An Integrated Experimental and Computational Platform for Discovery and Processing of Functional Nano-Emulsions

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The ability to design new materials rationally and engineer their properties through control of processing history will enable the synthesis of whole new classes of materials with unique properties that are easily manufacturable. This research looks to develop those techniques through the manipulation of a simple system consisting of nanoparticles and polymer nano-emulsions into a wide variety of structural forms with tuned mechanical and related properties. Soft materials built from polymers, particles, emulsions, and surfactants are employed in a broad array of emerging and societally relevant applications including medical and energy technologies, and the interface between the human body and the electronic domain. As such, this could open new manufacturing paradigms assisting driving new areas in the economy and advancing our knowledge in materials design. The discovery and processing of soft materials is idiosyncratic. For each application, a set of material properties: elastic modulus, yield stress, yield strain, permeability, conductivity, among others is needed and the materials choices are vast. This research looks to reducing the design space of possible materials through the combined design and fabrication of structures utilizing external fields. The research will develop approaches to generating the materials design space and the means to efficiently derive structures from the generated data. The use of external fields which interact intimately with the components of the polymer composite to arrange the components at the microscale during fabrication to produce new mechanical structures. This data-driven material design and processing is a new approach and paradigm to materials choice and structure development. The research is complemented by the development of a video game broadening the public understanding of soft materials and their properties which will be made publicly available. The discovery of new soft and biomaterials is driven largely by trial and error facilitated by many empirical and a few theoretical structure-property relations. When such relations are known, materials with a desired functionality can be synthesized by targeting the appropriate microstructure (e.g. crystalline, glassy, fractal, anisotropic). Moreover, many soft matter materials are quenched to assemble into structures which are not the lowest energy state and depend strongly on processing history. Because soft materials are multifunctional and applied broadly: to biomedical research and technologies, for oil and gas exploration and operations, in consumer care products, and throughout agriculture and the food industry, highly tailored materials are required to meet application specific needs. This research will develop the experimental and computational tools needed for the design multifunctional soft materials built from nano-emulsions, nano-scale droplets composed of a diverse array of oils suspended in water. Through variation of chemical composition of the droplets and the additives in the suspending solvent, nano-emulsions with well defined inter-particle interactions can be synthesized. By varying in time the temperature, salinity, applied magnetic fields, and flow fields, the nano-emulsions can be induced to aggregate into a gelled structure with a wide range of different morphological characteristics. A specific aim of this work is to understand and control how processing history can be used to exert fine control over this micro-structure and resulting properties of these nano-composites. This will be achieved by performing detailed simulations that explore the vast, experimentally accessible parameter space, and then utilizing machine learning tools to select fruitful nano-emulsions and processing methods to yield novel soft materials.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.