LEAP-HI: AI-Optimized 3D Printing of Super-Soft Materials for Personalized Sensing

医学界的共同需求是连续监测局部组织的能力，但是当前的感测技术缺乏针对不同个体的独特解剖结构的轮廓传感器所必需的个性化。这项旨在促进美国繁荣、健康和基础设施的领先工程（LEAP-HI）研究旨在开发低成本的个性化传感器，这些传感器可以按需制造，以提高各行各业美国人的健康和福祉。该方法利用对新材料的基础研究，结合先进的机器学习和人工智能，以“3D打印”个性化传感器，并将其应用于医疗保健-从假肢到诊断和治疗-可能会影响数百万人。这些跨越材料科学、工程和计算的进步将提高美国创新的经济竞争力，并通过实验和计算研究之间的紧密协同作用，帮助培养下一代科学家和工程师。一系列涵盖多样性、教育和外联的举措将进一步推动各年龄组工作的关键方面。这包括（a）在爱荷华州州立大学的新网络物理系统未成年人中的网络制造模块，（B）与墨西哥裔/西班牙裔促进协会，美洲原住民科学协会和全国黑人工程师协会的当地分会合作，吸引学生参与研究，（c）通过在沃尔夫探索和创新博物馆（MOXI）为儿童举办的活动，（d）与工业研究和服务中心合作，传播网络制造方面的最佳做法和培训模块，和（e）与加州大学圣巴巴拉分校和爱荷华州州立大学的传播办公室合作，向公众传播研究成果，以促进美国工程领导力的需求。这项研究将克服通过开发新材料和加工技术，3D打印具有与人体组织相匹配的机械性能的聚合物，实现个性化健康监测的传统传感器技术。我们的战略将通过以下方式对先进材料的制造转化产生根本性的见解：（1）设计和合成材料，这些材料固有地为3D打印提供可加工性，同时保持生物相容性和机械功能;（2）通过设备规模的建模和预测动力学对打印过程进行先进的多尺度模拟，以了解构建过程中的宏观进展，在微观结构尺度上，根据材料沉积和固化来定制材料特性;（3）传感器响应和物理感知机器学习模型的数据融合，以实现3D打印过程的实时预测和控制;以及（4）创建超软结构传感器，以实现独特的传感模式，对个性化人类健康产生社会影响。这些研究旨在通过利用计算和机器学习模型来利用数据革命，以收敛的方式设计更有效的聚合物和传感器。这项研究将产生实验和计算方法，也可以应用到其他工程学科。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

A common need in the medical community is an ability to monitor local tissue continuously, but current sensing technology lacks the personalization necessary to contour sensors for the unique anatomy of different individuals. This Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) research seeks to develop low-cost personalized sensors which can be fabricated on demand to enhance the health and well-being of Americans from all walks of life. The approach leverages fundamental research into new materials in tandem with advanced machine learning and artificial intelligence to ‘3D print’ personalized sensors with applications in health care—from prosthetics to diagnostics and therapeutics—that could impact millions of people. These advances spanning materials science, engineering, and computation will improve the economic competitiveness of the United States’ innovation and help train the next generation of scientists and engineers through a tight synergy between experimental and computational research. A series of initiatives spanning diversity, education, and outreach to further will advance key aspects of the work across age groups. This includes (a) modules on cybermanufacturing in a new cyber physical systems minor at Iowa State University, (b) working with the local chapters of Society for Advancement of Chicanos/Hispanics, Native Americans in Science, and the National Society of Black Engineers to draw students into research, (c) K-12 engagement through activities at the Wolf Museum of Exploration and Innovation (MOXI) for children, (d) working with Center for Industrial Research and Service (CIRAS) to disseminate best practices and training modules in cyber manufacturing, and (e) working with the communications offices of both the University of California at Santa Barbara and Iowa State University to disseminate the research to the public to promote the need for Engineering Leadership in the United States.This research will overcome the limitations of conventional sensor technologies for personalized health monitoring by developing new materials and processing techniques to 3D print polymers with mechanical properties that are matched to human tissue. Our strategy will yield fundamental insights into the translation of advanced materials to manufacturing by: (1) Designing and synthesizing materials that inherently provide processability for 3D printing while maintaining biocompatibility and mechanical function; (2) Advanced multi-scale simulations of the printing process through modeling and predictive dynamics at the device scale to understand macroscopic progression during the build process, and at the microstructure scale to tailor the material properties as a function of material deposition and curing; (3) Data fusion from sensor response and physics-aware machine learning models to enable real-time prediction and control of the 3D printing process; and (4) Creating ultra-soft structured sensors that enable unique sensing modalities for societal impact in personalized human health. These research aims to harness the data revolution by exploiting computational and machine-learning models to design more effective polymers and sensors in a convergent fashion. This research will yield experimental and computational methodologies that can also be applied to other engineering disciplines.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.