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Amorphous materials such as glass, polymer and amorphous alloy have broad applications ranging from daily life to extreme conditions due to their unique properties in elasticity, strength and electrical resistivity. A better understanding of atomic structure of amorphous materials will provide invaluable information for their further engineering and applications. However, experimentally determining the three-dimensional (3D) atomic structure of amorphous materials has been a long-standing problem. Due to the disordered atomic arrangement, amorphous materials do not have any translational and rotational symmetry at long-range scale. Conventional characterization methods, such as the scattering and the microscopy imaging, can only provide the statistic structural information which is averaged over the macroscopic region. The knowledge of the 3D atomic structure of amorphous materials is limited. Recently atomic resolution electron tomography (AET) has proven an increasingly powerful tool for atomic scale structural characterization without any crystalline assumptions, which opens a door to determine the 3D structure of various amorphous materials. In this review, we summarize the state-of-art characterization methods for the exploration of atomic structures of amorphous materials in the past few decades, including X-ray/neutron diffraction, nano-beam and angstrom-beam electron diffraction, fluctuation electron microscopy, high-resolution scanning/transmission electron microscopy, and atom probe tomography. From experimental data and theoretical descriptions, 3D structures of various amorphous materials have been built up. Particularly, we introduce the principles and recent progress of AET, and highlight the most recent groundbreaking feat accomplished by AET, i.e., the first experimental determination of all 3D atomic positions in a multi-component glass-forming alloy and the 3D atomic packing in amorphous solids. We also discuss the new opportunities and challenges for characterizing the chemical and structural defects in amorphous materials.

Metal-oxide interfaces with poor coherency have specific properties comparing to bulk materials and offer broad applications in heterogeneous catalysis, battery, and electronics. However, current understanding of the three-dimensional (3D) atomic metal-oxide interfaces remains limited because of their inherent structural complexity and the limitations of conventional two-dimensional imaging techniques. Here, we determine the 3D atomic structure of metal-oxide interfaces in zirconium-zirconia nanoparticles using atomic-resolution electron tomography. We quantitatively analyze the atomic concentration and the degree of oxidation, and find the coherency and translational symmetry of the interfaces are broken. Atoms at the interface have low structural ordering, low coordination, and elongated bond length. Moreover, we observe porous structures such as Zr vacancies and nano-pores, and investigate their distribution. Our findings provide a clear 3D atomic picture of metal-oxide interface with direct experimental evidence. We anticipate this work could encourage future studies on fundamental problems of oxides, such as interfacial structures in semiconductor and atomic motion during oxidation process. A detailed understanding of metal-oxide interfaces is essential for uncovering their intrinsic properties. Here, the authors investigate the 3D atomic structure of metal-oxide interfaces in Zr-ZrO2 nanoparticles using atomic-resolution electron tomography. [ABSTRACT FROM AUTHOR]

In view of the slow detection speed caused by the huge amount of computation and parameters in deep convolutional neural networks and the low accuracy by complex scenes in nowadays face detection, this paper presents an anchorfree face detection algorithm combing light-weight backbone and multi-feature fusion. The method firstly constructs a light-weight convolutional neural network as the backbone for feature extraction. Then three different modules are used to process the feature fusion:receptive field enhancement module to strengthen image information extraction, weighted feature fusion module to improve detection accuracy, channel shuffle fusion module to simplify the detection head. Finally, the center point anchor-free detection method is used to calculate the fused features and predict the position of faces. The experimental results show that the final model parameter is only 5.1 MB. The detection accuracy of easy, medium and hard in WIDERFACE verification set are improved by 1.4, 2.2 and 4.8 percentage points respectively compared with the benchmark method, which shows that the method has high accuracy in complex scenes face detection with a light-weight model and verifies the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]