This award supports a new approach to identify and realize new functional materials under realistic conditions. The method is directed evolution of non-living materials based on auto-regulatory scaffold hosts. The method is bio-inspired. It mimics the approach observed in living things. The research involves developing a platform which incorporates synthesis, screening and feedback, by design, and offers a practical pathway to materials discovery. This strategy overcomes the limitations of screening materials via computer simulations only. The project builds on the convergence of different research areas such as tissue engineering, systems biology, scalable nanomanufacturing and machine learning. The discovery of new materials leads to new functionalities, which leads to new devices and systems, which leads to new products, which benefits society and economy and enhances the nation's prosperity and security. The project demonstrates a paradigm shift in materials discovery and invention. Education plans involve training graduate students and postdocs in convergence approaches to materials screening and discovery, design and realization of auto-regulatory scaffolds and machine learning. Outreach plans are to develop programs such as dissemination through public lectures, integration of research results into new undergraduate courses, and publication of perspectives that combine convergence of research from different fields.
The project's approach is to screen materials through the auto-regulatory interaction of sensors, regulators and known and unknown materials. These components are located on scaffolds, which are tissue engineering-inspired constructs, whose dimensions are convenient for the developed fabrication and synthesis tools and which may be adapted to a wide range of node materials that include hydrogels, polymers, nanomaterials, and biomaterials. The auto-regulatory aspects of the research involve humidity and photothermal energy sources, among others. The auto-regulatory processes are based on interference effects, thermal expansion, chemical reactions, and cellular response and motion for which the project develops theory and modeling. One benefit of the evolutionary platform is that it interfaces seamlessly with seemingly incompatible combinations of materials such as hydrogels and semiconducting nanomaterials or biological cells and inorganic materials. Materials screening is coupled with machine learning methods for prediction of new materials, which can be extended to other user-defined outcomes.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
