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SmartOptoelectronics

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Machine-learning-guided design of perovskite lanthanum oxide cathodes for solid...

Machine-learning-guided design of perovskite lanthanum oxide cathodes for solid oxide fuel cells 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 ma... ver más

31/05/2024

FUNDACIÓ PRIVADA I...

184K€

Presupuesto del proyecto: 184K€

Líder del proyecto

FUNDACIÓ PRIVADA INSTITUT DE RECERCA DE L'ENE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5 | 35M€

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Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-05-31

HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2021 ExpectedOutcome:

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3 Participantes

FUNDACIÓ PRIVADA INSTITUT DE...

183.53K€ | Lider

THE GOVERNING COUNCIL OF THE...

UNIVERSITY OF TORONTO

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Duración del proyecto: 24 meses Fecha Inicio: 2022-05-18
Fecha Fin: 2024-05-31

Líder del proyecto

FUNDACIÓ PRIVADA INSTITUT DE RECERCA DE L'ENE... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5 | 35M€

Presupuesto del proyecto 184K€

Descripción del proyecto 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.

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