Collaborative Research: A Digital Manufacturing Platform to Democratize Biological Tissue Access Using Smart Two-Photon Polymerization

协作研究：利用智能双光子聚合实现生物组织访问民主化的数字制造平台

• 批准号: 2043168
• 负责人: Chenhui Shao
• 金额: $ 25万
• 依托单位: University of Illinois at Urbana-Champaign
• 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: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2043168&HistoricalAwards=false
• 关键词: Collaborative; Research; Digital; Manufacturing; Platform

Universal access to biological tissues for fundamental studies is limited, thereby constraining both the type and number of experiments that can be readily carried out. This is a particularly challenging problem for U.S. colleges and universities that do not possess the necessary infrastructure to further their tissue engineering research. This grant supports research to mitigate this challenge by extracting and storing tissue-structure information, which will be made broadly accessible to researchers, teachers, and students at any institution. The detailed information is obtained through the sequential process of imaging (reading), digitally storing, and laser-based manufacturing (writing) of the tissue architecture. Data obtained from this process will be uploaded onto an accessible data repository to facilitate broad dissemination. The project will also provide a platform to recruit students from diverse and underrepresented groups in STEM fields to learn about the emerging field of advanced biomanufacturing through strategic partnerships with local university chapters of engineering and science-based student affinity groups. Aspects of the research methods, as well as materials learned, will also be incorporated into both new and existing courses, and lecture modules developed for a new interdisciplinary online course on the freely accessible nanoHUB.org cyberinfrastructure platform. This award utilizes a convergence of disciplines to create a digital manufacturing platform, based on two-photon polymerization (TPP), that will enable cloud-based reading and writing of scaffolds with varying complexity in 3D collagen-fiber organization. Long-wavelength (near-infrared) optical pulses and long-working distance objectives will be used to enable penetration depths greater than 5x that has previously been reported, resulting in printed scaffolds volumes of 1 mm x 1 mm x 0.5 mm, which would be on the same scale as biologically relevant 3D in vitro models. The use of optical wavefront-shaping technology enables parallelization and reduction of writing artifacts, respectively. The machine-learning-based process control framework advances the fundamental understanding of TPP process variability, and facilitate high-throughput, high-fidelity biomanufacturing of scaffolds. This research contributes to the fields of statistics and machine learning by linking these disciplines to complex, unique data structures and types in biomanufacturing, as well as permit prototyping of collagen-based mechanical metamaterials.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.

用于基础研究的生物组织的普遍获取是有限的，从而限制了可以轻松进行的实验的类型和数量。对于不具备进一步开展组织工程研究所需基础设施的美国学院和大学来说，这是一个特别具有挑战性的问题。这笔赠款支持通过提取和存储组织结构信息来缓解这一挑战的研究，这些信息将供任何机构的研究人员、教师和学生广泛使用。详细信息是通过组织结构的成像（读取）、数字存储和激光制造（写入）的顺序过程获得的。从此过程中获得的数据将上传到可访问的数据存储库中，以促进广泛传播。该项目还将提供一个平台，招募来自 STEM 领域多元化和代表性不足群体的学生，通过与当地大学工程分会和科学学生亲和团体建立战略合作伙伴关系，了解先进生物制造这一新兴领域。研究方法以及所学材料也将被纳入新的和现有的课程，以及为可免费访问的 nanoHUB.org 网络基础设施平台上的新跨学科在线课程开发的讲座模块。该奖项利用学科融合创建一个基于双光子聚合 (TPP) 的数字制造平台，该平台将实现基于云的读取和写入 3D 胶原纤维组织中具有不同复杂性的支架。长波长（近红外）光脉冲和长工作距离物镜将用于使穿透深度大于先前报道的 5 倍，从而打印出 1 毫米 x 1 毫米 x 0.5 毫米的支架体积，这将与生物相关的 3D 体外模型具有相同的尺寸。光学波前整形技术的使用分别可以实现并行化和减少写入伪影。基于机器学习的过程控制框架增进了对 TPP 过程可变性的基本理解，并促进了支架的高通量、高保真度生物制造。这项研究通过将这些学科与生物制造中复杂、独特的数据结构和类型联系起来，为统计和机器学习领域做出了贡献，并允许基于胶原蛋白的机械超材料进行原型设计。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Hybrid physics-guided data-driven modeling for generalizable geometric accuracy prediction and improvement in two-photon lithography

• DOI: 10.1016/j.jmapro.2023.12.024
• 发表时间: 2024-01
• 期刊: Journal of Manufacturing Processes
• 影响因子: 6.2
• 作者: Sixian Jia;Jieliyue Sun;Andrew Howes;Michelle Dawson;K. Toussaint;Chenhui Shao

Machine-learning-enabled geometric compliance improvement in two-photon lithography without hardware modifications

• DOI: 10.1016/j.jmapro.2022.02.046
• 发表时间: 2022-04
• 期刊: Journal of Manufacturing Processes
• 影响因子: 6.2
• 作者: Yuhang Yang;Varun A. Kelkar;Hemangg S. Rajput;Adriana C. Salazar Coariti;K. Toussaint;Chenhui Shao