- Junhong Chen (Principal Investigator)
- Stuart Rowan (Co-Principal Investigator)
- Mark Hersam (Co-Principal Investigator)
- Santanu Chaudhuri (Co-Principal Investigator)
- Elizabeth Ainsworth (Co-Principal Investigator)
This Future EcoManufacturing research grant will enable a future intelligent, scalable, and democratized manufacturing paradigm that allows for distributed printing of low-cost, biodegradable, and recyclable electronic devices using locally identifiable resources, such as bio-based materials derived from plants. These electronic devices are critical components in the rapidly evolving Internet of Things (IoT). The distributed manufacturing can lower overall device costs (by saving transportation costs) and make the supply chain more resilient during disruptions (e.g., during a pandemic). This project will demonstrate as a prototype the distributed printing of a lithium-ion battery (LIB) - powered chemical sensors using plant-derived inks. The printed devices will be used for monitoring growth conditions of hydronic plants that are used to derive the inks. The same platform can be used to print many other sophisticated, biodegradable/recyclable electronic devices using bio-based materials through customization and active learning. Through partnership with community colleges, Manufacturing USA Institutes, and manufacturing incubators, the project aims to educate, train, engage, and excite diverse student audiences and the public on the future sustainable manufacturing through several new, tailored initiatives, such as a cross-institutional certificate program, printable electronics hackathon and DIY initiative, and citizen science competition.
The goal of the project is to enable a manufacturing supply chain from precision agriculture/hydroponics to advanced biodegradable and recyclable electronics. The project will lead to major science advances in three domains: precision growth of plants, manufacturing of tailored bio-based inks, and sustainable production of printable electronics. As a convergent research program, the project will further lead to value-added transferrable and scalable scientific advancements, including novel artificial intelligence/machine learning (AI/ML) algorithms for manufacturing, a framework for designing sustainable and systematically optimized manufacturing processes, and techniques for incorporating heterogeneous data into manufacturing data systems while automatically refining the models. Learned models will correlate plant phenotypes and growth conditions with cellulose and lignin extraction, connect ink formulation with desired ink properties, and associate printing parameters with electronic device performance and quality. The project will lead to an open-source biomaterials-based electronics manufacturing data infrastructure (BEMDI) that fosters innovation through building a community of innovators, educators, and industry partners interested in manufacturing bio-based printable electronics. This Future Manufacturing research is supported by the Divisions of Civil, Mechanical and Manufacturing Innovation (CMMI), Biological Sciences (BIO), Emerging Frontiers and Multidisciplinary Activities (EFMA), Materials Research (DMR), Electrical Communications and Cyber Systems (ECCS), Engineering Education and Centers (EEC), and Mathematical Sciences (DMS).