Bioinspired yeast microcapsules loaded with self-assembled nanotherapies for targeted treatment of cardiovascular disease

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滚筒反力式制动检验台制动力测量不确定度评定

本文介绍了滚筒反力式制动检验台制动力的不确定度评定。     

The interlaminar shear failure characteristics of asphalt pavement coupled heating cables

Nowadays, heating cables are used as heat sources for heating pavements in practical engineering. However, there is a contradiction between the snow melting function and the interlaminar stability of heating pavement. In order to solve the contradiction, the interlaminar failure behavior of asphalt mixture coupled heating cables specimen (AMCS) was researched, through experiments and the finite element method. Under the different conditions of heating cables and rolling times, a series of direct shear tests was performed at the interface of AMCS, to compare the interlaminar stability of three different AMCS. Meanwhile, based on the bilinear cohesive zone model and coulomb friction model a 2D finite element model was established, to simulate this shear failure processes and make up for the limitations of the experiment. According to above test and simulation results, the failure mechanism and the weakest interface in AMCS were found, and the influence of the heating cable’s diameter and embedded spacing on the interlaminar shear strength were found. Then, a modified coulomb theorem model was proposed to predict the shear strength of the AMCS. This research enriches the design theory of the heating pavement and it has great significance for its structural design of heating asphalt pavement.     

Synthesis of ultrathin semicircle-shaped copper nanowires in ethanol solution for low haze flexible transparent conductors

Nano Research - Copper nanowires (CuNWs) are becoming an indispensable item for next-generation transparent optical devices due to their excellent conductivity and transparency. In this work,...     

Computational Parametric Analysis of Cellular Solids with the Miura-Ori Metamaterial Geometry under Quasistatic Compressive Loads

Origami-based metamaterials have widespread application prospects in various industries including aerospace, automotive, flexible electronics, and civil engineering structures. Among the wide range of origami patterns, the fourfold tessellation known as Miura-ori is of particular attraction to engineers and designers. More specifically, researchers have proposed different 3D structures and metamaterials based on the geometric characteristics of this classic origami pattern. Herein, a computational modeling approach for the design and evaluation of 3D cellular solids with the Miura-ori metamaterial geometry which can be of zero or nonzero thicknesses is presented. To this end, first, a range of design alternatives generated based on a numerical parametric model is designed. Next, their mechanical properties and failure behavior under quasistatic axial compressive loads along three perpendicular directions are analyzed. Then, the effects of various geometric parameters on their energy absorption behavior under compression in the most appropriate direction are investigated. The findings of this study provide a basis for future experimental investigations and the potential application of such cellular solids for energy-absorbing purposes.     

Physics-Informed Multistage Machine Learning Strategy for the Nanomachining-Induced Plastic Deformation Behavior

The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high-throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one-step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low-cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high-speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high-quality products.