Controlling the surface traps in metal halide perovskites (MHPs) is essential for device performance, stability, and commercialization. Here, a facile approach is introduced to passivate the methylammonium lead iodide (MAPbI₃) perovskite single crystal (PSC) surface defects by benzylamine (BA) ligand treatment, and the natural crystallographic (100) facets surface of PSC is chosen as the research platform to provide a deeper understanding of the passivation process. The confocal photoluminescence (PL) results show that the pristine three-dimensional (3D) MAPbI₃ PSC surface with a symmetric emission spectrum is normally converted to a pure two-dimensional (2D) BA₂PbI₄, and also forms a quasi-2D Ruddlesden–Popper perovskite (RPP) BA₂MA_n−1PbnI_3n+1 (n = 2, 3, 4, … ∞) after BA exchange with cation defects. The blue shift in the PL peak, as well as the extended exciton lifetimes of time-resolved photoluminescence (TRPL), indicate the realization of surface defect passivation. Additionally, changes in surface morphology are also investigated. The reaction starts with the formation of small, layered crystallites over the surface; as time elapses, the layered crystallites spread and merge in contact with each other, eventually resulting in smooth features. Our findings present a simple approach for MAPbI₃ PSC surface defect passivation, which aims to advance MHP optimization processes toward practical perovskite device applications.

Existing photovoltaic (PV) power forecasting methods face challenges in simultaneously capturing multi-scale temporal characteristics, spatial dependencies among PV plants, long-term temporal correlations, and output uncertainty. To address these issues, this paper proposes a spatiotemporal probabilistic forecasting framework for PV plant clusters that integrates Variational Mode Decomposition (VMD), Graph Convolutional Networks (GCN), Informer, and Quantile Regression (QR). VMD decomposes non-stationary PV power series into components with different frequency characteristics, while GCN captures spatial dependencies among PV plants. Informer efficiently models long-term temporal dependencies, and QR generates probabilistic forecasts to quantify output uncertainty. The proposed VMD-GCN-Informer-QR model is evaluated using data from the Australian DKASC PV system. At the 5-min forecasting horizon, the proposed model obtains an MAE of 42.834 kW and the lowest RMSE of 72.182 kW. For probabilistic forecasting, the proposed model achieves a PICP of 89.682%, with an MPIW of 201.818 kW. Multi-step forecasting further shows that the proposed model outperforms Persistence from 15 to 60 min, with its relative advantage increasing as the forecasting horizon extends. Seasonal analysis also confirms the adaptability of the proposed model under different seasonal conditions. These results demonstrate the effectiveness of the proposed framework in limiting large forecasting errors, quantifying forecasting uncertainty, and maintaining robust performance over extended forecasting horizons.