Applications of Continuous Flow Reactors Towards Screening Catalytically Active Nanoparticle Syntheses

Thumbnail Image
Journal Title
Journal ISSN
Volume Title
USC Digital Library
The dissertation presented herein is structured into chapters that delve into various research domains within milli- and microfluidic systems. Part of this dissertation includes collaborative authorship. Chapter 1 introduces the fundamentals of fluid mechanics. In this chapter, some highlights of the important physical phenomena that are dominant in milli- and microscale flow systems are presented, focusing on flow dynamics, diffusion, and computational fluid dynamics simulations. It emphasizes the importance of fluid behavior in microscale systems and introduces a case study on microfluidics applications in biomolecular systems in which a portion of a manuscript I participated in as a third author is presented. Chapter 2 covers applications of continuous flow synthesis of colloidal nanoparticles using milli-and microfluidics systems, highlighting the advantages of miniaturized systems in reaction-based nanoparticle syntheses. Chapter 3 is adapted from a published manuscript in which I am a joint primary author. Chapter 3 describes the use of continuous flow methods for screening the reaction parameters of catalytically active molybdenum carbide nanoparticle synthesis with an emphasis on throughput optimization using a Design of Experiment approach. Chapter 4 introduces machine learning-assisted spectrophotometry, showcasing the integration of machine learning algorithms for the kinetic analysis of ionic liquid-based platinum nanoparticle synthesis. Chapter 5 introduces in-situ characterization for continuous flow reactors with a particular objective of studying the nucleation and growth kinetics of nanoparticle synthesis using X-ray scattering. This chapter provides a critical evaluation of flow reactor designs for in situ X-ray scattering analysis, focusing on the synthesis of ionic liquid-based Pt nanoparticles.
continuous flow, millifluidics, microfluidics, machine learning, nanoparticle synthesis