Open AccessCombining 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
Author(s) -
Aakash Varambhia,
Angela E. Goode,
Ritsuko Sato,
Trung Dung Tran,
Alissa Stratulat,
Markus Boese,
Gareth D. Hatton,
Doğan Özkaya
Publication year - 2022
Publication title -
johnson matthey technology review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.571
H-Index - 49
ISSN - 2056-5135
DOI - 10.1595/205651322x16508983994949
Subject(s) - pipeline (software) , gasoline , particulates , filter (signal processing) , nanotechnology , focused ion beam , diesel particulate filter , materials science , grid , process engineering , engineering , mechanical engineering , chemistry , ion , electrical engineering , waste management , geometry , mathematics , organic chemistry
The performance of a particulate filter is determined by multi-scale properties that span the macro, meso and atomic scale. Traditionally, the primary role of a GPF is to reduce solid particles and liquid droplets. At the macroscale, transport of gas through a filter’s channels and interconnecting pores act as main transport arteries for catalytically active sites. At the mesoscale, the micropore structure is important for ensuring that there are enough active sites that are accessible for the gas to reach the catalyst nanoparticles. Whereas 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 used as an input for gas flow simulations and better product design.