Enhancing the Efficiency and Stability of Inverted Formamidinium-Cesium Lead-Triiodide Perovskite Solar Cells through Lewis Base Pretreatment of Buried Interfaces.

Mixed components of formamidinium(FA) and cesium (Cs)-based perovskite solar cells are the most hopeful for commercialization owing to their excellent operational and phase stabilities, especially for devices with inverted structure. The nonradiative recombination of carriers can be effectively suppressed through interface optimization, therefore, the performance of devices can be improved. Notably, the buried interface emerges as critical aspects such as charge transport, charge recombination kinetics, and morphology of perovskite films. This study focuses on a straightforward yet effective approach to overcome buried interface challenges between organic polymers (poly(-triarylamine) (PTAA) and FACs-based perovskite films. The PTAA substrate is pretreated with a Lewis base known as 2-butynoic acid (BA) with a C═O functional group. First, it can be an interfacial buffering layer, harmonizing stress mismatch between the perovskite and PTAA layers, consequently optimizing crystallization and improving perovskite film quality. Second, Pb2+ defect can be passivated at the buried interface of the perovskite film through binding with the C═O group of the BA molecule. This dual-function strategy leads to a substantial enhancement in both photoelectric conversion efficiency (PCE) and stability of devices. Finally, the PCE of the device-modified buried interface with BA reaches an impressive 23.33%. Furthermore, unencapsulated devices with BA treatment maintain approximately 94% of their initial efficiency after aging at maximum power point tracking for 1000 h.

Simple Structural Descriptor Obtained from Symbolic Classification for Predicting the Oxygen Vacancy Defect Formation of Perovskites.

Symbolic classification is an approach of interpretable machine learning for building mathematical formulas that fit certain data sets. In this work, symbolic classification is used to establish the relationship between oxygen vacancy defect formation energy and structural features. We find a structural descriptor na(ra/Ena - rb), where na is the valence of the a-site ion, ra is the radius of the a-site ion, Ena is the electronegativity of the a-site ion, and rb is the radius of the b-site ion. It accelerates the screening of defect-free oxide perovskites in advance of density functional theory (DFT) calculations and experimental characterization. Our results demonstrate the potential of symbolic classification for accelerating the data-driven design and discovery of materials with improved properties.