(508f) Perovskite to Non-Perovskite Phase Changes Using in Situ Measurements
AIChE Annual Meeting
2022
2022 Annual Meeting
Materials Engineering and Sciences Division
Understanding Perovskite Semiconductors
Wednesday, November 16, 2022 - 2:00pm to 2:15pm
To better understand the formation of nonperovskite phases in halide perovskites, and work toward a full understanding of the formation mechanism, rates involved, and compositional/material factors influencing these parameters, we performed in situ measurements of the perovskite-nonperovskite phase transformation. We will present initial work focused on the model system CsPbI3 where we track the kinetics of the perovskite to non-perovskite phase transition. In this system, we demonstrate We demonstrate that the perovskite-nonperovskite phase transition is first order with respect to atmospheric water and appears to be nucleation limited with nonperovsktie phase nucleation occuring at the film surface. In conststant temperature/humidity conditions, we demonstrate the ability to decrease the phase transition rate from 7.5 × 10-3 s-1 to 1.4 × 10-3 s-1, over 5x, by surface iodide treatment of the films. This insight will help the design of more robust perovskite films and continued understanding of this phase transformation.
Furthermore, we will present significant data exploring the complex relationship between humidity, as well as other initiating gas-phase species besides water, and temperature to strengthen the understanding of the role of water in the perovskite-nonperovskite phase transition. Accurate quantification of these various effects and parameters will be an important tool for the development of test protocols for applications where low levels of water vapor will be present (e.g., perovskite solar cells using plastic substrates). Initial results will also be presented which compare and constrast the perovskite-nonperovskite phase transformation in CsPbI3 to FAPbI3 and FAxCs1-xPbI3 materials. Characterization and complete description of these effects should lead to better predictions of material and device stability and more accurate predictions of real-world lifetime from accelerated testing data.