Temperature-Dependent Crystallization Mechanisms of Methylammonium Lead Iodide Perovskite From Different Solvents

Hybrid perovskites are a novel type of semiconductors that show great potential for solution-processed optoelectronic devices. For all applications, the device performance is determined by the quality of the solution-processed perovskite thin films. During solution processing, the interaction of solvent with precursor molecules often leads to the formation of solvate intermediate phases that may diverge the crystallization pathway from simple solvent evaporation to a multi-step formation process. We here investigate the crystallization of methylammonium lead iodide (MAPbI(3)) from a range of commonly utilized solvents, namely dimethyl sulfoxide (DMSO), N,N-dimethylformamide (DMF), N-methylpyrrolidone (NMP), and gamma-butyrolactone (GBL) at different temperatures ranging from 40 degrees C to >100 degrees C by in-situ grazing-incidence wide-angle X-ray scattering (GIWAXS) measurements. For all solvents but GBL, we clearly observe the formation of solvate-intermediate phases at moderate processing temperatures. With increasing temperatures, an increasing fraction of the MAPbI(3) perovskite phase is observed to form directly. From the temperature-dependence of the phase-formation and phase-decomposition rates, the activation energy to form the MAPbI(3) perovskite phase from the solvate-phases are determined as a quantitative metric for the binding strength of the solvent within the solvate-intermediate phases and we observe a trend of DMSO > DMF > NMP > GBL. These results enable prediction of processing temperatures at which solvent molecules can be effectively removed.

Automated summary

Hybrid perovskites are promising semiconducting materials for solution-processed optoelectronic devices, with the quality of perovskite thin films being critical for device performance. By investigating the crystallization process from a range of solvents, the binding strength of solvents within solvate-intermediate phases can be quantified, enabling prediction of optimal processing temperatures.