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Metal–organic framework (MOF) materials have shown promise in many applications, ranging from gas storage to absorption and catalysis. Because of the high porosity and low density of many MOFs, densification methods such as pelletization and extrusion are needed for practical use and for commercialization of MOF materials. Therefore, it is important to elucidate the mechanical properties of MOFs and to develop methods of further enhancing their mechanical strength. Here, we demonstrate the influence of phase purity and the presence of a pore-reinforcing component on elastic modulus and yield stress of NU-1000 MOFs through nanoindentation methods and finite element simulation. Three types of NU-1000 single crystals were compared: phase-pure NU-1000 prepared with biphenyl-4-carboxylic acid as a modulator (NU-1000-bip), NU-1000 prepared with benzoic acid as a modulator (NU-1000-ben), which results in an additional, denser impurity phase of NU-901, and NU-1000-bip whose mesopores were infiltrated with silica (SiOx(OH)y@NU-1000) by nanocasting methods. By maintaining phase purity and minimizing defects, the elastic modulus could be enhanced by nearly an order of magnitude: phase-pure NU-1000-bip crystals exhibited an elastic modulus of 21 GPa, whereas the value for NU-1000-ben crystals was only 3 GPa. The introduction of silica into the mesopores of NU-1000-bip did not strongly affect the measured elastic modulus (19 GPa) but significantly increased the load at failure from 2000 μN to 3000–4000 μN.

The existence of glass defects seriously affects the quality of glass products. Traditional glass defect recognition methods based on convolutional neural networks (CNNs) suffer from a long training time and low recognition accuracy. By introducing convolutional auto-encoder (CAE) into a CNN, we propose an auto-encoding convolutional neural network, and pre-train the convolution kernel by CAE, thereby reducing the training time caused by randomly initializing the convolution kernel. At the same time, in order to cope with the over-fitting problem caused by a small sample of glass defect dataset, we employ a fuzzy support vector machine (FSVM) instead of the Softmax classifier to classify glass defects. Furthermore, we propose a multi-channel auto-encoding convolutional neural network model to deal with misidentification of inclusions and tumor type defects due to small differences in feature space. The model takes a defect image and its enhanced image as input, and averages the output of each channel as the final output, thus achieving accurate recognition of glass defects. Experimental results show that the glass defect recognition method based on auto-encoding convolutional neural network can greatly reduce the training time while maintaining the same classification accuracy rate. The glass defect recognition method based on multi-channel auto-encoding convolutional neural network achieves a recognition rate of 95% for both inclusion and tumor type defects. As a result, and the overall recognition rate is increased from 92.6% using a single channel to 97%.

The interaction between sand particles and sand bed is a key part for the study of wind‐blown sand. The splash functions obtained from experimental observations are of great significance to help us understand relevant physical processes. Due to the adoption of substitute material in experiment and immoderate assumptions in numerical simulation, the results of previous studies are still debatable. This paper experimentally studies on splash functions of natural eolian sand particles by using a self‐developed particle emission system to simulate the splash process. Based on the high‐speed photography technique, the distribution of the angle, speed, and number of the liftoff particles and its quantitative characterization are obtained. The mathematical expectations of relevant variables from this work are comparable with existing results but are quantitatively different from the measured data of substitute particles or the numerical simulated results. More importantly, the detailed statistical characteristics of the natural eolian sand splash function are also extracted from our experimental data, which are really significant for elaborative wind‐blown sand study in the future. A particle emission system is designed and employed to simulate the splash process of natural eolian sand particlesA total of 1,024 collision events are investigated, and the pertinent splash functions are obtainedThe results indicate that the splash functions of natural sand particles are different from those of substitute materials

Abstract

Two-phase nanolaminates are known for their high strength, yet they suffer from loss of ductility. Here, we show that broadening heterophase interfaces into “3D interfaces” as thick as the individual layers breaks this strength-ductility trade-off. In this work, we use micropillar compression and transmission electron microscopy to examine the processes underlying this breakthrough mechanical performance. The analysis shows that the 3D interfaces stifle flow instability via shear band formation through their interaction with dislocation pileups. To explain this observation, we use phase field dislocation dynamics (PFDD) simulations to study the interaction between a pileup and a 3D interface. Results show that when dislocation pileups fall below a characteristic size relative to the 3D interface thickness, transmission across interfaces becomes significantly frustrated. Our work demonstrates that 3D interfaces attenuate pileup-induced stress concentrations, preventing shear localization and offering an alternative way to enhanced mechanical performance.