Understanding and Monitoring Nanoparticle Self-Assembly Processes with Online Transmission Electron Microscopic Data

该奖项的目标是开发一种统计方法，通过在线测量纳米颗粒生产过程来了解纳米颗粒生产过程的动态方面，并在了解过程的基础上研究实现高质量纳米颗粒生产的质量技术。由于其大规模生产的潜力，主要的兴趣是纳米粒子自组装，通过它们的动态相互作用组装纳米粒子。所开发的方法表征并使用动态相互作用来监测自组装的质量。从纳米粒子自组装过程的噪声在线测量中，提出了一个自动跟踪单个纳米粒子及其相互作用的优化问题，并开发了一个计算效率高的次优算法来解决这个问题。跟踪的粒子相互作用符合所提出的用于估计未知过程参数的统计物理模型。为了填补基于物理的模型的空白，还开发了几个经验模型。PI将开发一种新的过程监测方法，该方法使用估计的过程参数值来测试纳米粒子自组装过程是否处于正常状态，并在可能的情况下定位过程异常的特殊原因。银纳米粒子自组装作为一个原型过程来验证所开发的方法。如果成功，这项工作将推进纳米颗粒生产过程的在线过程表征和监测，从而导致高质量的纳米颗粒生产。此外，开发的统计物理模型将导致对纳米颗粒中动态纳米级现象的基本理解，这将有助于扩展当前纳米颗粒物理和化学的研究。这项工作的结果将通过出版物和演讲传播，并将通过新课程、学生指导和K-12学生研讨会计划，为熟练的纳米工艺工程师提供教育机会，以缩小劳动力缺口。

The objective of this award is to develop a statistical methodology for understanding the dynamic aspects of nanoparticle production processes by using online measurements of the processes, and to study quality technology for achieving high-quality production of nanoparticles based on understanding the processes. Due to its potential for large-scale production, the main interest is in nanoparticle self-assembly, which assembles nanoparticles via their dynamic interactions. The developed methodology characterizes and uses the dynamic interactions for quality monitoring of the self-assembly. An optimization problem is formulated for automatically tracking individual nanoparticles and their interactions from the noisy online measurements of their self-assembly processes, and a computationally efficient sub-optimal algorithm is developed to solve the problem. The tracked particle interactions are fit to the proposed statistical-physical model for estimating unknown process parameters in an underlying physics-based model. Several empirical models are also developed for filling the void in the physics-based model. The PI will develop a new process monitoring method, which uses the values of the estimated process parameters, to test whether a nanoparticle self-assembly process is in normal condition and to locate special causes of process anomalies if available. A silver nanoparticle self-assembly is used as a prototypical process to validate the developed methodology.If successful, this work will advance online process characterization and monitoring for nanoparticle production processes, thereby leading to high quality production of nanoparticles. In addition, the developed statistical-physical model will lead to a fundamental understanding of dynamic nanoscale phenomena in nanoparticles, which will help to expand the current research into nanoparticle physics and chemistry. The results of this work will be disseminated via publications and presentations, and will be used to provide education opportunities for closing the workforce deficit with skilled nano-process engineers through new courses, student mentoring, and workshop programs for K-12 students.