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In situ Grazing Incidence Small Angle X-ray Scattering on Roll-To-Roll Coating of Organic Solar Cells with Laboratory X-ray Instrumentation
Published on: March 2, 2021
Toward Self-Driven Autonomous Material and Device Acceleration Platforms (AMADAP) for Emerging Photovoltaics
Jiyun Zhang1,2, Jens A Hauch1, Christoph J Brabec1,2
1Forschungszentrum Juelich GmbH, Helmholtz-Institute Erlangen-Nürnberg (HI ERN), Department of High Throughput Methods in Photovoltaics, Immerwahrstraße 2, 91058 Erlangen, Germany.
Automated laboratories accelerate the discovery and optimization of new solar materials, like perovskites and organic photovoltaics. These platforms use artificial intelligence and robotics to speed up research cycles and enhance energy infrastructure resilience.
Area of Science:
- Materials Science
- Renewable Energy Technologies
- Photovoltaics
Background:
- The growing demand for renewable energy necessitates the development of novel photovoltaic technologies beyond silicon.
- Emerging photovoltaics offer diversification and enhance the security and competitiveness of the solar industry.
- Discovering new functional solar materials involves complex optimization challenges in vast material and parameter spaces.
Purpose of the Study:
- To review advancements in automated and autonomous laboratories for materials discovery and device optimization.
- To highlight the application of these platforms in emerging photovoltaics, specifically perovskite and organic solar cells.
- To introduce in-house developed Materials Acceleration Platforms (MAPs) and Device Acceleration Platforms (DAPs).
Main Methods:
- Integration of robotic synthesis and characterization with AI-driven data analysis and experimental design.
- Development and utilization of two MAPs for materials discovery and two DAPs for device optimization.
- Application of robot-based high-throughput experimentation for optimizing complex material and parameter spaces.
Main Results:
- Demonstrated success in accelerating the discovery and optimization of organic interface materials, new semiconductors, and thin film composites.
- Showcased the potential of platforms like SPINBOT and AMANDA for optimizing device architectures and autonomous operation.
- Successfully addressed optimization challenges in multidimensional composition and parameter spaces for organic and perovskite photovoltaics.
Conclusions:
- Automated and autonomous laboratories are crucial for efficient materials discovery and device optimization in emerging photovoltaics.
- MAPs and DAPs significantly reduce experimental cycles and improve data quality for machine learning.
- A holistic concept for a self-driven autonomous material and device acceleration platform (AMADAP) laboratory is proposed for future solar materials development.
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