Collaborative Research: DMREF: Designer 3D Mesoscale Materials Synthesized in the Self-Assembly Foundry

合作研究：DMREF：在自组装铸造厂合成的设计师 3D 介观尺度材料

• 批准号: 2118678
• 负责人: Alfredo Alexander-Katz
• 金额: $ 120万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118678&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Designer; 3D

Self-assembly is one of the most promising avenues for the manufacturing/synthesis of materials and systems with exquisite control over nanoscale features while being fast, scalable, and inexpensive. It could enable the next revolution in integrated systems and designer mesoscale materials for multiple applications including information systems, sensing, actuation, and artificial intelligence. However, there are still many challenges in utilizing self-assembly as a precision fabrication technique. This project will develop a self-assembly foundry by implementing a dual assembly line in which experimental, molecular simulations, and artificial intelligence techniques are deployed simultaneously to create designer 3-dimensional (3D) nanostructured systems. This effort will examine the limits of 3D self-assembly to accelerate the fabrication of custom systems, from interconnected nanosystems used in computer chips to designer mechanical nanostructures for sensing and actuation. Particular emphasis will be placed in developing new manufacturing routes using topological principles. The broader impacts of this project envision a basis for training a new generation of scientist or engineers that can engage effectively with industry and academia. This project will also lead to the training of community college students and the development of online learning materials, as well as public engagement activities. The convergence of academia and industry; theory, computation, and experiment; different mentoring perspectives; and the high-level view of the self-assembly manufacturing process will provide a rich environment for the participants to develop new knowledge, skills, and abilities, with a strong emphasis on training and knowledge transfer.The scientific challenge that this project will tackle is that of being able to create arbitrary 3D structures using block copolymers, which are polymers composed of two or more chemistries. Using state-of-the-art computational techniques in conjunction with experiments, this project aims to unravel the design rules for self-assembling layer-by-layer systems that have a predesigned, non-symmetric, and intricate 3D structure from the information contained in the substrate. While in the optics field this corresponds to creating a hologram, the rules for doing so in a self-assembly system are not clear. Fault tolerance is one of the main challenges and topological constructs will be sought to make the self-assembly process robust. Direct learning through a new dual self-assembly line concept that combines, on one branch the physical process and in another branch the virtual process, will also be employed. Both of these assembly lines will be connected through an artificial intelligence engine to find the hidden correlations and learn the design rules. Thus, this project has the potential to set a blueprint for the future of nanomanufacturing.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.

自组装是材料和系统制造/合成最有前途的途径之一，可以精确控制纳米级特征，同时快速、可扩展且廉价。它可以为信息系统、传感、驱动和人工智能等多种应用带来集成系统和介观材料设计的下一次革命。然而，利用自组装作为精密制造技术仍然存在许多挑战。该项目将通过实施双装配线开发自组装铸造厂，其中同时部署实验、分子模拟和人工智能技术，以创建设计师 3 维 (3D) 纳米结构系统。这项工作将检查 3D 自组装的局限性，以加速定制系统的制造，从计算机芯片中使用的互连纳米系统到用于传感和驱动的设计机械纳米结构。将特别强调利用拓扑原理开发新的制造路线。该项目的更广泛影响为培训能够有效参与工业界和学术界的新一代科学家或工程师奠定了基础。该项目还将促进社区学院学生的培训、在线学习材料的开发以及公众参与活动。学术界与工业界的融合；理论、计算和实验；不同的指导观点；自组装制造过程的高级视图将为参与者提供丰富的环境，以发展新的知识、技能和能力，并重点强调培训和知识转移。该项目将解决的科学挑战是能够使用嵌段共聚物（由两种或多种化学物质组成的聚合物）创建任意 3D 结构。该项目利用最先进的计算技术与实验相结合，旨在阐明自组装逐层系统的设计规则，这些系统具有预先设计的、非对称的、复杂的 3D 结构，来自基板中包含的信息。虽然在光学领域这相当于创建全息图，但在自组装系统中这样做的规则尚不清楚。容错是主要挑战之一，我们将寻求拓扑结构来使自组装过程变得稳健。还将采用通过新的双自装配线概念进行直接学习，该概念将一个分支上的物理过程与另一个分支上的虚拟过程相结合。这两条装配线将通过人工智能引擎连接起来，以找到隐藏的关联并学习设计规则。因此，该项目有可能为纳米制造的未来制定蓝图。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

Synthesis and Self-Assembly of Silicon-Containing Azobenzene Liquid Crystalline Block Copolymers

• DOI: 10.1021/acs.macromol.2c02343
• 发表时间: 2023-01
• 期刊: Macromolecules
• 影响因子: 5.5
• 作者: Lin Weng;Mingchao Ma;Chenxiao Yin;Zhixiong Fei;Ke-Ke Yang-Ke;C. Ross;Lingying Shi

Emergence of layered nanoscale mesh networks through intrinsic molecular confinement self-assembly

通过内在分子限制自组装形成分层纳米级网状网络

• DOI: 10.1038/s41565-022-01293-z
• 发表时间: 2023
• 期刊: Nature Nanotechnology
• 影响因子: 38.3
• 作者: Sun, Zehao;Liu, Runze;Su, Tingyu;Huang, Hejin;Kawamoto, Ken;Liang, Ruiqi;Liu, Bin;Zhong, Mingjiang;Alexander-Katz, Alfredo;Ross, Caroline A.
• 通讯作者: Ross, Caroline A.

Reversible Morphology Locking via Metal Infiltration in a Block Copolymer

通过嵌段共聚物中的金属渗透实现可逆形态锁定

• DOI: 10.1021/acsnano.3c00723
• 发表时间: 2023
• 期刊: ACS Nano
• 影响因子: 17.1
• 作者: Ma, Mingchao;Liu, Runze;Su, Tingyu;Sun, Zehao;Ross, Caroline A.
• 通讯作者: Ross, Caroline A.