Robust optimization of nanoparticle synthesis in a supercritical CO2 process for energy applications

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

0933430 Grover Systematic methods are needed to quantify uncertainty in nanomanufacturing processes, and subsequently to design processes that are robust to these uncertainties. Nanoscale phenomena present a new challenge for manufacturing, due to the inherent stochastic dynamics, in addition to sensitivities to macroscopic process inputs like temperature and pressure. However, processes are currently being developed to take advantage of the new discoveries and advancements in nanoscience, and cost-effective engineering approaches and tools are needed to more efficiently explore the design space to develop nanotechnology-enabled products. In this work the PIs focus on the synthesis of metal nanoparticles, which are used in a wide range of applications from energy to medicine. For example, nanoparticles of controlled size and size distribution are needed to create high performance catalysts for NOx treatment in diesel engines, which produce lower CO2 emissions relative to gasoline engines. However, developing a high-throughput manufacturing process to create durable supported catalysts in a cost-effective manner has been elusive, in part due to design tradeoffs like higher performance but lower durability at smaller nanoparticle size. Moreover, significant variability exists both within a single batch of nanoparticles, due to the inherent distribution of particle nucleation times, and also between batches, due to drift in operating conditions and noise variables. This project is a comprehensive methodology for robust optimization of a batch process, using various sources of information integrated by a rigorous Bayesian method. First, mechanistic models of mean process behaviors, as is common in the engineering disciplines, will be developed. Since models of nanoscale phenomena are typically not accurate within manufacturing tolerances, mechanistic models will be supplemented with stochastic components linking within- and between-batch variations to controllable process parameters and noise variables for robust process design. Expert opinions help model trends and expected variance for upgrading the models into a stochastic-mechanistic simulation tool. The simulated data generated will be used to build a statistical-mechanistic model, which is less complex than the simulation model, suitable for efficient exploration of process recipes. Then, physical data will be collected based on optimal experimental design plans developed to validate and improve the statistical-mechanistic model. Finally, the refined model will be used to cost-effectively search for the optimized process recipe, to achieve the desired nanoparticle size with a narrow size distribution while minimizing batch-to-batch variation. Intellectual merit. The current disconnect between the fields of robust design in statistics and mechanistic modeling in engineering will be bridged by this methodology. Incorporating all sources of information on mean behavior and variance requires domain-specific knowledge and mechanistic understanding. This modeling approach for mean and variance of process variables is required to derive the recipe for a robust optimal process. Broader impact. The PI team is uniquely equipped to develop this new methodology for robust process optimization. They combine expertise in experiments, mechanistic modeling, process control, and experimental design, along with our close collaborations with industry. The diverse team of faculty and students (graduate, undergraduate, and high school) who will participate in the project will gain experience and insight that will allow them to work in interdisciplinary nanomanufacturing environments.