Reinventing Plastics for Electronics, Energy, Biomedical Applications and Additive Manufacturing
Precourt Pioneering Project
Awarded in the focus area of re-inventing plastics and their lifecycle of use.
Award start date: October 1st, 2021
The team involves experts from Stanford and SLAC, in theory, computation, molecular design, synthesis, with advanced characterization, and aims to establish the missing link between bond dynamics and physical, chemical, and mechanical properties of a new class of plastics consisting of dynamic polymer networks (DPNs). DPNs have reversible bonding and debonding that can be leveraged broadly for performance advantages: in manufacturing, for in-line error correction and scrap recovery; and in recycling, for separating monomers from additives via depolymerization. These advantages over single-use polymer networks (i.e., thermosets), which are typically made with static covalent bonds, are key enablers for sustainability efforts worldwide, e.g., in packaging, textiles, and structural composites. Because of the way DPNs interact with and adapt to their surroundings and to living things this research has the potential to also seed the growth of new technologies, including battery coatings, photovoltaics, electronic skin, portable power, haptic displays, and soft robotics.
This project aims to solve future plastics recycling and upcycling challenges by garnering a deeper understanding and control of the design and microstructures of polymers from the molecular to the bulk length scales.
Understanding materials design requirements - An important goal is to understand how molecular design impacts polymer dynamics and microstructure. Several polymer design parameters will be investigated using model polymers synthesized by the Bao group and these will be studied to understand how properties impact the mechanical strength of the material.
Theoretical framework development to establish predictive tools and design rules from molecular scale to bulk to predict polymer dynamics and mechanical properties. Cai and Qin have been developing simulations to connect the bond properties at the molecular level, through the evolution of polymer network, to the mechanical properties of the part at macroscopic scale.
Advanced characterization by Liang and Lee will develop in-situ characterization to study the structural and chemical dynamics of DPN to provide experimental verification for the theoretical models of Cai and Qin
The molecular design and processing rules established through this synergistic synthetic, theoretical, and characterization framework will provide a foundation for establishing a closed-loop circular life cycle of commercial thermoplastic and specially designed polymers.
Professor Zhenan Bao is Department Chair and K.K. Lee Professor of Chemical Engineering, and by courtesy, a Professor of Chemistry and a Professor of Material Science and Engineering at Stanford University. Bao founded the Stanford Wearable Electronics Initiate (eWEAR) in 2016 and serves as the faculty director.
Predicting mechanical strength of materials through theory and simulations of defect microstructures across atomic, mesoscopic and continuum scales. Developing new atomistic simulation methods for long time-scale processes, such as crystal growth and self-assembly. Applying machine learning techniques to materials research. Modeling and experiments on
the metallurgical processes in metal 3D printing. Understanding microstructure-property
relationship in materials for stretchable electronics, such as carbon nanotube network and
Jian Qin is an Assistant Professor in the Department of Chemical Engineering at the Stanford University. His research focuses on development of microscopic understanding of structural and physical properties of soft matters by using a combination of analytical theory, scaling argument, numerical computation, and molecular simulation.
Mengning Liang is a Staff Scientist at the Linac Coherent Light Source (LCLS), an X-ray Free Electron Laser (FEL) at SLAC National Accelerator Laboratory. Her research involves using the unique properties of X-ray FELs, including their coherence and high peak power to study the structure, dynamics, and kinetics of soft matter materials.