Skip to content
1887
  1. Home
  2. A-Z Publications
  3. Johnson Matthey Technology Review
  4. Volume 66, Issue 3
  5. Article
Volume 66, Issue 3
  • ISSN: 2056-5135
file format pdf download PDF

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.

© Johnson Matthey
Loading

Article metrics loading...

/content/journals/10.1595/205651322X16508983994949
2022-04-25
2024-11-05
Loading full text...

Full text loading...

/deliver/fulltext/jmtr/66/3/Varambhia_16a_Imp.html?itemId=/content/journals/10.1595/205651322X16508983994949&mimeType=html&fmt=ahah

References

  1. M. Shelef, R. W. McCabe, Catal. Today, 2000, 62, (1), 35 LINK https://doi.org/10.1016/s0920-5861(00)00407-7 [Google Scholar]
  2. L. Tao, E. Garnsey, D. Probert, T. Ridgman, ‘Innovation as Response to Emissions Legislation: Revisiting the Automatic Catalytic Converter at Johnson Matthey’, Centre for Technology Management Working Paper Series No. 2009/3, Institute for Manufacturing, University of Cambridge, Cambridge, UK, 1st February, 2009, 25 pp LINK https://doi.org/10.2139/ssrn.1923084 [Google Scholar]
  3. R. E. Hayes, S. T. Kolaczkowskib, P. K. C. Li, S. Awdry, Appl. Catal. B: Environ., 2000, 25, (2–3), 93 LINK https://doi.org/10.1016/s0926-3373(99)00122-8 [Google Scholar]
  4. Y.-D. Kim, S.-J. Jeong, W.-S. Kim, Environ. Eng. Sci., 2009, 26, (7), 1171 LINK https://doi.org/10.1089/ees.2008.0088 [Google Scholar]
  5. K. Jirátová, J. Balabánová, F. Kovanda, A. Klegová, L. Obalová, R. Fajgar, Catal. Lett., 2017, 147, (6), 1379 LINK https://doi.org/10.1007/s10562-017-2047-z [Google Scholar]
  6. K. Hormann, V. Baranau, D. Hlushkou, A. Höltzel, U. Tallarek, New J. Chem., 2016, 40, (5), 4187 LINK https://doi.org/10.1039/c5nj02814k [Google Scholar]
  7. X. Wang, Z. Zhao, C. Zhang, Q. Li, X. Liang, Catalysts, 2020, 10, (11), 1298 LINK https://doi.org/10.3390/catal10111298 [Google Scholar]
  8. T. Rasheed, M. Bilal, C. Li, F. Nabeel, M. Khalid, H. M. N. Iqbal, J. Photochem. Photobiol. B: Biol., 2018, 181, 44 LINK https://doi.org/10.1016/j.jphotobiol.2018.02.024 [Google Scholar]
  9. L. L. Lavery, J. Gelb, A. P. Merkle, A. Steinbach, Micros. Today, 2014, 22, (3), 16 LINK https://doi.org/10.1017/s155192951400056x [Google Scholar]
  10. J. Goral, I. Miskovic, J. Gelb, M. Andrew, ‘Correlative XRM and FIB-SEM for (Non)Organic Pore Network Modeling in Woodford Shale Rock Matrix’, International Petroleum Technology Conference (IPTC), Doha, Qatar, 6th–9th December, Paper No. IPTC-18477-MS, 2015, OnePetro Physical, Richardson, USA, 2015 LINK https://doi.org/10.2523/iptc-18477-ms [Google Scholar]
  11. F. Schmidt, M. Kühbacher, U. Gross, A. Kyriakopoulos, H. Schubert, R. Zehbe, Ultramicroscopy, 2011, 111, (4), 259 LINK https://doi.org/10.1016/j.ultramic.2010.12.017 [Google Scholar]
  12. G. Kothleitner, M. J. Neish, N. R. Lugg, S. D. Findlay, W. Grogger, F. Hofer, L. J. Allen, Phys. Rev. Lett., 2014, 112, (8), 085501 LINK https://doi.org/10.1103/physrevlett.112.085501 [Google Scholar]
  13. M. Watanabe, M. Kanno, E. Okunishi, JEOL News, 2010, 45, (1), 8 LINK https://admin.jeol.com.cn/admin/static/uploadfiles/20170419/929268fb-cce7-4dc8-a572-271a98f51976.pdf [Google Scholar]
  14. H. Tan, S. Turner, E. Yücelen, J. Verbeeck, G. Van Tendeloo, Phys. Rev. Lett., 2011, 107, (10), 107602 LINK https://doi.org/10.1103/physrevlett.107.107602 [Google Scholar]
  15. A. Choudhury, Arch. Comput. Methods Eng., 2021, 28, (5), 3361 LINK https://doi.org/10.1007/s11831-020-09503-4 [Google Scholar]
  16. P. Iassonov, T. Gebrenegus, M. Tuller, Water Resour. Res., 2009, 45, (9), W09415 LINK https://doi.org/10.1029/2009wr008087 [Google Scholar]
  17. A. Ioannidou, E. Chatzilari, S. Nikolopoulos, I. Kompatsiaris, ACM Comput. Surv., 2018, 50, (2), 20 LINK https://doi.org/10.1145/3042064 [Google Scholar]
  18. M. Čalkovský, E. Müller, M. Meffert, N. Firman, F. Mayer, M. Wegener, D. Gerthsen, Mater. Charact., 2021, 171, 110806 LINK https://doi.org/10.1016/j.matchar.2020.110806 [Google Scholar]
  19. ‘Importing a CZMODEL into ZEN blue or ZEN core’, Carl Zeiss AG, Jena, Germany, GitHub, 20th March, 2020 LINK https://github.com/zeiss-microscopy/OAD/tree/f47a5970fb710ba8066444b8c71f5781755fca64/Machine_Learning#importing-a-czmodel-into-zen-blue-or-zen-core [Google Scholar]
  20. I. Arganda-Carreras, V. Kaynig, C. Rueden, K. W. Eliceiri, J. Schindelin, A. Cardona, H. S. Seung, Bioinformatics, 2017, 33, (15), 2424 LINK https://doi.org/10.1093/bioinformatics/btx180 [Google Scholar]
  21. S. Berg, D. Kutra, T. Kroeger, C. N. Straehle, B. X. Kausler, C. Haubold, M. Schiegg, J. Ales, T. Beier, M. Rudy, K. Eren, J. I. Cervantes, B. Xu, F. Beuttenmueller, A. Wolny, C. Zhang, U. Koethe, F. A. Hamprecht, A. Kreshuk, Nat. Methods, 2019, 16, (12), 1226 LINK https://doi.org/10.1038/s41592-019-0582-9 [Google Scholar]
  22. M. Andrew, B. Hornberger, Microsc. Microanal., 2018, 24, (S2), 118 LINK https://doi.org/10.1017/s1431927618012989 [Google Scholar]
  23. K. Simonyan, A. Zisserman, 10th April, 2015 LINK https://doi.org/10.48550/arXiv.1409.1556
  24. J. Ali, R. Khan, N. Ahmad, I. Maqsood, Int. J. of Comp. Sci. Iss., 2012, 9, (5), 272 LINK http://ijcsi.org/articles/Random-forests-and-decision-trees.php [Google Scholar]
  25. T. Chen, C. Guestrin, ‘XGBoost: A Scalable Tree Boosting System’, in KDD ‘16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August, 2016, pp. 785–794 LINK https://doi.org/10.1145/2939672.2939785 [Google Scholar]
  26. J. Lafferty, A. McCallum, F. C. Pereira, ‘Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data’, ICML 2001, 28th June–1st July, 2001, San Francisco, USA, “Proceedings of the 18th International Conference on machine Learning 2001”, ed. C. E. Brodley, Morgan Kauffman Publishers, Burlington, USA, 2001, pp. 282–298 LINK https://dl.acm.org/doi/abs/10.5555/645530.655813 [Google Scholar]
  27. P. Tahmasebi, F. Javadpour, M. Sahimi, Transp. Porous Media, 2015, 110, (3), 521 LINK https://doi.org/10.1007/s11242-015-0570-1 [Google Scholar]
  28. S. Soyer, ‘czmodel 3.1.0: A Conversion Tool for TensorFlow or ONNX ANNs to CZANN’, Carl Zeiss AG, Jena, Germany, 26th April, 2022 LINK https://pypi.org/project/czmodel/ [Google Scholar]
  29. J. A. Taillon, C. Pellegrinelli, Y.-L. Huang, E. D. Wachsman, L. G. Salamanca-Riba, Ultramicroscopy, 2018, 184, (A), 24 LINK https://doi.org/10.1016/j.ultramic.2017.07.017 [Google Scholar]
  30. G. S. Armatas, Chem. Eng. Sci., 2006, 61, (14), 4662 LINK https://doi.org/10.1016/j.ces.2006.02.036 [Google Scholar]
  31. S. van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, T. Yu, and the scikit-image contributors,, PeerJ, 2014, 2, e453 LINK https://doi.org/10.7717/peerj.453 [Google Scholar]
  32. K. Wu, E. Otoo, A. Shoshani, J. M. Reinhardt, ‘Optimizing Connected Component Labeling Algorithms’, Medical Imaging, 12th–17th February, 2005, San Diego, USA, “Proceedings Medical Imaging 2005: Image Processing”, eds. J. M. Fitzpatrick, 5747, SPIE, Bellingham, USA, 2005, pp. 1965–1976 LINK https://doi.org/10.1117/12.596105 [Google Scholar]
  33. A. Hammoumi, M. Moreaud, E. Jolimaitre, T. Chevalier, A. Novikov, M. Klotz, A. Cherrafi, I. El Hassani, ‘Efficient Pore Network Extraction Method Based on the Distance Transform’, in “Artificial Intelligence and Industrial Applications: Artificial Intelligence Techniques for Cyber-Physical, Digital Twin Systems and Engineering Applications”, eds. T. Masrour, 144, Springer Nature Switzerland AG, Cham, Switzerland, 2021, pp. 1–13 LINK https://doi.org/10.1007/978-3-030-53970-2_1 [Google Scholar]
  34. T. H. Loeber, B. Laegel, S. Wolff, S. Schuff, F. Balle, T. Beck, D. Eifler, J. H. Fitschen, G. Steidl, J. Vac. Sci. Technol. B, 2017, 35, (6), 06GK01 LINK https://doi.org/10.1116/1.4991638 [Google Scholar]
  35. J. Schwartz, Y. Jiang, Y. Wang, A. Aiello, P. Bhattacharya, H. Yuan, Z. Mi, N. Bassim, R. Hovden, Microsc. Microanal., 2019, 25, (3), 705 LINK https://doi.org/10.1017/s1431927619000254 [Google Scholar]
  36. M. B. Clennell, Geol. Soc. London: Spec. Publ., 1997, 122, (1), 299 LINK https://doi.org/10.1144/gsl.sp.1997.122.01.18 [Google Scholar]
  37. P. Moldrup, T. Olesen, T. Komatsu, P. Schjønning, D. E. Rolston, Soil Sci. Soc. Am. J., 2001, 65, (3), 613 LINK https://doi.org/10.2136/sssaj2001.653613x [Google Scholar]
  38. J. Joos, T. Carraro, A. Weber, E. Ivers-Tiffée, J. Power Sources, 2011, 196, (17), 7302 LINK https://doi.org/10.1016/j.jpowsour.2010.10.006 [Google Scholar]
  39. R. Cárdenes, C. Alberola-López, J. Ruiz-Alzola, Image Vis. Comput., 2010, 28, (3), 307 LINK https://doi.org/10.1016/j.imavis.2009.05.013 [Google Scholar]
  40. D. Gostovic, N. J. Vito, K. A. O’Hara, K. S. Jones, E. D. Wachsman, J. Am. Ceram. Soc., 2011, 94, (2), 620 LINK https://doi.org/10.1111/j.1551-2916.2010.04111.x [Google Scholar]
  41. J. Kašpar, R. Di Monte, P. Fornasiero, M. Graziani, H. Bradshaw, C. Norman, Top. Catal., 2001, 16, (1–4), 83 LINK https://doi.org/10.1023/a:1016682831177 [Google Scholar]
  42. R. D. Leapman, P. Rez, D. F. Mayers, J. Chem. Phys., 1980, 72, (2), 1232 LINK https://doi.org/10.1063/1.439184 [Google Scholar]
  43. C. C. Ahn, P. Rez, Ultramicroscopy, 1985, 17, (2), 105 LINK https://doi.org/10.1016/0304-3991(85)90003-8 [Google Scholar]
  44. S. P. Sutera, R. Skalak, Annu. Rev. Fluid Mech., 1993, 25, 1 LINK https://doi.org/10.1146/annurev.fl.25.010193.000245 [Google Scholar]
/content/journals/10.1595/205651322X16508983994949
Loading
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
Johnson Matthey Technology Review 66, 355 (2022); https://doi.org/10.1595/205651322X16508983994949
/content/journals/10.1595/205651322X16508983994949
/content/journals/10.1595/205651322X16508983994949
Loading

Data & Media loading...

  • Article Type: Research Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test