Papers
10
Total Citations
748
H-Index
9
About
Stefan Langner is a materials scientist whose research sits at the intersection of photovoltaics, high-throughput experimentation, and machine learning-driven materials discovery. His work focuses primarily on two next-generation solar cell technologies: perovskite-based photovoltaics and organic photovoltaics (OPV), with a particular emphasis on accelerating materials optimization through robotic automation and artificial intelligence. Langner's most influential contributions include pioneering the use of robot-based high-throughput screening platforms to systematically explore vast compositional spaces that would be prohibitively slow using conventional methods. His landmark 2021 study on temperature-induced stability reversal in perovskites (174 citations) challenged prevailing assumptions about accelerated ageing tests, while his equally cited work on OPV materials (168 citations) demonstrated how machine learning could unlock the full efficiency potential of organic solar cell blends. His 2017 investigation into wide bandgap perovskite stability (120 citations) was among the earliest to apply robotic screening to this critical challenge. Beyond efficiency, Langner has championed eco-friendly processing routes, notably developing alcoholic nanoparticle inks for organic solar cells and exploring quasi-2D perovskite stability. With several papers exceeding 100 citations, his research has meaningfully shaped how the photovoltaics community approaches materials discovery, making high-throughput and self-driving laboratory methodologies central to modern renewable energy research.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10