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.