  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, so as 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.
