Pierre-Thomas Brun, Assistant Professor of Chemical and Biological Engineering (CBE)
Seed start and end dates: November 1, 2018 - October 31, 2019
Natural soft materials are often architected at all scales, and shaped in periodic structures that achieve advanced functionalities which cannot be matched by man-made materials [1–3]. Adapting these concepts to our own technological needs requires the development of a new fabrication pathways to structure and shape soft materials. The research objective of this project is to devise and formalize a new class of topologically and hierarchically architected soft materials (ASM) using interfacial instabilities. Our ASM consist of silicone based elastomers patterned with liquid inclusions whose shape, arrangement and composition is programmed using the rules and tools of fluid mechanics. Specifically, the Rayleigh-Plateau instability (RPI) is harnessed in viscous threads printed in polymer melts that cure in finite time so as to ’freeze’ the disperse phase and form tangible objects (see Fig1). The project is concerned with the directed control of these instabilities to robustly fabricate and control the size, the arrangement, and the morphology of our ASM.
While instabilities are traditionally regarded as a route towards failure in engineering, the PI aims to follow a different path; taming fluidic instabilities and harnessing the patterns and structures they naturally form. This methodology, recently demonstrated by the PI in another problem, capitalizes on the inherent periodicity, scalability, versatility and robustness of instabilities. This new design paradigm – building with instabilities – calls for an improved understanding of instabilities and pattern formation in complex media. Stability analysis is a classic topic in fluid mechanics, yet, little is known on the so called inverse problem: finding the optimal set of initial conditions and interactions that will be transmuted into a target shape without direct external intervention. More broadly, the project is rooted on the basis of recognizing model experiments as a valuable and powerful tool for discovery and exploration, in turn seeding the development of formal and predictive models.