Tunable surface topographies via particle-enhanced soft composites

Abstract

We introduce a new class of particle-enhanced soft composites (PESC) that can generate, on demand, custom and reversible surface topographies, with surface features that can be highly localized. These features can be specifically patterned or alternatively can be random in nature. Our PESC samples comprise a soft elastomeric matrix with stiff particles embedded below the surface. The surfaces of the samples presented in this thesis are originally smooth and flat but complex morphologies emerge under application of a stimuli (here we show application of primarily compressive loading). We demonstrate these adaptive surface topographies with both physical experiments and finite element simulations which are used to design and to study the mechanical response. A variety of different surface patterns can be attained by tailoring different dimensionless geometric parameters (e.g. different particle sizes, shapes, and distributions), as well as material properties. The design space of the system and the resulting surface topographies are explored and classified systematically. Given that our method depends primarily on the geometry of the particle arrays, our mechanism for on-demand custom surface patterning is applicable over a wide range of length scales. These surfaces can be used in a variety of different applications including control of fluid flow, adhesion, wettability and many others.