Collaborative Research: DMREF: Multi-material digital light processing of functional polymers

合作研究：DMREF：功能聚合物的多材料数字光处理

• 批准号: 2323715
• 负责人: Michael Chabinyc
• 金额: $ 114.94万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323715&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Multi; material

Non-technical Description: Additive manufacturing, commonly referred to as 3D printing, is a technology that could revolutionize a diverse array of applications, from personalized products to medical implants. One of the most powerful 3D printing techniques for making plastic objects uses patterned illumination of a resin to define the shape of a printed part. While light-based 3D printing is currently limited to a single material, this project aims to develop new resins that allow for printing multiple materials with varying properties, such as stiffness or elasticity, in a single step. The research will combine a feedback loop spanning materials chemistry, computational science, machine learning (ML), and physical characterization aligning with principles of the Materials Genome Initiative (MGI). The primary target for materials optimization will be the design of structured surfaces with varying mechanical properties for directing cell growth. Such surfaces are essential for both fundamental studies and practical applications. The integration of experiments, data analytics, and manufacturing methods in the research will prepare graduate and undergraduate students with diverse backgrounds for the future workforce. Public outreach and K-12 outreach to improve awareness in the science and engineering of advanced materials manufacturing methods will be carried out.Technical Description: The project will develop methods and materials for additive manufacturing that enable 3D printing of multi-material objects. The research will be accomplished through three main objectives that enable a unique ability to create multi-material objects with spatially defined mechanical properties by digital light processing (DLP). In Objective 1, multi-material DLP resins will be developed using machine learning methods to find chemistries that enable patterns with extreme mechanical contrast (moduli from kPa to GPa) in response to different wavelengths of light. In Objective 2, a multi-material DLP process will be developed using photoswitches to control light exposure that will be optimized by inverse design using digital twins of the DLP process. In Objective 3, machine-learning accelerated topology optimization will be used to define multi-material structures to direct cell behavior. This design tool will translate the platform of materials and processing routes into substrates that direct cell growth via spatially defined regions of topography and mechanical contrast. All three objectives will leverage an MGI-driven feedback loop between experiment and theory where advances in each area enhance the overall outcomes.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.

非技术描述：添加剂制造（通常称为3D打印）是一种技术，可以彻底改变各种应用程序，从个性化产品到医疗植入物。制造塑料对象的最强大的3D打印技术之一使用树脂的图案照明来定义印刷部分的形状。虽然目前基于光的3D打印仅限于一种材料，但该项目旨在开发新的树脂，允许单个步骤打印具有不同属性（例如刚度或弹性）的多种材料。这项研究将结合跨越材料化学，计算科学，机器学习（ML）的反馈回路，并与物质基因组计划（MGI）的原理保持一致。材料优化的主要目标是设计具有不同机械性能的结构化表面，以指导细胞生长。这样的表面对于基本研究和实际应用都是必不可少的。研究中实验，数据分析和制造方法的整合将为研究生和本科生提供不同背景的未来劳动力。将进行公共宣传和K-12外展，以提高高级材料制造方法的科学和工程学的认识。技术描述：该项目将开发添加剂制造的方法和材料，以实现3D多材料对象的3D打印。这项研究将通过三个主要目标来完成，这些目标能够通过数字光处理（DLP）创建具有空间定义机械性能的多物质对象的独特能力。在目标1中，将使用机器学习方法开发多物质DLP树脂，以找到具有极端机械对比度的模式（从KPA到GPA），以响应不同的光波长。在目标2中，将使用Photoswitches开发多物质DLP过程，以控制光线曝光，通过使用DLP工艺的数字双胞胎进行逆设计，该过程将优化。在目标3中，机器学习的加速拓扑优化将用于定义多物质结构以指导细胞行为。该设计工具将将材料和加工途径的平台转化为通过空间定义的地形区域和机械对比度引导细胞生长的底物。这三个目标将在实验和理论之间利用MGI驱动的反馈回路，在每个领域的进步增强了整体成果。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来评估的审查标准。