Dynamic Data-Driven Monitoring of Nanoparticle Self-Assembly Processes

Abstract

This chapter presents a dynamic, data-driven modeling methodology, capable of tracking and predicting the transient dynamics of nanoparticle selfassembly processes. The proposed methodology is built upon emerging online instrumentation technology, including two different machines of complementing capabilities: a light scattering machine which can operate almost instantaneously at a high temporal resolution but cannot measure nanoparticles at a high spatial resolution, and a transmission electron microscope of a high spatial resolution but a low temporal resolution. To take the full advantage of the multi-resolution instruments, the proposed methodology employs an adaptive data-retrieving strategy of dynamic datadriven application systems, guiding the expensive electron microscope to take measurements only when there is a need to verify the real-time light scattering observations model. The proposed methodology induces a close coupling between modeling and measurement-taking, vesting in a competent strategy that can improve simultaneously both modeling quality and measurement efficiency.