Statistical morphological identification of low-dimensional nanomaterials by using TEM

Nanomaterials with low-dimensional morphology display unique properties in catalysis and related fields, which are highly dependent on the structure and aspect ratio. Thus, accurate identification of the structure and morphology is the basis to correlate to the performance. However, the widely adopted techniques such as XRD is incapable to precise identify the aspect ratio of low-dimensional nanomaterials, not even to quantify the morphological uniformity with statistical deviation value. Herein, ZnO nanorod and nanosheet featured with one-and two-dimensional morphology were selected as model materi-als, which were prepared by the hydrothermal method and statistically characterized by transmission electron microscopy (TEM). The results indicate that ZnO nanorods and nanosheets display rod-like and orthohexagnal morphology, which mainly encapsulated with {100} and {001} planes, respectively. The 7.36 +/- 0.20 and 0.39 +/- 0.02 aspect ratio (c/a) of ZnO nanorods and nanosheets could be obtained through the integration of the (100) and (002) diffraction rings in selected area electron diffraction (SAED). TEM combining with the SAED is favorable compare with XRD, which not only provides more accurate aspect ratio results with standard deviation values but also requires very small amounts of sample. This work is supposed to provide a convenient and accurate method for the characterization of nanomaterials with low-dimensional morphology through TEM. (c) 2021 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

The combination of transmission electron microscopy (TEM) with selected area electron diffraction (SAED) is advantageous for the characterization of low-dimensional nanomaterials, providing more accurate aspect ratio results with standard deviation values.