Descrizione del progetto
Lo screening ad alte prestazioni sagoma i materiali fotovoltaici organici più promettenti
Il mercato fotovoltaico ha compiuto enormi passi avanti nei quasi settanta anni da quando è stata presentata la prima cella solare pratica in silicio. Se da un alto il silicio ha dominato il mercato per anni, dall’altro il fotovoltaico organico sta guadagnando terreno quale alternativa promettente con numerosi vantaggi in merito a prestazioni, caratteristiche e lavorazione. Grazie al sostegno del programma di azioni Marie Skłodowska-Curie, il progetto IDEAL sta affrontando la sfida di fare uscire questa tecnologia promettente dal laboratorio e condurla sul campo. Avvalendosi dell’apprendimento automatico sui dati raccolti da tecniche di elaborazione ad alte prestazioni e numerosi materiali, il gruppo del progetto prevede di trovare il migliore compromesso tra efficienza e stabilità mediante la previsione dei descrittori molecolari più importanti per i materiali candidati.
Obiettivo
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.
Campo scientifico
- 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
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinatore
28006 Madrid
Spagna