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.
