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High-throughput multiscale characterization and artificial intelligence to develop new sustainable electrode materials for water electrolysis
Goals
Improving the performance of alkaline electrolyzers through the development of high-performance heterostructured electrodes
Innovations
The traditional approach to improving electrocatalysts involves trial and error. Here, the innovative approach is to use semi-automated multi-scale characterization platforms to generate large amounts of data that will feed machine learning algorithms to accelerate the discovery of more active and sustainable materials for water electrolysis.
PhD student:
Anton Voronkin
Promoter
Prof. Jon Ustarroz | Université Libre de Bruxelles |
Co-promoter
Prof. Nathalie Job | Université de Liège |
Research Center
Dr. Jean François Vanhumbeeck | Centre de Recherches Métallurgiques |
Tasks
T02-1 | Development of catalytic particles for integration into heterostructured catalytic coatings |
T02-2 | Elaboration of wet and dry heterostructured catalytic coatings |
T02-3 | High-throughput physico-chemical and functional characterization of synthesized coatings |
T02-4 | Artificial Intelligence algorithm development for semi-automatic processing of generated data |
Anton Voronkin presents his project during the Kick-off - September 13, 2024.
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