Argonne National Laboratory,
Keywords: nanomaterials manufacturing, continuous flow synthesis, in-situ characterization, machine learning algorithm
Summary:Although nanoscale materials and structures are common in nature they only recently advanced to industry and are slowly becoming commercialized. There is a lot of exciting research results coming out of research labs in recent years but the hard reality is that nanotechnology products are still rarely incorporated in existing applications. The critical bottleneck in harvesting the benefits of advanced nanomaterials is their low availability. Industry still struggles to translate discovery lab inventions into tangible technological applications as it is widely regarded that nanomaterials are notoriously difficult to scale up to a commercial manufacturing level. Large scale production in batch mode usually means lower material quality and batch-to-batch variability. Argonne’s Material Engineering Research Facility is developing science-based reliable and reproducible manufacturing protocols for nanomaterials in continuous flow mode. This multidisciplinary project is aimed to investigate and better understand the fundamental processes that govern nucleation and particle growth in a continuous flow reactor system; phenomena that directly influence the resulting particle morphology and desired property. Continuous flow microfluidic reactor is uniquely suited for nanomaterial synthesis. Particle size and morphology is controlled via temperature, residence time, process chemistry and precursor concentration. All these parameters can be easily and reproducibly controlled over long period of time due to the superior and predictable mass and heat transfer capabilities of a microfluidic reactor. The quantity of materials produced in continuous flow mode depends only on time the process is run contrary to reactor’s volume in batch mode, the key difference to eliminate batch-to-batch variability. Continuous flow syntheses present multiple benefits over the traditional batch approaches; excellent homogeneity leads to narrow particle size distribution, fully automated computer controlled system expedites nanoparticle synthesis optimization, processes can be run in condition difficult or impossible to otherwise achieve, and scalable architecture provide the tools for seamless moving from benchtop process optimization to production. Continuous flow reactions represent a new paradigm not only with respect to materials synthesis, but for time-resolved studies of the reaction mechanism. Instead of monitoring reaction progress as a function of time, in continuous flow reactions reaction progress is monitored as a function of distance along the flow path. The resolution with which the reaction is probed is largely independent of the kinetics of the reaction or the time-resolution of the measurement and instead depends on spatial resolution of the probe along the beam path. The project is extensively utilizing Argonne’s Advanced Photon Source in-situ material characterization capability and sophisticated machine-learning computational methods to develop a deep knowledge of the relationship between process parameters, nanoparticle morphology, physicochemical properties and material performance. While the initial focus of this effort is mostly on core/shell bi-metallic nanocatalyst for fuel cell application, once developed, the ability to precisely control process parameters and to characterize flow reaction in-situ using advanced X-ray tools combined with computational method in a learning loop will allow for a quick optimization of manufacturing procedures that can be readily extended to other material systems.