CAREER: Investigating Nanosheets of Thermosensitive Polymer Grafted Nanoparticles: Integrated Research and Education

职业：研究热敏聚合物接枝纳米颗粒的纳米片：综合研究和教育

• 批准号: 2047743
• 负责人: Sanket Deshmukh
• 金额: $ 49.7万
• 依托单位: Virginia Polytechnic Institute and State University
• 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: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047743&HistoricalAwards=false
• 关键词: CAREER; Investigating; Nanosheets; Thermosensitive; Polymer

NONTECHNICAL SUMMARYThis award supports computational and theoretical research, and education aimed to advance understanding of nanomaterials and how they self-assemble. Different nanomaterials with various structures, properties, and functions have been developed by employing self-assembly approaches that spontaneously organize atoms or molecules into ordered structures. A new class of emerging nanomaterials are sheets made of highly ordered metallic nanoparticle arrays separated by attached organic molecular chains. Spacing between these nanoparticles largely determines their functional properties such as optical, catalytic, and electronic. Mechanical properties such as strength and flexibility often determine whether a nanosheet can be used in an application, such as an optoelectronic or biomedical device. Currently, the ability to design 2D nanosheets with desired spacing between nanoparticles and mechanical properties remains an open challenge. The PI aims to use simulation to develop design rules for controlling the spacing between the nanoparticles, and the mechanical properties of 2D nanosheets. The insights obtained from this research are aimed to help guide experimental efforts to engineer 2D nanosheets that possess desired functional and mechanical properties. This project includes an integrated education and outreach plan that leverages results and software tools generated in the course of the research. The plan is intended to improve student training and enhance diversity. The educational activities will focus on integrating machine learning methods and data science concepts into the undergraduate and graduate curriculum. In addition, an online three-week course will be developed; it will focus on applications of artificial intelligence in science and engineering for working professionals from industry as the target audience. The research team will develop virtual reality software, which will be used to stimulate the interest of middle- and high-school students in pursuing careers in science, technology, engineering, and mathematics. TECHNICAL SUMMARYThis award supports computational and theoretical research, and education on two-dimensional (2D) monolayered nanosheets of functionalized nanoparticles, which are composed of highly ordered, hexagonally packed inorganic nanoparticle arrays separated by grafted organic ligands. These nanosheets have emerged as a fundamentally new class of nanomaterials. The nanoparticle packing and mechanical properties of these nanosheets are important in determining their suitability for applications ranging from optoelectronics to biomedical fields. Packing controls functionality, for example optical, and catalytic properties of the nanosheets. However, due to a lack of a direct molecular-level characterization technique to study the structure of these nanosheets and lack of availability of accurate computational models, our ability to design 2D nanosheets with required nanoparticle packing and mechanical properties is limited. The PI aims to establish a fundamental molecular-level understanding of the nanoparticle packing and mechanical properties of 2D nanosheets of thermosensitive polymer functionalized nanoparticles using multi-scale simulation models. A machine learning accelerated optimization framework will be used to develop transferable and compatible coarse-grained models of thermosensitive polymers, metals, and solvents. These models will be employed to perform hypothesis-driven calculations aimed to address fundamental questions about the effect of polymer characteristics, nanoparticle design parameters, and metal type on the nanoparticle packing and mechanical properties of the nanosheets obtained through self-assembly of polymer grafted nanoparticles. The successful completion of this research would establish design rules for multifunctional nanosheets with desired nanoparticle packing and mechanical properties. The integrated education plan is directly tied to these research activities. The PI intends to incorporate artificial intelligence into the undergraduate and graduate curriculum by introducing machine learning methods to engineering students. The PI will develop an online three-week course with focus on applications of artificial intelligence in science and engineering for working professionals from industry as the target audience. As a part of integrated outreach activities, the research team will develop a "4-D Nanosimulator", which creates an interactive virtual reality environment that can be used to enter the world of atoms through simulation trajectories. This software will be used to engage middle- and high-school students and stimulate their interest in pursuing careers in science, technology, engineering, and mathematics.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.

该奖项支持计算和理论研究，以及旨在促进对纳米材料及其自组装方式的理解的教育。具有不同结构、性质和功能的不同纳米材料已经通过采用自发地将原子或分子组织成有序结构的自组装方法而被开发。一类新兴的纳米材料是由高度有序的金属纳米颗粒阵列制成的薄片，这些金属纳米颗粒阵列被连接的有机分子链分开。这些纳米颗粒之间的间距在很大程度上决定了它们的功能特性，如光学，催化和电子。诸如强度和柔性的机械性能通常决定纳米片是否可以用于诸如光电或生物医学器件的应用中。目前，设计具有纳米颗粒之间所需间距和机械性能的2D纳米片的能力仍然是一个开放的挑战。PI旨在使用模拟来开发控制纳米颗粒之间的间距和2D纳米片的机械性能的设计规则。从这项研究中获得的见解旨在帮助指导实验工作，以设计具有所需功能和机械性能的2D纳米片。该项目包括一个综合教育和推广计划，利用研究过程中产生的成果和软件工具。该计划旨在改善学生培训和加强多样性。教育活动将侧重于将机器学习方法和数据科学概念整合到本科和研究生课程中。此外，还将开发一个为期三周的在线课程;该课程将侧重于人工智能在科学和工程中的应用，面向作为目标受众的行业专业人士。该研究团队将开发虚拟现实软件，用于激发初中和高中学生追求科学，技术，工程和数学职业的兴趣。 该奖项支持计算和理论研究，以及功能化纳米颗粒的二维（2D）单层纳米片的教育，这些纳米颗粒由高度有序的六方堆积无机纳米颗粒阵列组成，由接枝的有机配体隔开。这些纳米片已经成为一种全新的纳米材料。这些纳米片的纳米颗粒包装和机械性能在确定其适用于从光电子到生物医学领域的应用中是重要的。堆积控制功能，例如纳米片的光学和催化性质。然而，由于缺乏直接的分子水平的表征技术来研究这些纳米片的结构和缺乏准确的计算模型的可用性，我们设计具有所需的纳米颗粒包装和机械性能的2D纳米片的能力是有限的。PI旨在使用多尺度模拟模型建立对热敏聚合物功能化纳米颗粒的2D纳米片的纳米颗粒包装和机械性能的基本分子水平理解。机器学习加速优化框架将用于开发热敏聚合物，金属和溶剂的可转移和兼容的粗粒度模型。这些模型将被用来执行假设驱动的计算，旨在解决有关聚合物的特性，纳米粒子的设计参数，和金属类型的纳米粒子的包装和机械性能的纳米片的聚合物接枝的纳米粒子的自组装获得的影响的基本问题。这项研究的成功完成将建立具有所需纳米颗粒包装和机械性能的多功能纳米片的设计规则。综合教育计划与这些研究活动直接相关。PI打算通过向工程专业的学生介绍机器学习方法，将人工智能纳入本科和研究生课程。PI将开发一个为期三周的在线课程，重点关注人工智能在科学和工程中的应用，面向行业专业人士作为目标受众。作为综合推广活动的一部分，研究小组将开发一个“4-D纳米模拟器”，该模拟器创建一个交互式虚拟现实环境，可用于通过模拟轨迹进入原子世界。该软件将用于吸引初中和高中学生，激发他们追求科学、技术、工程和数学职业的兴趣。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。