CAREER: Evaluating the Process-Structure-Property Relationships of Carbon Nanotube Forests with In-Situ Synthesis Observation and Dynamic Simulations

这个教师早期职业发展（CAREER）计划研究项目通过直接观察和免费的数值模拟，研究碳纳米管在集体成长过程中如何相互作用。相对于许多其它常规工程材料，纳米级材料如碳纳米管提供了优越的上级机械、电学和热学性质。当大量的碳纳米管阵列合成在一起时，它们形成了相互连接和自组织的群体，称为碳纳米管森林。生长中的纳米管之间的相互作用导致结构弯曲和扭结，从而降低其力学性能。 采用人工智能和机器学习的数值模拟将促进与纳米管形成相关的高维处理空间的快速探索，并将指导该项目的实验方面。这项工作的结果和对合成过程的理解将有助于控制碳纳米管在合成过程中的相互作用机制，从而增强和工程化碳纳米管森林的性质。中学生将通过为期一周的夏令营参与这项研究，在夏令营中，通过与密苏里州大学艺术与考古博物馆的合作，将动手纳米材料工程与艺术表达相结合。这些调查的结果将使使用纳米级的艺术和观察，以吸引和激励小学生在STEM为基础的学习与大学艺术博物馆合作。驱动碳纳米管森林自组装的力量和机制目前知之甚少，部分原因是原位诊断技术有足够的分辨率来询问不断发展的机械相互作用是落后的。该项目将在扫描和透射电子显微镜内使用原位合成方法实现碳纳米管森林生长和组装的直接可视化。将研究各种合成条件，以将重要加工因素的作用与观察到的行为联系起来。透射电子显微镜技术将不仅有利于单个纳米管及其宿主催化剂纳米颗粒的时间分辨目视检查，而且还有利于原子分辨率或接近原子分辨率的成分分析。扫描电子显微镜技术将检查协调组装的碳纳米管在更大的人口在长度尺度范围从几十纳米到几十微米。实验观察结果将输入到时间分辨有限元模拟中，以询问纳米管森林组装期间产生的力。合成后，碳纳米管森林的力学性能将使用原位扫描电子显微镜压缩测量和有限元模型模拟进行验证。经过验证的模型将作为一种工具，使用人工智能算法，自动搜索适当的合成条件，满足用户定义的森林属性集的过程-结构-属性关系的快速评估。一个碳纳米管森林合成模拟工具将在nanohub.org上免费提供，以促进该项目中开发的模拟的更广泛影响。

This Faculty Early Career Development (CAREER) Program research project investigates how carbon nanotubes interact with one another during collective growth by using direct observation and complimentary numerical simulation. Nanoscale materials such as carbon nanotubes offer superior mechanical, electrical, and thermal properties relative to many other conventional engineering materials. When vast arrays of carbon nanotubes are synthesized together they form interconnected and self-organized populations known as carbon nanotube forests. The interactions between growing nanotubes lead to the structures bending and kinking which detract from their mechanical properties. Numerical simulations employing artificial intelligence and machine learning will facilitate the rapid exploration of high-dimensional processing space associated with nanotube formation and will guide the experimental aspects of this project. The results of this work and the understanding of the synthesis process will help control the mechanisms of carbon nanotube interactions during their synthesis, leading to enhanced and engineered carbon nanotube forest properties. Middle school students will be engaged with the research through a one-week summer camp in which hands-on nanoscale materials engineering will be merged with artistic expression through collaboration with the University of Missouri Museum of Art and Archeology. The results of these investigations will enable the use of the art and observation at the nanoscale to engage and inspire grade school children in STEM-based learning in cooperation with the university art museum.The forces and mechanisms that drive carbon nanotube forest self-assembly are currently poorly understood, in part because in-situ diagnostic techniques with sufficient resolution to interrogate the evolving mechanical interactions are lagging. This project will implement direct visualization of carbon nanotube forest growth and assembly using in-situ synthesis methods within scanning and transmission electron microscopes. A variety of synthesis conditions will be investigated to link the role of important processing factors to the observed behaviors. Transmission electron microscope techniques will facilitate not only time-resolved visual inspection of individual nanotubes and their host catalyst nanoparticles but also compositional analyses at or near atomic resolution. Scanning electron microscope techniques will examine coordinated assembly of carbon nanotubes within the larger population at length scale ranging from tens of nanometers of tens of microns. Experimental observations will be input into a time-resolved finite element simulation to interrogate the forces generated during nanotube forest assembly. After synthesis, the mechanical properties of carbon nanotube forests will be measured using in-situ scanning electron microscope compression and simulated with the finite element model for validation. The validated model will serve as a vehicle for rapid assessment of process-structure-property relationships using an artificial intelligence algorithm to autonomously search for appropriate synthesis conditions that satisfy user-defined forest property sets. A carbon nanotube forest synthesis simulation tool will be made publically available at nanohub.org to facilitate broader impact of the simulations developed in this project.