FMRG: Manufacturing ADvanced Electronics through Printing Using Bio-based and Locally Identifiable Compounds (MADE-PUBLIC)

这项未来生态制造研究资助将实现未来智能，可扩展和民主化的制造模式，允许使用本地可识别资源（如来自植物的生物基材料）分布式打印低成本，可生物降解和可回收的电子设备。 这些电子设备是快速发展的物联网（IoT）中的关键组件。 分布式制造可以降低整体设备成本（通过节省运输成本），并使供应链在中断期间更具弹性（例如，在大流行期间）。该项目将作为原型展示使用植物衍生油墨的锂离子电池（LIB）供电的化学传感器的分布式打印。打印的设备将用于监测用于获得油墨的液体循环植物的生长条件。 通过定制和主动学习，同一平台可用于使用生物基材料打印许多其他复杂的、可生物降解/可回收的电子设备。通过与社区学院、美国制造业研究所和制造业孵化器的合作，该项目旨在通过几项新的、量身定制的举措，如跨机构证书计划、可打印电子黑客和DIY倡议以及公民科学竞赛，教育、培训、吸引和激发不同的学生受众和公众对未来可持续制造业的兴趣。该项目的目标是实现从精准农业/水培到先进的可生物降解和可回收电子产品的制造供应链。该项目将在三个领域取得重大科学进展：植物的精确生长，定制生物基油墨的制造以及可印刷电子产品的可持续生产。 作为一个融合的研究计划，该项目将进一步带来增值的可转移和可扩展的科学进步，包括用于制造的新型人工智能/机器学习（AI/ML）算法，设计可持续和系统优化制造流程的框架，以及将异构数据纳入制造数据系统同时自动细化模型的技术。 学习模型将植物表型和生长条件与纤维素和木质素提取相关联，将油墨配方与所需油墨特性相关联，并将印刷参数与电子设备性能和质量相关联。 该项目将建立一个基于开源生物材料的电子制造数据基础设施（BEMDI），通过建立一个对制造生物基可印刷电子产品感兴趣的创新者、教育工作者和行业合作伙伴社区来促进创新。 这项未来制造研究得到了土木，机械和制造创新（CMMI），生物科学（BIO），新兴前沿和多学科活动（EFMA），材料研究（DMR），电气通信和网络系统（ECCS），工程教育和中心（EEC），该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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).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.