Knowledge-driven fine-tuning of perovskite-based electrode materials for reversible Chemicals-to-Power devices

Digital, Industry & SpaceHORIZON-RIAID: 101091534
EC Contribution
€51,680
Consortium Size
12 orgs
Start Year
2023
Summary

We target a knowledge-based methodological entry to the finding of new generation electrode materials based on perovskites for reversible SOFC/SOEC technologies. The latter are archetypal complex systems: the physico-chemical processes at play involve surface electrochemical reactions, ionic diffusion, charge collection and conduction, which all occur timely within a very limited region. Hence, true in-depth understanding of the key parameters requires characterisation at the right place, at the right time frame and under the proper operating conditions. The price to pay for achieving this multiply-relevant characterisation is the involvement of non-trivial, advanced characterisation techniques. Multi-scale modelling will contribute to turn experimental datasets into a genuine scientific description and make time-saving predictions. In KNOWSKITE-X, the coupling between theoretical and experimental activities is made real by the choice of partners, who are all active in genuinely articulate theory and practice to understand active systems. To provide unifying concepts and to widen the project’s outcomes, intensive collaboration with knowledge discovery using machine-learning and deep learning methods is planned and AI-enabled tools will be used to compensate the smallness of relevant datasets. Such efforts are intended in view of building strong correlations capable of establishing robust composition-structure-activity-performance relations and hence, lead the way to knowledge-based predictions. By doing this, we also target the implementation of simplified testing protocols and tools operable by industrial stakeholders, which results can be augmented thanks to the knowledge-based pivotal correlations implemented during the project. To this end, dedicated efforts will be made in certifying the interoperability and usability of the project’s advances in the form of harmonised documentation and open science sharing.

Consortium (12)

Project Results (13)

Source: CORDIS, the EU research results database.

Publications (13)
Electrospun CoFe<sub>2</sub>O<sub>4</sub> Nanowires Tailored for Magnetoelectrochemistry
ACS Nano· 2025DOI
Zexuan Wang, María F. Navarro Poupard, Ramsundar Rani Mohan, Loukya Boddapati, Jijun Zhang, Saeed Kamali, Chiara Biz, Mauro Fianchini, Francis Leonard Deepak, Jose Gracia, Laura M. Salonen, Yury V. Kolen’ko
Investigating reduction and oxidation of La<sub>0.72</sub>Sr<sub>0.18</sub>Fe<sub>0.9</sub>Ni<sub>0.1</sub>O<sub>3–</sub> <i> <sub>δ</sub> </i> as a Co-free perovskite electrode for symmetrical solid oxide cells
Journal of Physics: Energy· 2025DOI
Siavash M Alizadeh, Pascal Roussel, Mohammad Golmohammad, Valerie Theuns, Duncan Fagg, Oleg I Lebedev, Elise Berrier, Yury V Kolen’Ko
On the Role of Priors in Bayesian Causal Learning
IEEE Transactions on Artificial Intelligence· 2025DOI
Bernhard C. Geiger, Roman Kern
Photothermal activation of methane dry reforming on perovskite-supported Ni-catalysts: Impact of support composition and Ni loading method
Catalysis Today· 2025DOI
Andrea Osti, Simone Costa, Lorenzo Rizzato, Beatrice Senoner, Antonella Glisenti
Workshop on Data Lifecycles in the World of Materials Modelling and Characterisation
EMMC 2025· 2025DOI
Alexandre Ouzia, Geoffrey Daniel, Jesper Friis, Emanuele Ghedini, Peter Imrich, Julian de Marchi, Yoav Nahshon, Franz Martin Rohrhofer, Sophie Schmid, and Alexandra Simperler
Causal Semi-Supervised Learning with Factorized Priors
· 2024DOI
Bernhard C. Geiger, Roman Kern
Enhancing coking resistance of nickel-based catalysts for dry reforming of methane <i>via</i> nitric oxide abatement: a support study
Catalysis Science & Technology· 2024DOI
Beatrice Senoner, Andrea Osti, Antonella Glisenti
Information (Switzerland)
Catalysts· 2024DOI
A. Osti, L. Rizzato, J. Cavazzani, A. Meneghello, A. Glisenti
Machine learning based stellar classification with highly sparse photometry data
Open Research Europe· 2024DOI
Seán Enis Cody, Sebastian Scher, Iain McDonald, Albert Zijlstra, Emma Alexander, Nick Cox
Utilizing Carbonaceous Materials Derived from [BMIM][TCM] Ionic Liquid Precursor: Dual Role as Catalysts for Oxygen Reduction Reaction and Adsorbents for Aromatics and CO2
ChemPlusChem· 2024DOI
Ourania Tzialla, George V. Theodorakopoulos, Konstantinos G. Beltsios, George Pilatos, K. Suresh Kumar Reddy, Chandrasekar Srinivasakannan, Giulia Tuci, Giuliano Giambastiani, Georgios N. Karanikolos, Fotios K. Katsaros, Evangelos Kouvelos, George Em. Rom
Information (Switzerland)
Catalysts· 2023DOI
L. Rizzato, J. Cavazzani, A. Osti, M. Scavini, A. Glisenti
Optimizing Citrate Combustion Synthesis of A-Site-Deficient La,Mn-Based Perovskites: Application for Catalytic CH4 Combustion in Stoichiometric Conditions
Catalysts· 2023DOI
Andrea Osti; Lorenzo Rizzato; Jonathan Cavazzani; Antonella Glisenti
Software in the natural world: A computational approach to hierarchical emergence
Fernando E. Rosas, Bernhard C. Geiger, Andrea I Luppi, Anil K. Seth, Daniel Polani, Michael Gastpar, Pedro A.M. Mediano