EAGER: Reaction Engineering for Flame Spray Pyrolysis of Perovskite Oxide Nanocrystals

火焰喷雾热解(FSP)是一种合成多种化学成分和物理性能的金属氧化物粉末的先进技术。由于FSP易于扩展，因此在光催化剂、气体传感器、电池、太阳能电池和燃料电池的粉末生产中具有重要应用。虽然已经证明了使用FSP生产金属氧化物纳米晶材料，但目前还不知道如何控制这一过程，以制造所述应用所需的化学纯材料。由于优化FSP的纯实验方法昂贵且时间密集，本研究计划将开发一种新的FSP反应器建模框架，以预测粉末颗粒如何在火焰反应器中形成和生长。为了描述这一复杂的过程，模型必须集成与含金属原料组分的分解化学、燃料热解和氧化以及气溶胶粒子成核和生长动力学相对应的元素。这个模拟和实验相结合的程序的结果将是一个计算工具，可以预测随着众多FSP工艺参数的变化，纳米晶的化学成分、尺寸分布和晶相。从根本上讲，该项目将促进气溶胶科学的进步，推动化学反应工程领域的发展。从应用的角度来看，该项目将使FSP反应堆能够进行基于计算机的设计，能够进行高精度的控制，并显著降低纳米颗粒制造操作的燃料成本。从教育的角度来看，研究团队将把这一项目的成果纳入他们的课程内容，帮助培训新一代STEM劳动力，并继续将更多的本科生和多样化/代表性不足的群体纳入前沿研究。通过利用他们过去在喷雾燃烧和烟尘/气溶胶建模方面的成功，研究团队提出了一个新的建模框架，并支持实验测量，该框架将使基于模型的钙钛矿氧化物合成FSP反应器的设计和优化成为可能。该研究项目将集中在以乙基己酸乙酯衍生的金属前驱体和乙基己酸为燃料，用FSP法合成掺镧钛酸钡(La：BaTiO3)。实验将被用来测量迁移率大小的选定纳米晶体中La、Ba、Ti和O的原子百分比，以及合成La：BaTiO3中BaTiO3、BaO和TiO2%的百分比(以及相信息)，以提供所提出的模型的验证数据。利用项目组开发的全球反应路径选择分析工具，将确定多种金属盐前体的前体分解、燃料氧化和热解、纳米颗粒成核和表面反应的详细化学反应机理。同样，动态自适应化学技术将被用作一种模型简化方法，使之能够使用实际水平的计算资源模拟复杂的反应系统。最后，将开发一个7维混合截面矩模型来具体跟踪BaO、TiO2和BaTiO_3的分数、颗粒尺寸和微晶尺寸，以回答纳米颗粒成分在生长过程中是否发生变化。完成后，建模策略将广泛适用于用于广泛工业规模颗粒制造应用的火焰热解系统。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.