论文标题
朝在线转向火焰喷雾热解纳米颗粒合成
Towards Online Steering of Flame Spray Pyrolysis Nanoparticle Synthesis
论文作者
论文摘要
火焰喷雾热解（FSP）是一种制造技术，可用于大规模生产工程纳米颗粒，用于催化，能源材料，复合材料等。 FSP仪器高度取决于许多可调节的参数，包括燃油喷射速率，燃料氧混合物和温度，这可能会极大地影响屈服纳米颗粒的质量，数量和特性。优化FSP合成需要监视，分析，表征和修改实验条件。此外，我们提出了表征未燃烧溶液的体积分布的高斯（DOG）方法的混合CPU-GPU差异，以便启用近实时的时间优化和FSP实验的转向。与标准实现的比较表明，我们的方法是更有效的数量级。该替代信号可以作为在线端到端管道的组成部分，从而最大化合成产量。
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.
