(26c) Application of a Continuous Flow Millifluidic Reactor Towards Optimizing Manufacturing Throughput for Molybdenum Carbide Nanoparticles

Transition Metal Carbides (TMCs) has been extensively investigated for their use for multiple catalytic transformations, including CO₂ hydrogenation, hydrodeoxygenation and isomerization reactions. Due to the harsh conditions in which most TMCs are prepared, much effort has been focused on discovering new synthesis methodologies that yield nanostructured products with high surface area and enhanced activity which can be leveraged for catalysis. Recently, a scalable solution-phase synthesis of phase pure α-MoC_1-x nanoparticles by thermolytic decomposition of Mo(CO)₆ at mild reaction conditions and their superior catalytic properties was been demonstrated and adapted to continuous flow production in a millifluidic reactor. As well as providing a means to produce nanoparticles on an industrial scale, continuous flow platforms coupled with statistical Design of Experiment (DoE) methods can provide a powerful method for understanding the reaction parameter space for this synthetic route. This approach facilitates the rapid and efficient optimization of the reaction, with the target of maximizing throughput for scaling production. The heat and mass transport characteristics provided by millifluidic flow reactors allows for efficient control over reagents and parameters. This is essential for maximizing the throughput of nanoparticle products obtained from millifluidic reactors for scaling up nanofabrication. In this work, we demonstrate the use of statistical DoE in tandem with response surface methodology (RSM) for a parametric screening analysis to optimize the throughput of a molybdenum carbide nanoparticle synthesis utilizing a millifluidic flow reactor. A 2⁴ full factorial design was implemented to evaluate four input variables (reaction temperature, flow rate, percentage of oleylamine, and precursor concentration) that carry statistically significant effects on three responses (throughout, residence time, and yield). RSM was then used to optimize the system based on the input goal of maximizing throughput. The three significant input variables identified by the screening were evaluated using a Doehlert matrix in order to investigate each significant variable at a higher number of levels based on their significance in effecting the throughput. Preliminary results show an optimized throughput of which translates to an increase by a factor of ~2.84 compared to the previously reported value (0.775 g hr^-1). This DoE screening analysis and throughput optimization of MoC_1-x synthesis opens the door to an increased feasibility for industrial scale-up.