Descripción del proyecto
El cribado de alto rendimiento perfila los materiales fotovoltaicos orgánicos más prometedores
El mercado fotovoltaico ha avanzado mucho a lo largo de los casi siete decenios transcurridos desde que se hizo la demostración práctica de la primera celda fotovoltaica de silicio. Si bien el silicio ha dominado el mercado durante años, la tecnología fotovoltaica orgánica está ganando terreno como una alternativa prometedora, con muchas ventajas en cuanto a rendimiento, características y procesamiento. Con el apoyo de las Acciones Marie Skłodowska-Curie, el equipo del proyecto IDEAL está afrontando el reto de trasladar esta tecnología prometedora del laboratorio a la práctica. Mediante el uso de aprendizaje automático con datos recopilados de técnicas de procesamiento de alto rendimiento y distintos materiales, el equipo espera definir la mejor solución intermedia en cuanto a eficacia y estabilidad a través de la predicción de los descriptores moleculares más importantes de los materiales candidatos.
Objetivo
The researcher Dr. Sergi Riera-Galindo apply for a fellowship to address effIcient anD stablE orgAnic photovoLtaics (IDEAL) by combinatorial screening. This fellowship will be carried out in the NANOPTO group of Institute of Material Science of Barcelona (ICMAB-CSIC) under the supervision of Dr. Mariano Campoy-Quiles.
Organic photovoltaics (OPV) are a promising emerging renewable energy technology due to several attractive traits, including the possibility to broadly tune colour and transparency, light weight, insensitivity to the angle of illumination, and very high efficiency under low and indoor illumination. Moreover, their amenability for solution processing at low thermal budgets enables the roll-to-roll (R2R) fabrication of OPV modules, ensuring cost-efficient production in terms of energy and economics. Nowadays, the most important challenge is to transfer the large potential of OPV from lab scale to industry and improving its stability.
The overall objective of IDEAL is the development of highly efficient and stable OPV modules for diffuse light applications such as building integration and powering of internet of things (IoT) sensors.
In this project we will use high throughput fabrication methodology using combinatorial screening to evaluate the performance of a material system much faster than the conventional methods. The big data produced will be analysed by machine learning algorithms to predict the full photocurrent versus composition curves from vary basic molecular descriptors and to determine which are the most important parameters that define the best compromise between efficiency and stability.
The expertise of the researcher on fabrication organic electronic devices by solution processed techniques will be combined with the host group experience in combinatorial screening methodology and the use of machine learning in OPV. This project will be instrumental for the researcher to become a cutting-edge scientist and create his own group.
Ámbito científico
- engineering and technologymaterials engineeringcolors
- natural sciencescomputer and information sciencesdata sciencebig data
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energysolar energyphotovoltaic
Palabras clave
Programa(s)
Régimen de financiación
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinador
28006 Madrid
España