Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis

• URL: http://arxiv.org/abs/2010.08486v1
• Date: Fri, 16 Oct 2020 16:38:16 GMT
• Title: Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis
• Authors: Maksim Levental, Ryan Chard, Joseph A. Libera, Kyle Chard, Aarthi Koripelly, Jakob R. Elias, Marcus Schwarting, Ben Blaiszik, Marius Stan, Santanu Chaudhuri, Ian Foster
• Abstract summary: Flame Spray Pyrolysis (FSP) is a manufacturing technique to mass produce engineered nanoparticles for applications in energy materials, synthesis, 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 yielded nanoparticles.
• Score: 0.5280518172740245
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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