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

Dimensional Dependence of the Optical Absorption Band Edge of TiO₂ Nanotube Arrays beyond the Quantum Effect

Instead of investigating the quantum effect that influences the absorption band edge of TiO₂ nanostructures, herein we report that geometrical parameters can also be utilized to manipulate the optical band gap of the TiO₂ nanotube arrays. Hexagonal arrays of TiO₂ nanotubes with an excellent crystalline quality were fabricated by techniques combining anodic aluminum oxide templates and atomic layer deposition. Through absorption spectroscopic analysis we observed that the optical absorption band edge of the TiO₂ nanotube arrays exhibited a red shift as the diameter of the nanotube was tuned to be larger and the distance between two nanotubes became smaller accordingly, while the wall thickness of the nanotube was kept constant. Subsequent finite-difference time-domain simulations supported the observation from theoretical aspect and revealed a large near-field enhancement around the outer space of the nanotubes for the arrays with densely distributed nanotubes when the corresponding arrays were exposed to the illuminations. Thus, this paper provides a new perspective for the shift of the optical band gap, which is of great significance to the research in photoelectronics

Understanding the Electron Beam Resilience of Two-Dimensional Conjugated Metal–Organic Frameworks

Knowledge of the atomic structure of layer-stacked two-dimensional conjugated metal–organic frameworks (2D c-MOFs) is an essential prerequisite for establishing their structure–property correlation. For this, atomic resolution imaging is often the method of choice. In this paper, we gain a better understanding of the main properties contributing to the electron beam resilience and the achievable resolution in the high-resolution TEM images of 2D c-MOFs, which include chemical composition, density, and conductivity of the c-MOF structures. As a result, sub-angstrom resolution of 0.95 Å has been achieved for the most stable 2D c-MOF of the considered structures, Cu3(BHT) (BHT = benzenehexathiol), at an accelerating voltage of 80 kV in a spherical and chromatic aberration-corrected TEM. Complex damage mechanisms induced in Cu3(BHT) by the elastic interactions with the e-beam have been explained using detailed ab initio molecular dynamics calculations. Experimental and calculated knock-on damage thresholds are in good agreement