Improving the Environmental Stability of Methylammonium-Based Perovskite Solar Cells

Abstract

Perovskite solar cells (PSCs), as an emerging type of photovoltaics, have reached beyond 20% efficiency within a decade. Technoeconomic analysis suggests that PSCs are promising alternatives to the market-dominant silicon, because PSC manufacturing processes are more cost effective due to their solution processing methods. However, the prototypical perovskite material, methylammonium lead iodide (MAPbI3), is environmentally unstable and degrades in the presence of oxygen, light, and moisture. Thus, despite its high initial performance, the degrading performance over time means that the levelized cost of electricity (LCOE) of perovskites is prohibitively high. An improved stability (targeting <0.25% degradation per year or less) could help improve the LCOE of perovskites to surpass silicon.

Researchers have been incorporating low-dimensional (LD), such as 0D, 1D, or 2D perovskites, to improve PSCs stability. We can obtain LD perovskites by changing any 𝐴, 𝐵, or 𝑋 ions in the 𝐴𝐵𝑋3 structures of high-performing 3D perovskites. The 𝐴-site cations can be organic or inorganic, which give us a vast number of possible perovskite compounds. Some common examples of 3D perovskite 𝐴-site cation are methylammonium (MA) and formamidinium (FA). When the 𝐴-site is larger than FA, it forms LD perovskite structures.

This thesis focuses on investigating how to incorporate the LD perovskites as a capping layer to improve the stability of MA-based perovskites, including how to screen and select the 𝐴-site cations of LD perovskite capping layers that can improve the MAPbI3 absorber stability, how to improve the stability of MAPb(IxBr1–x )3 mixed halide, a wide-bandgap absorber for tandem cells and indoor PV applications, and how to incorporate capping layers in inverted p–i–n PSCs device architectures. These 3 questions are answered by combining high-throughput experiments with machine learning analysis. The optoelectronic, structural, and chemical composition properties of the LD capping-3D perovskite absorber materials are probed to identify the degradation mechanisms using advanced characterization methods. This deeper understanding of perovskite degradation and the strategies to solve the instability issue are critical to push PSCs closer toward commercialization.

Keywords: perovskite solar cell, low-dimensional perovskite, capping layer, 2D-3D heterostructures, high-throughput experiment, machine learning