Descripción del proyecto
La mejora de las técnicas analíticas podría revelar fenómenos de transporte de carga en las pilas a escala nanométrica
La innovación en baterías es clave para la evolución de las nuevas técnicas de producción y transporte de energía. Sin embargo, para progresar en este campo, es necesario entender mejor los procesos a escala atómica que tienen lugar en las interfaces e interfases de las pilas. El proyecto OPINCHARGE, financiado con fondos europeos, pretende desarrollar diversas técnicas analíticas operando a escala nanométrica para comprender de forma precisa estos procesos. El consorcio, formado por 10 organizaciones de 7 países, desarrollará diferentes técnicas químicas y físicas pioneras. También se emplearán técnicas de IA/aprendizaje automático para mejorar la adquisición y el análisis de datos. Se hará especial hincapié en el intercambio de datos, un aspecto fundamental de la Plataforma de Aceleración del Genoma de la Interfaz de la Batería y los Materiales (BIG-MAP).
Objetivo
Battery innovation has played a major role in the development of new energy production & transport technologies, becoming true enablers of a clean, affordable and secure energy economy. However, innovation is currently being hindered by the lack of understanding of the processes happening at atomic levels in the batteries' interfaces and interphases. Thus, the OPINCHARGE consortium aims to develop a set of effective operando nanoanalytical techniques and methodologies to understand the interfacial processes in batteries in unprecedented level of detail.
For this, 10 organizations from 7 different countries will work together, combining their expertise and infrastructure, to find new ways of addressing this challenge. This will be fostered through 3 main pillars of technique innovation: chemical-based, isotope-based, and physics-based. Consequently, main techniques to be addressed operando will be: X-ray scattering, enhanced Raman, STEM-EELS & EDX, FIB-SIMS, Neutron imaging, OEMS and NMR.
Parallelly, the consortium will integrate AI/Machine Learning support, in order to improve data acquisition and analysis, making the data crunching processes more efficient and meaningful. Likewise, data treatment and sharing are cornerstones of the project, as Open Science practices and scientific collaboration with the community are recognized by the consortium as key aspects of the BIG-MAP objectives of the Batteries partnership and 2030+ programmes.
With a 36 month duration, the project is divided in 7 work packages distributed among the partners according to their expertise, with LIST as leader of the consortium. Dissemination, exploitation and communication activities will allow to maximize the impact of the results and the outreach of these, by actively promoting the diffusion of the information derived from the activities of the project.
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HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinador
4362 Esch Sur Alzette
Luxemburgo