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

LEAP-HI：人工智能优化的超软材料 3D 打印，实现个性化传感

• 批准号: 2053760
• 负责人: Baskar Ganapathysubramanian
• 金额: $ 200万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053760&HistoricalAwards=false
• 关键词: LEAP; HI; AI; Optimized; 3D

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.

医学界的一个共同需求是监测局部组织的能力，但当前的传感技术始终缺乏针对不同个体独特解剖结构的轮廓传感器所需的个性化功能，这是美国繁荣、健康和基础设施的领先工程 (LEAP-HI)。 ）研究旨在开发可按需制造的低成本个性化传感器，以提高各行各业美国人的健康和福祉。该方法利用新材料的基础研究与先进的机器学习和人工智能相结合。 “3D打印”这些传感器在材料科学、工程和计算领域的进步将提高美国创新的经济竞争力，并帮助培养下一代。通过实验和计算研究之间的紧密协同，科学家和工程师将进一步推进跨年龄段工作的关键方面，其中包括（a）新网络物理中的网络制造模块。爱荷华州立大学的小系统大学，(b) 与奇卡诺/西班牙裔进步协会、美国原住民科学协会和国家黑人工程师协会的当地分会合作，吸引学生参与研究，(c) 通过沃尔夫博物馆的活动参与 K-12 的活动儿童探索与创新中心 (MOXI)，(d) 与工业研究与服务中心 (CIRAS) 合作，传播网络制造的最佳实践和培训模块，以及 (e) 与加州大学通信办公室合作圣巴巴拉和爱荷华州立大学向公众传播这项研究，以促进美国对工程领导力的需求。这项研究将通过开发 3D 打印的新材料和加工技术，克服传统传感器技术在个性化健康监测方面的局限性我们的策略将通过以下方式为先进材料转化为制造提供基本见解：(1) 设计和合成本质上为 3D 打印提供可加工性的材料，同时保持生物相容性和机械功能 (2) ) ) 高级多尺度(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；(4) 创建超软结构化传感器，实现独特的传感模式，对个性化人类产生社会影响；这些研究旨在通过利用计算和数据来驾驭数据革命。这项研究将产生也可应用于其他工程学科的实验和计算方法。该奖项反映了 NSF 的法定使命，并通过使用评估被认为值得支持。基金会的智力价值和更广泛的影响审查标准。

项目成果

Scalable adaptive algorithms for next-generation multiphase flow simulations

用于下一代多相流模拟的可扩展自适应算法

• DOI: 10.1109/ipdps54959.2023.00065
• 发表时间: 2022-09-25
• 期刊: 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
• 作者: K. Saurabh;Masado Ishii;Makr;A. Khanwale;H. Sundar;B. Ganapathysubramanian
• 通讯作者: B. Ganapathysubramanian

Distributed multigrid neural solvers on megavoxel domains

巨型体素域上的分布式多重网格神经求解器

• DOI: 10.1145/3458817.3476218
• 发表时间: 2021-11
• 期刊: Storage and Analysis
• 作者: Balu, Aditya;Botelho, Sergio;Khara, Biswajit;Rao, Vinay;Sarkar, Soumik;Hegde, Chinmay;Krishnamurthy, Adarsh;Adavani, Santi;Ganapathysubramanian, Baskar
• 通讯作者: Ganapathysubramanian, Baskar

NURBS-Diff: A Differentiable Programming Module for NURBS

NURBS-Diff：NURBS 的可微分编程模块

• DOI: 10.1016/j.cad.2022.103199
• 发表时间: 2022-05
• 期刊: Computer-Aided Design
• 影响因子: 4.3
• 作者: Deva Prasad, Anjana;Balu, Aditya;Shah, Harshil;Sarkar, Soumik;Hegde, Chinmay;Krishnamurthy, Adarsh
• 通讯作者: Krishnamurthy, Adarsh

Carbon Nanotube Composites with Bottlebrush Elastomers for Compliant Electrodes

用于顺应性电极的具有洗瓶刷弹性体的碳纳米管复合材料

• DOI: 10.1021/acspolymersau.1c00034
• 发表时间: 2021-11
• 期刊: ACS Polymers Au
• 作者: Self, Jeffrey L.;Reynolds, Veronica G.;Blankenship, Jacob;Mee, Erin;Guo, Jiaqi;Albanese, Kaitlin;Xie, Renxuan;Hawker, Craig J.;de Alaniz, Javier Read;Chabinyc, Michael L.;et al
• 通讯作者: et al

Digital Light Processing of Dynamic Bottlebrush Materials

动态洗瓶刷材料的数字光处理

• DOI: 10.1002/adfm.202200883
• 发表时间: 2022-02-23
• 期刊: Advanced Functional Materials
• 影响因子: 19
• 作者: Chungryong Choi;Yoichi Okayama;Parker T. Morris;Lindsay L. Robinson;M. Gerst;Joshua C. Speros;C. Hawker
• 通讯作者: C. Hawker