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NICKEFFECT aims to develop novel ferromagnetic Ni-based coating materials to replace the scarce and costly Platinum and ensure high efficiency in key applications.

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Since the NICKEFFECT projects aims at replacing platinum catalyst based PEM Fuel Cell (FC) cathodes by porous graphite electrodes that are covered with nickel alloy (nano-)particles, the plating process for creating these nickel nuclei populations  must be properly understood and controlled.   Same as for Water Electrolysis (WE) cells, the use of porous electrodes allows creating a high surface to volume ratio.  The throwing power of a full coverage nickel alloy plating process into a porous structure as for example a carbon fiber cloth is limited (WE cathodes), and the same holds for a nucleation plating process into a porous carbon structure (FC cathodes).  But whereas the main specifications in case of a full coverage nickel alloy plating process for WE cathodes involve primarily the local plating layer thickness and alloy content, for FC cathodes practically all local characteristics of the nuclei population are of importance, involving nuclei density, nuclei size...

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The NICKEFFECT project participated in the Artificial Intelligence for Advanced Materials Conference (AI4AM 2024), held from July 2nd to 4th in the city of Barcelona, Spain. This cross-disciplinary international event brought together top experts from industry and research institutions who utilise Artificial Intelligence (AI) to advance discoveries in materials science. The conference's main goal is to refine automated designs for both structural and electronic material models in engineering, focusing on improving interoperability among material databases and enabling reverse material engineering.   Konrad Eiler, from NICKEFFECT's project partner Universitat Autònoma de Barcelona, did a presentation on the active learning approach used to accelerate experiments as part of the project. Konrad gave some insights on the practical benefits of the Active Learning methodology in guiding experimentalists to find the best conditions for growing Ni-W films for catalysis.     Active Learning in Materials Science   Konrad's presentation highlighted how AI, particularly Active Learning, significantly aids experimentalists in determining optimal...

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