Hello, my name is Vrishank and I’m a materials science and engineering major working on Flame Spray Pyrolysis this summer under Joe Libera, Nikola Ferrier and Jakob Elias, along with Ignacio Gonzales who worked with coding. Flame Spray Pyrolysis is a method of producing nanopowders by burning specific precursor solutions in a continuous flame. While the method holds promise for the scalable and continuous production of nanoparticles, it is possible to optimize the conditions for production in order to fine tune the final products.
The search for sustainable and scalable nanopowder production is of the utmost important in the face of the global energy crisis. Their high surface area-to-volume ratio is the key to optimizing industry processes where FSP products such as LLZO and TEOS find use as catalysts and electrolytes. One advantage of FSP is that it allows for the fine tuning of nanoparticle morphologies and will allow for Argonne to benchmark industry procedures for different compounds and properties.
This summer I worked closely with Joe Libera, understanding the process, trimming data using the in-lab data view program and analyzing the optical emission spectroscopy and Scanning Mobility Particle Sizer data. The main obstacle we encountered was the little resource and literature we had on the correlations between the optical emission spectrum and the product and thus decided the best route for us to go was to develop analytical tools to help deconstruct the FSP OES data.
I’m Ignacio Gonzales, rising junior who is majoring in a Mechanical Engineering and Manufacturing and Design Engineering. Over the summer I worked, analysing data at the Flame Spray Pyrolysis (FSP) project under Jakob Elias. The FSP is a new method of production of nanomaterials that Joe Libera has been developing at the Argonne National Laboratory. The benefits of this method is that it will enable a continuous production of nanomaterials (as opposed to bulk production) and it will costs less than current methods of production. Currently the FSP is able to produce nanomaterials, however these materials are raw; their shape, size and agglomerate structure are not controlled. The project consists in optimizing the conditions, of the FSP in order to be able to produce a final product with full control of the outcome.
I was assigned to the computational side of this project along with David MacCumber. Joe Libera and his team, had been conducting various tests with the FSP using various concentrations both LLZO and TEOS particles. From this Trials, there was a lot of data to work with, including Optical Emission Spectroscopy (OES) and Scanning Mobility Particle Sizer (SMPS) data. I mainly worked on deconstructing the OES data in order to obtain, specific features such as peak, height, width, area, equations for each broadband, etc. To achieve this, I worked closely with Vrishank Menon, and intern in the experimental side, he acted as a bridge between the experimental and computational side of this project and helped us understand the relevance of the data . Additionally I used various toolkits and packages in Python, such as RamPy and SciPy to perform a more accurate analysis . On the future, features will be used to create a Neural Net and for a Machine Learning platform in which using additional data such as a APS X-ray analysis on the nanomaterial, we can predict the properties of the nanomaterial produced.