An Atomic Scale Understanding of Defect Mediated Stability and Hysteresis in Perovskite Solar Cells
Inexpensive and reproducible chemical synthesis coupled with high conversion efficiencies make perovskite materials an attractive solution for many future photovoltaic applications. However, there are significant fundamental materials challenges that have to be first understood and then overcome for these perovskites to reach widespread commercial viability – such as the long term stability of the cells and a well-documented hysteresis effect that makes it difficult to determine the overall efficiency of the system. Both of these (and other) challenges are most likely linked to point defect mediated migration that occurs in the vicinity of the major interfaces in the photovoltaic cells during operation. Recent upgrades to the aberration corrected JEOL 2100 STEM at the University of Liverpool now permit atomic resolution images to be acquired under extremely low electron doses (<1 e/Å2). Under these conditions, organic structures (and defects and interfaces) are stable enough for point vacancy distributions to be imaged directly. Furthermore, in-situ electrochemical stages for the microscope now permit interface structures to be biased while immersed in liquid/vapour/gas, illuminated with light and simultaneously imaged on the atomic scale. Such capabilities mean that the university is poised to make unique observations on the stability of interfaces and defects in photovoltaic systems that are not possible anywhere else in the world. The proposed project involves a graduate student performing world-class electron microscopy experiments on photovoltaic perovskite systems produced by collaborators within the PV doctoral training centre. While the research will generally provide some of the first atomic scale images of these systems, the project will specifically aim to accomplish the following: • Image structures, interfaces and defects in photovoltaic perovskites on the atomic scale using revolutionary sub-sampling, inpainting and machine learning methods. • Image point/extended defects and measure mobilities under electrochemical bias with and without illumination and under ambient/elevated/reduced temperatures. • Image and measure defect/interface mobilities under bias, temperature and in gas/vapour/liquid to understand the environmental limitations of the systems For further information on the project and how to apply, please email Professor Nigel D. Browning – email@example.com
For more information on what to expect as a CDT-PV student then please see our CDT-PV Handbook.