DMREF: Accelerating the Design of Adhesives with Nanoscale Control of Thermomechanical Properties

This Designing Materials to Revolutionize and Engineer our Future (DMREF) grant supports the generation of new knowledge related to the design of advanced adhesives for use in many industries, including automotive, aerospace, electronics, construction, and defense. Adhesives have significant benefits over traditional fasteners because they can bind different materials to one another and can be applied to wide areas which distributes the applied stresses and reduces failure. However, traditional epoxy adhesives can be very brittle. This research project will focus on understanding the structure-property relationships of epoxy adhesives that will enable the design of adhesives that are less brittle, more environmentally friendly, and are lightweight. This work will combine state-of-the-art experimental, simulation, and machine learning techniques to learn about these structured adhesives and accelerate the design and development cycles. As a part of this approach, we will ensure that the data and models will be widely shared so that they can be leveraged by academic, industrial and government researchers. Society will benefit from this project both through the new knowledge of structured adhesives, as well as through the education and training of student populations at three universities on how to effectively combine experimental and modeling approaches to design new materials. Hierarchically structured adhesives exhibit exceptional promise to incorporate multiple toughening mechanisms into epoxy adhesives. However, there are many outstanding questions about the potential multiscale assembly and toughening mechanisms of these materials. Hierarchical epoxy adhesives have large combinatorial variable spaces, complicated multiscale structures and properties, and inherent tradeoffs during multi-property optimization, which results in decades-long development cycles. The primary objective of this work is to develop the requisite fundamental understanding to enable the discovery and design of hierarchically ordered adhesives with tailored thermomechanical properties. Specifically, we will develop validated multiscale models to increase our understanding of block copolymer self-assembly in epoxy, the adhesion forces, and other key mechanical properties (toughness, modulus) for hierarchically ordered adhesives. The hierarchical structure of the materials will be developed using a combined top-down/bottom-up approach which leverages additive manufacturing and block copolymer self-assembly, respectively. To accelerate the discovery process, experimental and multiscale modeling-generated data will be incorporated along with machine learning tools for property prediction into an existing tool called Polymer design using Machine-learning Assisted Property Screening. The resultant upgraded tool will be an integrated computational materials engineering framework for adhesive design with strong connections to molecular building blocks and quantitative structure-property-performance relations which will be made available to a broader community of stakeholders. This project is supported by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) of the Directorate for Engineering (ENG), and the Division of Materials Research (DMR) of the Directorate for Mathematical and Physical Sciences (MPS). 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.