دورية أكاديمية
Disentangling Electronic Transport and Hysteresis at Individual Grain Boundaries in Hybrid Perovskites via Automated Scanning Probe Microscopy.
| العنوان: | Disentangling Electronic Transport and Hysteresis at Individual Grain Boundaries in Hybrid Perovskites via Automated Scanning Probe Microscopy. |
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| المؤلفون: | Liu Y; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States., Yang J; Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States., Lawrie BJ; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.; Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States., Kelley KP; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States., Ziatdinov M; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States.; Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States., Kalinin SV; Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States., Ahmadi M; Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States. |
| المصدر: | ACS nano [ACS Nano] 2023 May 23; Vol. 17 (10), pp. 9647-9657. Date of Electronic Publication: 2023 May 08. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: American Chemical Society Country of Publication: United States NLM ID: 101313589 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1936-086X (Electronic) Linking ISSN: 19360851 NLM ISO Abbreviation: ACS Nano Subsets: PubMed not MEDLINE; MEDLINE |
| أسماء مطبوعة: | Original Publication: Washington D.C. : American Chemical Society |
| مستخلص: | 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. |
| فهرسة مساهمة: | Keywords: autonomous and automated experiments; grain boundary; hybrid perovskites; machine learning; scanning probe microscopy |
| تواريخ الأحداث: | Date Created: 20230508 Date Completed: 20230523 Latest Revision: 20230523 |
| رمز التحديث: | 20231215 |
| DOI: | 10.1021/acsnano.3c03363 |
| PMID: | 37155579 |
| قاعدة البيانات: | MEDLINE |
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