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：增材制造，通常被称为3D打印，是一种可以彻底改变从个性化产品到医疗植入物等各种应用的技术。用于制造塑料物体的最强大的3D打印技术之一使用树脂的图案化照明来定义打印部件的形状。虽然基于光的3D打印目前仅限于单一材料，但该项目旨在开发新的树脂，允许在一个步骤中打印具有不同特性的多种材料，例如刚度或弹性。该研究将结合联合收割机的反馈回路跨越材料化学，计算科学，机器学习（ML）和物理表征与材料基因组计划（MGI）的原则。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。这种表面对于基础研究和实际应用都是必不可少的。实验，数据分析和制造方法在研究中的整合将为未来的劳动力准备不同背景的研究生和本科生。将开展公众宣传和K-12宣传，以提高科学和工程领域对先进材料制造方法的认识。技术说明：该项目将开发用于增材制造的方法和材料，使多材料物体的3D打印成为可能。该研究将通过三个主要目标来完成，这些目标使其能够通过数字光处理（DLP）创建具有空间定义机械特性的多材料对象的独特能力。在目标1中，将使用机器学习方法开发多材料DLP树脂，以找到能够响应不同波长的光的具有极端机械对比度（从kPa到GPa的模量）的图案的化学物质。在目标2中，将开发一种多材料DLP工艺，使用光电开关控制曝光，并使用DLP工艺的数字孪生进行逆向设计优化。在目标3中，机器学习加速拓扑优化将用于定义多材料结构以指导细胞行为。这种设计工具将把材料平台和加工路线转化为基质，通过空间定义的地形和机械对比区域指导细胞生长。这三个目标都将利用实验和理论之间的MGI驱动的反馈回路，每个领域的进步都会增强整体成果。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。