Argonne National Laboratory, Data Science and Learning Division, United States of America
Flame Spray Pyrolysis (FSP) is a manufacturing technique to mass produce engineered nanoparticles for applications in catalysis, energy materials, composites and more. FSP instruments are highly dependent on a number of adjustable parameters, including fuel injection rate, fuel-oxygen mixtures and temperature, which can greatly affect the quality, quantity and properties of the yielded nanoparticles. Optimizing FSP synthesis requires monitoring, analyzing, characterizing and modifying experimental conditions. Here, we propose a hybrid CPU-GPU Difference of Gaussians (DoG) method for characterizing the volume distribution of unburnt solution, to enable near-real-time optimization and steering of FSP experiments. Comparisons against standard implementations show our method to be an order of magnitude more efficient. This surrogate signal can be deployed as a component of an online end-to-end pipeline that maximizes the synthesis yield.