Disentangling Electronic Transport and Hysteresis at Individual Grain Boundaries in Hybrid Perovskites via Automated Scanning Probe Microscopy

Abstract

Underlying the rapidly increasing photovoltaic efficiency and stability of metal halide perovskites (MHPs) is the advancement in the understanding of the microstructure of polycrystalline MHP thin film. Over the past decade, intense efforts have been aimed at understanding the effect of microstructures on MHP properties, including chemical heterogeneity, strain disorder, phase impurity, etc. It has been found that grain and grain boundary (GB) are tightly related to lots of microscale and nanoscale behavior in MHP thin films. Atomic force microscopy (AFM) is widely used to observe grain and boundary structures in topography and subsequently to study the correlative surface potential and conductivity of these structures. For now, most AFM measurements have been performed in imaging mode to study the static behavior; in contrast, AFM spectroscopy mode allows us to investigate the dynamic behavior of materials, e.g., conductivity under sweeping voltage. However, a major limitation of AFM spectroscopy measurements is that they require manual operation by human operators, and as such only limited data can be obtained, hindering systematic investigations of these microstructures. In this work, we designed a workflow combining the conductive AFM measurement with a machine learning (ML) algorithm to systematically investigate grain boundaries in MHPs. The trained ML model can extract GBs locations from the topography image, and the workflow drives the AFM probe to each GB location to perform a current–voltage (IV) curve automatically. Then, we are able to have IV curves at all GB locations, allowing us to systematically understand the property of GBs. Using this method, we discovered that the GB junction points are less conductive, potentially more photoactive, and can play critical roles in MHP stability, while most previous works only focused on the difference between GB and grains.

The Role of SnO2 Processing on Ionic Distribution in Double-Cation–Double Halide Perovskites

“…42 While the systematic study exploring the structure−property relationships associated with the strain-controlled MHP films utilizing spatially resolved electrical and chemical characterization techniques is required, the global impact of K+ distribution in the MHP films is evidenced by the c-AFM analysis. 38,40,43,44 With the micro-strain control, we observed that hybrid B and C samples generate higher conductivity in light and dark conditions compared to samples A and D. These results are in agreement with Figure S5 and Table S1 featuring the calculated PCE from J−V curves. Sample B, which represents the most efficient solar cell in the series, produces photocurrent upward of 300 pA in light on and 120 pA in dark conditions.…”

Influence of A-Site Composition on the Nanostructure and Electrical Properties of APbI3 Perovskite Films: Implications for Solar Cells

“…Extensive research has been dedicated to improving the efficiency and stability of PSCs by mitigating hysteresis. − Previous studies have revealed that GBs in perovskite films play a crucial role in ion migration pathways. , In order to investigate the influence of A-site doping on the GB regions, we utilized KPFM and C-AFM to measure the surface potential and current of the three thin films. The surface potential images of the three thin films under dark and illuminated conditions are depicted in Figure a–f.…”

FPGA and computer-vision-based atom tracking technology for scanning probe microscopy

“…With the maturity of machine learning and computer vision technology, it has more and more extensive applications in the field of scanning probe microscope. [16][17][18][19] For example, it is able to divide grain boundaries [20] and classify lattice structures, [21] and it can be applied in cell detection and shape distinction. [22] With the help of the software of computer vision, various methods mainly based on feature matching make it possible to achieve atom tracking.…”