2025 AIChE Annual Meeting

(190h) Scalable Manufacturing of Inorganic Nanoparticles Using an Inert High-Temperature Jet Mixing Reactor

Authors

Nicholas Brunelli, Ohio State University
Typical laboratory batch reactors for inorganic nanoparticles (NPs) synthesis can be difficult to scale since rapid particle nucleation and growth require efficient mixing to produce monodisperse particle size distribution (PSD). This limits their use in commercial applications. Our work explores a milli fluidic jet mixing reactor (JMR) design consisting of an axial flow with two jets impinging on the mainline, resulting in a single stream that exits the reactor that has mixing time of milliseconds. Our lab has successfully studied the synthesis of metal NPs and core@shell NPs like Pd@TiO2 in JMR at room temperature, where the Pd core was synthesized in batch as it requires high temperature for its formation. The JMR has not been investigated at high temperatures previously resulting in a barrier for a fully continuous nanoparticle synthesis. Keeping in mind the direction of economic feasibility, copper (Cu) was used as a model system to study the behavior of the high-temperature reactor. Preliminary results showed Cu NPs synthesized in JMR have a better PSD than the batch process. Particles synthesized in the batch process were more than 100 nm and agglomerated whereas particles synthesized in high temperature JMR were around 11 nm. The shape and size change of these nanoparticles also changed when the synthesis temperature increased from 100°C to 150°C, at 100°C particles are mostly spherical in batch while at 150°C fractals were seen. In JMR spherical particles were seen at a main line flow rate Qmain- 0.8 mL/min and a jet line flow rate of Qjet- 0.4 mL/min whereas nanorods were seen when operated at Qmain- 0.4 mL/min, Qjet- 0.2 mL/min. Higher flow rates enhance mixing efficiency, resulting in more uniform reactant distribution and higher supersaturation. Future work will evaluate the yield of Cu NPs in both batch and JMR to address the scalability issues.