© 2015 American Chemical Society. Organometal halide perovskites have recently attracted tremendous attention both at the experimental and theoretical levels. These materials, in particular methylammonium triiodide, are still limited by poor chemical and structural stability under ambient conditions. Today this represents one of the major challenges for polycrystalline perovskite-based photovoltaic technology. In addition to this, the performance of perovskite-based devices is degraded by deep localized states, or traps. To achieve better-performing devices, it is necessary to understand the nature of these states and the mechanisms that lead to their formation. Here we show that the major sources of deep traps in the different halide systems have different origin and character. Halide vacancies are shallow donors in I-based perovskites, whereas they evolve into a major source of traps in Cl-based perovskites. Lead interstitials, which can form lead dimers, are the dominant source of defects in Br-based perovskites, in line with recent experimental data. As a result, the optimal growth conditions are also different for the distinct halide perovskites: growth should be halide-rich for Br and Cl, and halide-poor for I-based perovskites. We discuss stability in relation to the reaction enthalpies of mixtures of bulk precursors with respect to final perovskite product. Methylammonium lead triiodide is characterized by the lowest reaction enthalpy, explaining its low stability. At the opposite end, the highest stability was found for the methylammonium lead trichloride, also consistent with our experimental findings which show no observable structural variations over an extended period of time.
|Original language||English (US)|
|Number of pages||8|
|Journal||Chemistry of Materials|
|State||Published - Jun 12 2015|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-11-009-21
Acknowledgements: This publication is based in part on work supported by Award KUS-11-009-21, made by King Abdullah University of Science and Technology (KAUST), by the Ontario Research Fund Research Excellence Program, and by the Natural Sciences and Engineering Research Council (NSERC) of Canada. Computations were performed on the Southern Ontario Smart Computing Innovation Platform (SOSCIP) Blue Gene/Q supercomputer located at the University of Toronto’s SciNet(60) HPC facility. The SOSCIP multiuniversity/industry consortium is funded by the Ontario Government and the Federal SciNet is funded by the Canada Foundation for Innovation under the auspices of Compute Canada; the Government of Ontario; Ontario Research Fund–Research Excellence; and the University of Toronto.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.