Descripción del proyecto
Diseño de materiales para pilas de combustible de óxido sólido con aprendizaje automático
Las pilas de combustible de óxido sólido de película delgada a microescala (μSOFC, por sus siglas en inglés) son una tecnología futura prometedora para aplicaciones energéticas portátiles. Esta alternativa emergente cuenta con una alta eficiencia, flexibilidad de combustible y elevadas densidades de potencia. Las propiedades mejoradas de transporte de oxígeno y la resistencia a las altas temperaturas de funcionamiento hacen que los materiales de óxido de lantano sean adecuados para su uso en los cátodos de este tipo de pilas de combustible. En el proyecto SmartOptoelectronics, financiado por las Acciones Marie Skłodowska-Curie, se probarán métodos de aprendizaje automático para estudiar la relación entre las propiedades estructurales y el rendimiento electroquímico de los óxidos de lantano de tipo perovskita con datos experimentales de alto rendimiento. Sus investigadores examinarán el espacio químico de los óxidos de lantano para así diseñar materiales con un mejor rendimiento para su uso en las μSOFC.
Objetivo
Microscale thin-film-based solid oxide fuel cells (μSOFCs) are an emerging alternative for portable power supply due to their high efficiency, fuel flexibility and high volumetric and specific power densities. Promising cathode materials for μSOFCs are perovskite lanthanum-based oxide materials which have improved oxygen transport properties and resistance to the high operating temperatures. However, the physicochemical factors influencing the performance of these materials are yet to be well understood. The SmartOptoelectronics project will develop machine learning (ML) methods to establish trends between the structural properties and the electrochemical performance of perovskite lanthanum-based oxides based on high-throughput experimental data. These techniques will be used to explore the chemical space of lanthanum-based oxides with the goal of undestanding and designing lanthanum-based materials with enhanced performances for μSOFC applications. Machine learning methods will be validated in three main steps: (1) deriving structure-property relationships in lanthanum-based oxides from spectroelectrochemical data of combinatorial ternary and quaternary maps; (2) demonstrating new lanthanum-based oxides with enhanced electrochemical properties and performance; (3) optimising the operation of devices based on top-performing materials with operando monitoring of spectroelectrochemical properties. The project will have a high impact on the work programme and on the candidate’s skills and future prospects by developping an expertise in machine learning and large scale clean energy conversion devices, which are Key Enabling Technologies in Horizon Europe and complement her background in spectroelectrochmistry of multi-redox catalytic materials. The project will also re-enforcing the candidate’s transferrable skills and technology transfer competence as part of the KIC Innoenergy community and the clean energy R&D&I sector.
Ámbito científico
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energy
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- engineering and technologyenvironmental engineeringenergy and fuelsfuel cells
- engineering and technologyenvironmental engineeringenergy and fuelsenergy conversion
Palabras clave
Programa(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régimen de financiación
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global FellowshipsCoordinador
08930 Sant Adria De Besos
España