Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis

TL;DR

This paper introduces a hybrid CPU-GPU Difference of Gaussians method for real-time monitoring and optimization of Flame Spray Pyrolysis nanoparticle synthesis, significantly improving efficiency and enabling online process steering.

Contribution

A novel hybrid CPU-GPU DoG approach for real-time nanoparticle synthesis monitoring, facilitating online optimization of FSP parameters.

Findings

Method is an order of magnitude more efficient than standard implementations.

Enables near-real-time analysis for process optimization.

Supports online control of nanoparticle synthesis.

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.