Combining State of the Art Open Source and Proprietary Machine Learning Technologies to Build a Data Analysis Pipeline for Gasoline Particulate Filters using X-Ray Microscopy, Focused Ion Beam-Scanning Electron Microscopy and Transmission Electron Microscopy

Abstract

The performance of a particulate filter is determined by properties that span the macro, meso and atomic scales. Traditionally, the primary role of a gasoline particulate filter (GPF) is to reduce solid particles and liquid droplets. At the macro scale, transport of gas through a filter’s channels and interconnecting pores act as main transport arteries for catalytically active sites. At the meso scale, the micropore structure is important for ensuring that enough active sites are accessible for the gas to reach the catalyst nanoparticles. At the atomic scale, the structure of the catalyst material determines the performance and selectivity within the filter. Understanding all length scales requires a correlative approach but this is often quite difficult to achieve due to the number of software packages a scientist has to deal with. We demonstrate how current state-of-the-art approaches in the field can be combined into a streamlined pipeline to characterise particulate filters by digitally reconstructing the sample, analysing it at high throughput, and eventually use the result as an input for gas flow simulations and better product design.