination of components can yield self-fueling, shape-changing and self-directing behavior in synthetic materials that could reduce the cost in energy to operate the systems since the habitual external monitoring is replaced by the internalized self-regulating behavior.

            Meeting this goal is challenging because the action of a specific feedback loop on a responsive synthetic material is not completely known. Thus, we lack the information to combine the appropriate loops to produce the desired behavior in active matter. Furthermore, the new dynamics that can emerge from this coupling of feedback and active materials remains to be discovered. This lack of knowledge is limiting our ability to “instruct” synthetic materials to autonomously perform orchestrated steps. Hence, we are missing opportunities to devise new guidelines for actuating soft materials. We aim to address this shortcoming by determining how to integrate energy dissipation from active materials and spatiotemporal regulation from feedback loops for exceptional control over the dynamic behavior of synthetic materials systems.

            The proposed research takes advantage of our recent models for soft active matter (flexible sheets) that captures all the vital dynamic interactions in the system: hydrodynamics, chemical reaction-diffusion processes, mechanical behavior of the flexible sheets, and the fluid structure interactions that couple the fluid motion and the sheets’ behavior. For this study, the sheets offer two distinct advantages over hard active matter. First, the flexibility of these sheets provides greater degrees of freedom and enables the material to undergo adaptive reconfiguration. Second, the expanse of the flexible sheets allows us to vary the network’s spatial layout, which will influence the temporal behavior of the system. Using our modeling approach, we can design feedback loops to guide materials to produce situation-specific responses to different conditions, making the system adaptive to a range of environments. 

            The work is well aligned with the mission statements of the DOE Bimolecular Materials program. We are designing functional materials that encompass concepts from biology to manage energy transfer, allowing the materials to autonomously perform mechanical work. We will also demonstrate that the energy transduction performed at the molecular scale permits the microscale propulsion of the surrounding fluid, which drives the material to exhibit macroscale motion and shape changes.

            The findings are impactful to diverse fields in science and technology. Establishing approaches to control systems operating out of equilibrium (such as the systems considered here) constitutes an important goal in the physics and materials communities. Determining correlations between the action of feedback loops and a material’s response is valuable for research in chemistry, materials science, biology, and biomimicry. Our new computer simulations for multicomponent non-equilibrium systems can advance developments in computational modeling. Design rules for creating autonomously functional materials can benefit technological advances in “smart” actuators, robotic materials, and self-regulating machines. Hence the research will influence scientific thinking in a broad range of disciplines.