Imaging Metal Halide Perovskites Material and Properties at the Nanoscale
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...
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Veröffentlicht in: | Advanced energy materials 2020-07, Vol.10 (26), p.n/a |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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.
Metal halide perovskites present ideal characteristics for both light‐absorbing and light‐emitting applications. Yet, their underlying qualities must be well understood to sustain the current rate of advancement. This review discusses how relevant material properties can be resolved at the nanoscale through atomic force microscopy, including device stability and the promise of machine learning methods. |
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ISSN: | 1614-6832 1614-6840 |
DOI: | 10.1002/aenm.201903161 |