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Efficient and Stable Organic Photovoltaics by Combinatorial Screening

Project description

High-throughput screening profiles the most promising organic photovoltaics materials

The photovoltaics market has advanced tremendously over the nearly seven decades since the first practical silicon solar cell was demonstrated. While silicon has dominated the market for years, organic photovoltaics are gaining ground as a promising alternative with many benefits in performance, traits and processing. With the support of the Marie Skłodowska-Curie Actions programme, the IDEAL project is tackling the challenge of moving this promising technology from the laboratory to the field. Using machine learning on data collected from high throughput processing techniques and numerous materials, the team expects to define the best compromise between efficiency and stability through prediction of the most important molecular descriptors for candidate materials.


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.


Net EU contribution
€ 160 932,48
28006 Madrid

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Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Research Organisations
Total cost
€ 160 932,48