NSF Award
2323716
Non-technical Description: Additive manufacturing, commonly referred to as 3D printing, is a technology that could revolutionize a diverse array of applications, from personalized products to medical implants. One of the most powerful 3D printing techniques for making plastic objects uses patterned illumination of a resin to define the shape of a printed part. While light-based 3D printing is currently limited to a single material, this project aims to develop new resins that allow for printing multiple materials with varying properties, such as stiffness or elasticity, in a single step. The research will combine a feedback loop spanning materials chemistry, computational science, machine learning (ML), and physical characterization aligning with principles of the Materials Genome Initiative (MGI). The primary target for materials optimization will be the design of structured surfaces with varying mechanical properties for directing cell growth. Such surfaces are essential for both fundamental studies and practical applications. The integration of experiments, data analytics, and manufacturing methods in the research will prepare graduate and undergraduate students with diverse backgrounds for the future workforce. Public outreach and K-12 outreach to improve awareness in the science and engineering of advanced materials manufacturing methods will be carried out.<br/><br/>Technical Description: The project will develop methods and materials for additive manufacturing that enable 3D printing of multi-material objects. The research will be accomplished through three main objectives that enable a unique ability to create multi-material objects with spatially defined mechanical properties by digital light processing (DLP). In Objective 1, multi-material DLP resins will be developed using machine learning methods to find chemistries that enable patterns with extreme mechanical contrast (moduli from kPa to GPa) in response to different wavelengths of light. In Objective 2, a multi-material DLP process will be developed using photoswitches to control light exposure that will be optimized by inverse design using digital twins of the DLP process. In Objective 3, machine-learning accelerated topology optimization will be used to define multi-material structures to direct cell behavior. This design tool will translate the platform of materials and processing routes into substrates that direct cell growth via spatially defined regions of topography and mechanical contrast. All three objectives will leverage an MGI-driven feedback loop between experiment and theory where advances in each area enhance the overall outcomes.<br/><br/>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.
