Highly ion-conductive solid polymer electrolyte (SPE) based on polyethylene (PE) non-woven matrix is prepared by filling poly(ethylene glycol) (PEG)-based crosslinked electrolyte inside the pores of the non-woven matrix. The PE non-woven matrix not only shows good mechanical strength for SPE to be a free-standing film, but also has very porous structure for high ion conductivity. The ion conductivity of SPE based on PE non-woven matrix can be enhanced by adding sufficient non-volatile plasticizer such as poly(ethylene glycol) dimethyl ether (PEGDME) into ion conduction phase without sacrificing mechanical strength. SPE with 20 wt.% crosslinking agent and 80 wt.% non-volatile plasticizer shows 3.1 × 10−4 S cm−1 at room temperature (20 °C), to our knowledge, which is the highest level for SPEs. It is also electrochemically stable up to 5.2 V and has high transference number about 0.52 due to the introduction of anion receptor as an additive. The interfacial resistance between Li electrode and SPE is low enough to perform charge/discharge test of unit cell consisting of LiCoO2/SPE/Li at room temperature. The discharge capacity of the unit cell shows 87% of theoretical value with 86% Coulombic efficiency. 相似文献
A polycrystalline high-density magnesium fluoride, fabricated into plates or shapes by hot-pressing, exhibits high in-line transmittance from 2.5 to 6.0 m, and single-crystal magnesium fluoride extends from 0.1 to 6.0 m. The ultimate and practical transmittance of hot-pressed magnesium fluoride using intrinsic and extrinsic reflectance, absorptance and scattering mechanisms, are investigated. The intrinsic scattering mechanism due to the polycrystalline structure is basically responsible for the tremendous difference in transmittance in the short wavelength region of the spectrum. The in-line transmittance of polycrystalline and singlecrystal MgF2 is discussed in terms of sample thickness. 相似文献
Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.
In the post-genomic era, proteomics has achieved significant theoretical and practical advances with the development of high-throughput technologies. Especially the rapid accumulation of protein-protein interactions (PPIs) provides a foundation for constructing protein interaction networks (PINs), which can furnish a new perspective for understanding cellular organizations, processes, and functions at network level. In this paper, we present a comprehensive survey on three main characteristics of PINs: centrality, modularity, and dynamics. 1) Different centrality measures, which are used to calculate the importance of proteins, are summarized based on the structural characteristics of PINs or on the basis of its integrated biological information; 2) Different modularity definitions and various clustering algorithms for predicting protein complexes or identifying functional modules are introduced; 3) The dynamics of proteins, PPIs and sub-networks are discussed, respectively. Finally, the main applications of PINs in the complex diseases are reviewed, and the challenges and future research directions are also discussed. 相似文献
The Cd−Zn system has been thermodynamically reassessed with the CALPHAD method by combining more recent experimental data,
in particular the activities of zinc in the liquid phase. A good agreement is obtained between the calculated and experimental
thermodynamic parameters as well asphase boundaries. 相似文献