Deep-Learning Pipeline for Statistical Quantification of Amorphous Two-Dimensional Materials.

Aberration-corrected transmission electron microscopy enables imaging of two-dimensional (2D) materials with atomic resolution. However, dissecting the short-range-ordered structures in radiation-sensitive and amorphous 2D materials remains a significant challenge due to low atomic contrast and laborious manual evaluation. Here, we imaged carbon-based 2D materials with strong contrast, which is enabled by chromatic and spherical aberration correction at a low acceleration voltage. By constructing a deep-learning pipeline, atomic registry in amorphous 2D materials can be precisely determined, providing access to a full spectrum of quantitative data sets, including bond length/angle distribution, pair distribution function, and real-space polygon mapping. Accurate segmentation of micropores and surface contamination, together with robustness against background inhomogeneity, guaranteed the quantification validity in complex experimental images. The automated image analysis provides quantitative metrics with high efficiency and throughput, which may shed light on the structural understanding of short-range-ordered structures. In addition, the convolutional neural network can be readily generalized to crystalline materials, allowing for automatic defect identification and strain mapping.

Optimal acceleration voltage for near-atomic resolution imaging of layer-stacked 2D polymer thin films.

Despite superb instrumental resolution in modern transmission electron microscopes (TEM), high-resolution imaging of organic two-dimensional (2D) materials is a formidable task. Here, we present that the appropriate selection of the incident electron energy plays a crucial role in reducing the gap between achievable resolution in the image and the instrumental limit. Among a broad range of electron acceleration voltages (300 kV, 200 kV, 120 kV, and 80 kV) tested, we found that the highest resolution in the HRTEM image is achieved at 120 kV, which is 1.9 Å. In two imine-based 2D polymer thin films, unexpected molecular interstitial defects were unraveled. Their structural nature is identified with the aid of quantum mechanical calculations. Furthermore, the increased image resolution and enhanced image contrast at 120 kV enabled the detection of functional groups at the pore interfaces. The experimental setup has also been employed for an amorphous organic 2D material.

Imaging an unsupported metal-metal bond in dirhenium molecules at the atomic scale.

Metallic bonds remain one of the most important and least understood of the chemical bonds. In this study, we generated Re2 molecules in which the Re-Re core is unsupported by ligands. Real-time imaging of the atomic-scale dynamics of Re2 adsorbed on a graphitic lattice allows direct measurement of Re-Re bond lengths for individual molecules that changes in discrete steps correlating with bond order from one to four. Direct imaging of the Re-Re bond breaking process reveals a new bonding state with the bond order less than one and a high-amplitude vibrational stretch, preceding the bond dissociation. The methodology, based on aberration-corrected transmission electron microscopy imaging, is shown to be a powerful analytical tool for the investigation of dynamics of metallic bonding at the atomic level.