2D materials for conducting holes from grain boundaries in perovskite solar cells.
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ABSTRACT:
SUBMITTER: You P
PROVIDER: S-EPMC8012639 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Grain boundaries and interfacial impurities are the main factors that limit the further development of polycrystalline perovskite solar cells because their existence severely deteriorates the device performance. In order to optimize the efficiency of perovskite solar cells, it is essential to eliminate these defects. In the present work, potassium iodide (KI) is incorporated into the perovskite absorber. KI incorporation improves the crystallinity of the perovskite, increases the grain size, and decreases the contact potential distribution at the grain boundary, which are verified by X-ray diffraction, scanning electronic microscopy and Kelvin probe force microscopy. Besides, the activation energy of the recombination, estimated from the temperature dependent current-voltage of perovskite solar cells, is larger than the bandgap calculated from the temperature coefficient. These suggest that KI incorporation effectively passivates the grain boundaries and interfacial defects. As a result, charge trapping in the absorber as well as the bimolecular and trap-assisted recombination of the device are significantly suppressed. Consequently, the open circuit voltage and fill factor of the incorporated devices are greatly improved, enabling an optimized power conversion efficiency of 19.5%, in comparison with that of 17.3% for the control one. Our work provides an effective strategy of defect passivation in perovskite solar cells by KI incorporation and clarifies the mechanism of the performance optimization of KI incorporated devices.
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Project description:Grain boundaries (GBs) in two-dimensional (2D) materials are known to dramatically impact material properties ranging from the physical, chemical, mechanical, electronic, and optical, to name a few. Predicting a range of physically realistic GB structures for 2D materials is critical to exercising control over their properties. This, however, is nontrivial given the vast structural and configurational (defect) search space between lateral 2D sheets with varying misfits. Here, in a departure from traditional evolutionary search methods, we introduce a workflow that combines the Graph Neural Network (GNN) and an evolutionary algorithm for the discovery and design of novel 2D lateral interfaces. We use a representative 2D material, blue phosphorene (BP), and identify 2D GB structures to test the efficacy of our GNN model. The GNN was trained with a computationally inexpensive machine learning bond order potential (Tersoff formalism) and density functional theory (DFT). Systematic downsampling of the training data sets indicates that our model can predict structural energy under 0.5% mean absolute error with sparse (<2000) DFT generated energy labels for training. We further couple the GNN model with a multiobjective genetic algorithm (MOGA) and demonstrate strong accuracy in the ability of the GNN to predict GBs. Our method is generalizable, is material agnostic, and is anticipated to accelerate the discovery of 2D GB structures.
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