Water-Accelerated Photo-oxidation of CH3NH3PbI3 Perovskite: Mechanism, rate orders, and rate constants

23 August 2021, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


Understanding the chemical reactions that hybrid organic-inorganic halide perovskite (HP) semiconductors undergo in the presence of moisture, oxygen, and light are essential to the commercial development of HP solar cells and optoelectronics. Here we use optical absorbance to study the kinetics of methylammonium lead iodide (MAPbI3) degradation in response to combinations of moisture, oxygen, and illumination over a range of temperatures. We identify two primary reaction pathways that dominate MAPbI3 material degradation in these mixed environmental conditions: (1) dry photooxidation (DPO) due to the combined role of oxygen and photoexcited electrons (with a rate of 2 x 10-9 mol/m2∙s in dry air at 25 C and an effective activation energy of 0.62 eV), and (2) a water-accelerated photooxidation (WPO) process due to the combined role of water, oxygen, and photoexcited electrons (with a rate of 1 x 10-7 mol/m2s in 50% RH air at 25 C and observed effective activation energy of 0.07 eV). Commonly reported humidity-only, blue light, and thermal degradation pathways are demonstrated to have rates that are respectively 100, 1000, and >1000 times slower than predominant photooxidation processes in ambient conditions. Extracting kinetic rate constants from the dynamics of the initial degradation, we calculate that in dry air, photooxidation rate of MAPbI3 follows a f(x)∝x/(1+Kx) relationship with respect to oxygen in the vapor phase (PO2) and excess concentration of photoexcited electrons (n). In humid air, photooxidation of MAPbI3 exhibits first order kinetics with respect to the partial pressure of water in the vapor phase (PH2O). However, with respect to PO2 and n, kinetics follow a f(x)∝x/(1+kx)^2 relationship with respect to rate. We then identify a plausible reaction mechanism for degradation of MAPbI3 material that is consistent with these rate functionalities. The rate determining step for both DPO and WPO is proton abstraction by photogenerated superoxide radicals. However, proton donation by adsorbed water proceeds much more rapidly than donation by methylammonium, resulting in faster degradation rates for WPO at typical ambient conditions (~50% RH). Rate laws derived from this mechanism were fit to the entire dataset to extract rate constants for DPO and WPO processes. Accurate predictions of material degradation rates, with narrow confidence intervals of fit parameters as identified by the Bootstrap algorithm, provide the first experimental estimates of the equilibrium constants of oxygen adsorption on MAPbI3 (Keq ≈ 3 x 10-3 kPa-1) and superoxide generation from adsorbed oxygen and photoexcited electrons in MAPbI3 (Keq ≈ 5 x 10-15 (photons/m2∙s)-0.7). Given that water has been reported as a degradation product of DPO, the results reported here highlight the need for the development of encapsulation schemes that rigorously block oxygen, as over longer time periods, product water (if generated) may accumulate inside the packaging and initiate the much faster WPO process.


rate orders
rate constants

Supplementary materials

Supporting Information for Water-Accelerated Photo-oxidation of CH3NH3PbI3 Perovskite: Mechanism, rate orders, and rate constants
Experimental details; supporting discussion and text; quantification of absorbance and reflectance as a function of degradation extent; thickness-dependent degradation rate; degradation rates in dark environments; relation between photoexcited electron concentration and illumination intensity; how convective flow rate of MAPbI3 impacts degradation rate; first-principles derivation of the functional form of rate expressions for water-assisted and dry photooxidation pathways; raw ΔA vs time data and linear fits of all data used in Figures 2-4, quantifying “pre-anneal” used to dry thin films before limiting case experiments; J-V curves of devices made from MAPbI3 films and morphology; estimation of measurement error and quantification of model parameter uncertainty using the bootstrap algorithm.


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