Flame spray pyrolysis (FSP) is an advanced technology to synthesize metal oxide powders with a wide range of chemical compositions and physical properties. Because it is readily scalable, FSP has significant applications in powder production for photocatalysts, gas sensors, batteries, solar cells, and fuel cells. While the production of metal oxide nanocrystalline materials has been demonstrated using FSP, it is currently unknown how to control the process to make the chemically pure materials needed for the applications described. Because purely experimental approaches to optimizing the FPS are costly and time-intensive, a new modeling framework for FSP reactors will be developed in this research program to predict how powder particles form and grow in flame reactors. To describe this complex process, the model must integrate elements corresponding to the decomposition chemistry of metal-containing feed components, fuel pyrolysis and oxidation, and aerosol particle nucleation and growth dynamics. The outcome of this combined modeling and experimental program will be a computational tool that can predict the chemical composition, size distribution, and crystal phase of the nanocrystals as the numerous FSP processing parameters are changed. From a fundamental perspective, this project will promote progress in aerosol science and advance the field of chemical reaction engineering. From the application viewpoint, this project will enable computer-based design of FSP reactors capable of high-precision control and significantly reduced fuel costs for nanoparticle manufacturing operations. From an educational perspective, the research team will incorporate the outcomes of this project into their course content, help train the next generation STEM workforce, and continue to include more undergraduates and diversified/under-represented groups into frontier research.
By leveraging their past success in spray combustion and soot/aerosol modeling, the research team proposes a new modeling framework, with supporting experimental measurements, that will enable model-based design and optimization of FSP reactors for perovskite oxide synthesis. The research project will focus on the synthesis of Lanthanum doped Barium Titanate (La:BaTiO3) by FSP using ethylhexanoate-derived metal precursors and ethylhexanoic acid as a fuel. Experiments to measure the atomic percentages of La, Ba, Ti, and O in mobility-size selected nanocrystals, and the percentages of BaTiO3, BaO, and TiO2 (along with phase information) in the synthesis of La:BaTiO3, will be used to provide validation data for the proposed model. Detailed chemical reaction mechanisms for precursor decomposition, fuel oxidation and pyrolysis, nanoparticle nucleation and surface reactions for multiple metal salt precursors will be identified using a global reaction pathway selection analysis tool developed by the project team. Likewise, a dynamic adaptive chemistry technique will be used as a model reduction method enabling simulations of complex reaction systems using practical levels of computational resources. Finally, a 7-dimensional hybrid sectional-moment model will be developed to track the fractions of BaO, TiO2, and BaTiO3, particle size, and crystallite size specifically to answer whether the nanoparticle composition changes during growth. When complete, the modeling strategy will be broadly applicable to flame pyrolysis systems for a wide range of industrial-scale particle manufacturing applications.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
