| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Banko, Lars
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (26/26 displayed)
- 2024From quinary Co–Cu–Mo–Pd–Re materials libraries to gas diffusion electrodes for Alkaline hydrogen evolutioncitations
- 2023Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurementscitations
- 2023Oxidative depolymerisation of kraft lignincitations
- 2023Microscale combinatorial libraries for the discovery of high-entropy materialscitations
- 2022Composition and electrical resistance results of a Ir-Pd-Pt-Rh-Ru composition spread thin film materials library
- 2022Zooming‐in - visualization of active site heterogeneity in high entropy alloy electrocatalysts using scanning electrochemical cell microscopycitations
- 2022Unusual phase formation in reactively sputter‐deposited La-Co-O thin‐film librariescitations
- 2022Computationally accelerated experimental materials characterizationcitations
- 2022Unravelling composition-activity-stability trends in high entropy alloy electrocatalysts by using a data‐guided combinatorial synthesis strategy and computational modelingcitations
- 2021Deep learning for visualization and novelty detection in large X-ray diffraction datasetscitations
- 2021Bayesian optimization of high‐entropy alloy compositions for electrocatalytic oxygen reductioncitations
- 2020Structure Zone Investigation of Multiple Principle Element Alloy Thin Films as Optimization for Nanoindentation Measurementscitations
- 2020Influences of Cr content on the phase transformation properties and stress change in V-Cr-O thin-film librariescitations
- 2020Fast-track to research data management in experimental material science - setting the ground for research group level materials digitalizationcitations
- 2020Predicting structure zone diagrams for thin film synthesis by generative machine learningcitations
- 2020Comparative study of the residual stress development in HMDSN-based organosilicon and silicon oxide coatingscitations
- 2020Structure zone investigation of multiple principle element alloy thin films as optimization for nanoindentation measurements
- 2020Crystallography companion agent for high-throughput materials discovery
- 2020High-throughput characterization of (Fe<sub><i>x</i></sub>Co<sub>1–<i>x</i></sub>)<sub>3</sub>O<sub>4</sub> thin-film composition spreadscitations
- 2019Mastering processing-microstructure complexity through the prediction of thin film structure zone diagrams by generative machine learning models
- 2019Mastering processing-microstructure complexity through the prediction of thin film structure zone diagrams by generative machine learning models
- 2019Effects of the Ion to growth flux ratio on the constitution and mechanical properties of Cr1–x-Alx-N thin filmscitations
- 2019Ion energy control via the electrical asymmetry effect to tune coating properties in reactive radio frequency sputteringcitations
- 2019Ion energy control via the electrical asymmetry effect to tune coating properties in reactive radio frequency sputteringcitations
- 2018Improved homogeneity of plasma and coating properties using a lance matrix gas distribution in MW-PECVDcitations
- 2018PEALD of SiO2 and Al2O3 thin films on polypropylenecitations
Places of action
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article
Unravelling composition-activity-stability trends in high entropy alloy electrocatalysts by using a data‐guided combinatorial synthesis strategy and computational modeling
Abstract
High entropy alloys (HEA) comprise a huge search space for new electrocatalysts. Next to element combinations, the optimization of the chemical composition is essential for tuning HEA to specific catalytic processes. Simulations of electrocatalytic activity can guide experimental efforts. Yet, the currently available underlying model assumptions do not necessarily align with experimental evidence. To study deviations of theoretical models and experimental data requires statistically relevant datasets. Here, a combinatorial strategy for acquiring large experimental datasets of multi-dimensional composition spaces is presented. Ru–Rh–Pd–Ir–Pt is studied as an exemplary, highly relevant HEA system. Systematic comparison with computed electrochemical activity enables the study of deviations from theoretical model assumptions for compositionally complex solid solutions in the experiment. The results suggest that the experimentally obtained distribution of surface atoms deviates from the ideal distribution of atoms in the model. Leveraging both advanced simulation and large experimental data enables the estimation of electrocatalytic activity and solid-solution stability trends in the 5D composition space of the HEA system. A perspective on future directions for the development of active and stable HEA catalysts is outlined.