(757d) Capturing Effects of Sample-to-Sample Variation on the Long-Term Stability of Perovskite Optoelectronic Properties | AIChE

(757d) Capturing Effects of Sample-to-Sample Variation on the Long-Term Stability of Perovskite Optoelectronic Properties


Dunlap-Shohl, W. - Presenter, University of Washington
Hillhouse, H., University of Washington
Tischhauser, A., University of Washington
Tsai, C. E., University of Washington
Meng, Y., University of Washington
Sunkari, P., University of Washington
Chen, Y. C., University of Washington
Meila, M., University of Washington
As efforts to commercialize halide perovskite-based thin film solar cells accelerate, reliability and reproducibility of these materials are becoming increasingly pressing concerns. Accurate estimates of solar cell service lifetimes are vital for manufacturers to provide warranties on these devices. Halide perovskites are known to be sensitive to heat, light, oxygen, moisture, and electrical bias,1 but their response to these stresses also depends on intrinsic qualities that may vary from sample to sample (e.g., grain size or point defect densities). These intrinsic properties are determined not only by variables that are deliberately manipulated during deposition, but also by those that are challenging to control or measure (e.g., temperature and composition of the atmosphere in a glove box).2 As a result, it is imperative to understand not only how environmental conditions affect perovskite film degradation, but also the range of variation that can be expected within a single condition, and to identify sample-specific film characteristics that can quantify this variation and its effects on long-term stability.

Previously, we have shown that initial evolution of a film’s optical transmittance can be exploited to predict decay kinetics of its carrier diffusion length across a wide range of environmental conditions,3 but relatively little attention has so far been given to the extent to which the effects of variations within a single set of conditions can be predicted. In this work, we investigate the variation in stability of methylammonium lead iodide (MAPbI3) thin films prepared and tested under identical conditions, and explore how early-time measurements can be used to predict long-term decay of their optoelectronic properties. In a typical degradation experiment, we measure the absolute photoluminescence intensity, photoconductivity, and optical transmittance of a perovskite film in situ (PL-PC-Tr measurements) while subjecting it to controlled environmental conditions. These three measurements allow us to quantify the quasi-Fermi level splitting (QFLS), mean carrier diffusion length, and material degradation rate, respectively. The mean carrier diffusion length is used as a figure of merit for specifying material service lifetime, as it characterizes the ability of a solar absorber to sustain and transport photoexcited carriers. The diffusion length also decays more quickly than the QFLS, signifying that carrier transport is more severely affected by material degradation than carrier generation and represents the "weak link" limiting photovoltaic efficacy. Thus, we use the time it takes for the diffusion length to decay to 80% of its original value as the effective service lifetime of a perovskite film (T80). MAPbI3 films prepared and tested under identical conditions can exhibit variations in T80 of over an order of magnitude. The time derivative of a film’s optical transmittance at the start of the experiment (initial bleaching rate) varies over a similar scale and exhibits a strong negative correlation with T80. That is, faster bleaching rates (implying faster conversion of opaque MAPbI3 to transparent PbI2) are associated with shorter T80. Machine learning models trained using regularized regression can exploit this relationship to accurately predict T80 using only information available from PL-PC-Tr measurements taken within the first few minutes of a film’s exposure to the environment. While the stability of nominally identical MAPbI3 films may vary considerably, this variation may be largely accounted for by their initial degradation behavior, which provides a straightforward means of forecasting service lifetime.


(1) Boyd, C. C.; Cheacharoen, R.; Leijtens, T.; McGehee, M. D. Understanding Degradation Mechanisms and Improving Stability of Perovskite Photovoltaics. Chem. Rev. 2019, 119 (5), 3418–3451. https://doi.org/10.1021/acs.chemrev.8b00336.

(2) Saliba, M.; Correa-Baena, J.-P.; Wolff, C. M.; Stolterfoht, M.; Phung, N.; Albrecht, S.; Neher, D.; Abate, A. How to Make over 20% Efficient Perovskite Solar Cells in Regular ( n–i–p ) and Inverted ( p–i–n ) Architectures. Chem. Mater. 2018, 30 (13), 4193–4201. https://doi.org/10.1021/acs.chemmater.8b00136.

(3) Stoddard, R. J.; Dunlap-Shohl, W. A.; Qiao, H.; Meng, Y.; Kau, W. F.; Hillhouse, H. W. Forecasting the Decay of Hybrid Perovskite Performance Using Optical Transmittance or Reflected Dark-Field Imaging. ACS Energy Lett. 2020, 5 (3), 946–954. https://doi.org/10.1021/acsenergylett.0c00164.