Description du projet
Le criblage à haut débit pour déterminer le profil des matériaux photovoltaïques organiques les plus prometteurs
Le marché photovoltaïque a accompli d’énormes progrès au cours des sept décennies qui ont suivi la première démonstration de la cellule solaire au silicium. Si le silicium a dominé le marché pendant des années, le photovoltaïque organique gagne du terrain en tant que solution prometteuse aux avantages pluriels en termes de performance, de caractéristiques et de traitement. Avec le soutien du programme Actions Marie Skłodowska-Curie, le projet IDEAL relève le défi de faire sortir cette technologie prometteuse du carcan du laboratoire. En tirant parti de l’apprentissage automatique sur des données collectées à partir de techniques de traitement à haut débit et de nombreux matériaux, l’équipe espère définir le meilleur compromis entre efficacité et stabilité grâce à la prédiction des descripteurs moléculaires les plus importants pour les matériaux candidats.
Objectif
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.
Champ scientifique
- 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
Mots‑clés
Programme(s)
Régime de financement
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinateur
28006 Madrid
Espagne