Understanding Non-Equilibrium Assembly for Additive Manufacturing of Functional Polymers

Nov 29, 2023, 4:00 pm5:00 pm
Princeton faculty/staff/students only.



Event Description

Printing technologies have the potential to revolutionize manufacturing of electronic and energy materials by drastically reducing the energy cost and environmental footprint while increasing throughput and agility. For instance, printing organic solar cells can potentially reduce energy payback time from 2-3 years to as short as 1 day! At the same time, additive manufacturing of such functional materials brings a new set of challenges demanding exquisite control over hierarchical structures down to the molecular-scale. We address this challenge by understanding the evaporative assembly pathway and flow-driven assembly central to all printing processes. We discover a surprising chiral liquid crystal mediated assembly of achiral semiconducting polymers in an evaporating meniscus. We uncover the molecular assembly mechanism and further show that the chiral helical structures can be largely modulated by controlling printing regimes. Such new topological states of semiconducting polymers can empower unprecedented control over charge, spin and exciton transport, reminiscent of how Nature efficiently transfer electrons and transduce energy using chiral helical structures. The ability to control non-equilibrium assembly during printing sets the stage for dynamically modulating assembled structures on the fly. We demonstrate this concept by programming nanoscale morphology and structure color of bottlebrush block copolymers during 3D printing. This approach holds the potential to reduce the use of environmentally toxic pigments by printing structure color. Complementing the above hypothesis-driven approach, we are pursuing data-science driven approach to drastically accelerate discovery and manufacturing of functional polymers. By linking automated synthesis, testing and machine learning in a close-loop, we are able to optimize function highly efficiently while discovering new physical insights for transferring closed-loop optimization into hypothesis driven discovery.