期刊
ADVANCED ENERGY MATERIALS
卷 10, 期 26, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/aenm.201903161
关键词
artificial intelligence; atomic force microscopy; functional imaging; halide perovskites; nanoscale properties
类别
资金
- NSF (ECCS) [16-10833]
- 2016 APS Ovshinsky Sustainable Energy Fellowship
- UMERC's 2018-2019 Harry K. Wells Graduate Fellowship
- UMD's 2019 Graduate Summer Research Fellowship
- UMD's Fall 2019 Ann G. Wylie Dissertation Fellowship
- University of Maryland's Dean Fellowship
Metal halide perovskites exhibit optimal properties for optoelectronic devices, ranging from photovoltaics to light-emitting diodes, utilizing simple fabrication routes that produce impressive electrical and optical tunability. As perovskite technologies continue to mature, an understanding of their fundamental properties at length scales relevant to their morphology is critical. In this review, an overview is presented of the key insights into perovskite material properties provided by measurement methods based on the atomic force microscopy (AFM). Specifically, the manner in which AFM-based techniques supply valuable information regarding electrical and chemical heterogeneity, ferroelectricity and ferroelasticity, surface passivation and chemical modification, ionic migration, and material/device stability is discussed. Continued advances in perovskite materials will require multimodal approaches and machine learning, where the output of these scanning probe measurements is combined with high spatial resolution structural and chemical information to provide a complete nanoscale description of materials behavior and device performance.
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