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

NICKEFFECT Project at SEMICON Europa 2025

The NICKEFFECT project, represented by partner Singulus Technologies AG, successfully exhibited its cutting-edge advancements at SEMICON Europa 2025 in Munich, focusing on green and sustainable data storage. Aligned with the event’s theme of European economic resilience, the exhibition highlighted the latest results on Platinum (Pt)-free, non-volatile MRAM stacks, a key development in reducing reliance on critical raw materials. Product Manager Matthias Landmann shared these innovative process developments at the Singulus stand, underscoring the project's commitment to enabling a technologically advanced and environmentally sustainable future for the microelectronics industry.

Doctoral Study Demonstrates Pt-Free Approaches for Future Low-Power Magnetic Devices

A significant breakthrough from the NICKEFFECT consortium, led by researcher Aitor Arredondo of the UAB, demonstrates a promising path toward future low-power magnetic devices by eliminating the need for expensive and scarce Platinum Group Metals (PGMs). His doctoral thesis, "Oxygen Magneto-Ionic Effects in NiCo-Based Structures," focuses on magneto-ionics, a novel technique that uses small voltages to move ions—like oxygen—within a material, thereby controlling its magnetic behavior without high electric currents. This shift to Pt-free Ni/Co multilayers offers a dramatic reduction in energy consumption for devices like next-generation MRAM, aligning with Europe's goal to reduce reliance on critical raw materials and champion energy-efficient technologies for data-intensive applications such as AI and neuromorphic computing.

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.

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