Welcome to
NICKEFFECT

Boosting the research and development of new solutions 
for materials replacing the Platinum Group Metals (PGM)

NICKEFFECT in a Nutshell

NICKEFFECT, a new project co-funded by the European Commission’s Horizon Europe programme, aims to develop novel ferromagnetic Ni-based coating materials to replace the scarce and costly Platinum and ensure high efficiency in key applications.  

Running from June 2022 until June 2026, the NICKEFFECT project is led by a consortium that is a multidisciplinary team comprised of 12 partners from 7 different EU and HEU-associated countries (Belgium, France, Germany, Greece, Ireland, Spain, and the United Kingdom). It covers stakeholders of the whole project value chain: scientific and technology developers, technology providers, end-users, as well as transversal partners.

Project Goals and Objectives

Synthesise ferromagnetic coating materials to replace Platinum as raw material;

Develop measures to ensure that the materials are affordable, durable and with increased corrosion resistance for the different working environments;

Successfully upscale production process in pilot plant to coat real scale components;

Ensure a safe and sustainable by-design approach and define pathways for the recovery, recyclability, purification and re-use of materials at the end of the products life;

Develop a decision support tool to facilitate the adoption of the safe and sustainable criteria when designing and producing metallic coatings free of PGMs;

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Latest News

Uncertainty quantification for small datasets in materials machine learning

For machine learning (ML) in high-tech materials development, where high-quality experimental datasets are typically small (hundreds of samples), developing reliable models requires specialized techniques like Uncertainty Quantification (UQ). UQ focuses on making a model "know what it doesn't know" by requiring it to predict not just the material property, but also its confidence level in that prediction. A common approach to achieve this is ensembling, where multiple models are trained slightly differently, and the standard deviation of their individual predictions is used as the measure of uncertainty. This uncertainty is critical because it allows for strategic post-processing, such as filtering out highly uncertain predictions (a process known as sparsification) to significantly reduce the overall prediction error, thereby enabling high-accuracy predictive modeling of new materials despite the constraints of limited data.

How CAE Improves Ni-Electrode Manufacturing – Webinar with Elsyca

NICKEFFECT latest technical webinar, “How CAE Improves the Manufacturing Process of Ni-based Porous Electrodes”, presented by project partner Elsyca, offered a deep dive into the critical role of Computer-Aided Engineering […]

NICKEFFECT and FreeMe Showcase Innovative Materials Modelling for Sustainability in recent Webinar

The joint “Materials Modelling for Sustainable Materials Development” Webinar, hosted by NICKEFFECT and FreeMe on October 22nd, successfully demonstrated how advanced computational tools are rapidly accelerating the creation of sustainable […]

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