A Dilute Fluorinated Phosphate Electrolyte Enables 4.9 V-Class Potassium Ion Full Batteries

Potassium ion batteries using graphite anode and high-voltage cathodes are considered to be optimizing candidates for large-scale energy storage. However, the lack of suitable electrolytes significantly hinders the development of high-voltage potassium ion batteries. Herein, a dilute (0.8 m ) fluorinated phosphate electrolyte is proposed, which exhibits extraordinary compatibility with both graphite anode and high-voltage cathodes. The phosphate solvent, tris(2,2,2-trifluoroethyl) phosphate (TFP), has weak solvating ability, which not only allows the formation of robust anion-derived solid electrolyte interphase on graphite anode but also effectively suppresses the corrosion of Al current collector at high voltage. Meanwhile, the high oxidative stability of fluorinated TFP solvent enables stable ultrahigh-voltage (4.95 V) cycling of a potassium vanadium fluorophosphate (KVPO₄F) cathode. Using TFP-based electrolyte, the 4.9 V-class potassium ion full cell based on graphite anode and KVPO₄F cathode shows rather remarkable cycling performance with a high capacity retention of 87.2% after 200 cycles. This study provides a route to develop dilute electrolytes for high-voltage potassium ion batteries, by utilizing solvents with both weak solvating ability and high oxidative stability.     

Face anti-spoofing based on multi-modal and multi-scale features fusion

Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network (CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion ( MMFF) is proposed. Specifically, first residual network ( Resnet )-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network (FPN), finally squeeze-and-excitation fusion ( SEF) module and self-attention network ( SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods.     

Estimating potential disaster waste generation for pre-disaster waste management

The paper discusses development of a method for estimating disaster waste that can be potentially generated by a natural disaster in the future for pre-disaster waste management. In particular, this research focuses on micro-disaster waste originating from household consumer durables. We documented the number of household consumer durables and built a mass per unit database of major consumer durables using web-based and statistical surveys. We also estimated present and future figures of disaster waste that can be generated in the study area. The estimated total amount of disaster waste that could be generated in 2015 was 24.1 kt (18.1–29.8 kt) and 108 kt (81.8–133 kt) for Kobe City and Ise-Shima region, respectively. The total quantities of TV sets, air conditioners, refrigerators, and washing machines generated in Kobe and Ise-Shima will range between 10.9 and 22.8 kt (247,000–545,000 units) in 2015, and 10.7 kt to 22.8 kt (249,000–550,000 units) in 2035. The quantity estimated for 2015 is equal to 61 % of the annual processing capacity of Plant A, Japan’s leading home appliances recycling plant. Finally, we discussed the contribution of the estimation results and geographic information systems in future recycling planning.     

Ultra-dense planar metallic nanowire arrays with extremely large anisotropic optical and magnetic properties

A nanofabrication method for the production of ultra-dense planar metallic nanowire arrays scalable to wafer-size is presented. The method is based on an efficient template deposition process to grow diverse metallic nanowire arrays with extreme regularity in only two steps. First, III–V semiconductor substrates are irradiated by a low-energy ion beam at an elevated temperature, forming a highly ordered nanogroove pattern by a “reverse epitaxy” process due to self-assembly of surface vacancies. Second, diverse metallic nanowire arrays (Au, Fe, Ni, Co, FeAl alloy) are fabricated on these III–V templates by deposition at a glancing incidence angle. This method allows for the fabrication of metallic nanowire arrays with periodicities down to 45 nm scaled up to wafer-size fabrication. As typical noble and magnetic metals, the Au and Fe nanowire arrays produced here exhibited large anisotropic optical and magnetic properties, respectively. The excitation of localized surface plasmon resonances (LSPRs) of the Au nanowire arrays resulted in a high electric field enhancement, which was used to detect phthalocyanine (CoPc) in surface-enhanced Raman scattering (SERS). Furthermore, the Fe nanowire arrays showed a very high in-plane magnetic anisotropy of approximately 412 mT, which may be the largest in-plane magnetic anisotropy field yet reported that is solely induced via shape anisotropy within the plane of a thin film.

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'98 Escherichia coli SWISS-2DPAGE database update

The combination of two-dimensional polyacrylamide gel electrophoresis (2-D PAGE), computer image analysis and several protein identification techniques allowed the Escherichia coli SWISS-2DPAGE database to be established. This is part of the ExPASy molecular biology server accessible through the WWW at the URL address http://www.expasy.ch/ch2d/ch2d-top.html . Here we report recent progress in the development of the E. coli SWISS-2DPAGE database. Proteins were separated with immobilized pH gradients in the first dimension and sodium dodecyl sulfate-polyacrylamide gel electrophoresis in the second dimension. To increase the resolution of the separation and thus the number of identified proteins, a variety of wide and narrow range immobilized pH gradients were used in the first dimension. Micropreparative gels were electroblotted onto polyvinylidene difluoride membranes and spots were visualized by amido black staining. Protein identification techniques such as amino acid composition analysis, gel comparison and microsequencing were used, as well as a recently described Edman "sequence tag" approach. Some of the above identification techniques were coupled with database searching tools. Currently 231 polypeptides are identified on the E. coli SWISS-2DPAGE map: 64 have been identified by N-terminal microsequencing, 39 by amino acid composition, and 82 by sequence tag. Of 153 proteins putatively identified by gel comparison, 65 have been confirmed. Many proteins have been identified using more than one technique. Faster progress in the E. coli proteome project will now be possible with advances in biochemical methodology and with the completion of the entire E. coli genome.     

Die shape optimisation for net-shape accuracy in metal forming using direct search and localised response surface methods

In this paper, three direct search algorithms, i.e. a modified simplex, random direction search and enhanced Powell’s methods together with a new localised response surface method are presented and applied to solve die shape optimisation problems for achieving net-shape accuracy in metal forming processes. The main motivation is to develop efficient and easy to implement optimisation algorithms in metal forming simulations which often involve complex tool and workpiece interaction and coupled thermal and mechanical analysis. Three case studies are presented including a simple upsetting, a 2D blade forging and a forward extrusion problem. In all cases, the objective was to achieve net-shape accuracy of the formed parts, one important criterion for precision forming. C+ + programs were developed to implement these algorithms and to automatically integrate optimisation computation and forging simulation. The optimisation results from the three case problems show that direct search based methods especially the modified simplex and the localised response surface methods are computationally efficient and robust for net-shape forging and extrusion optimisation problems. It is also suggested that these methods can be used in more complex forging problems where die shape design and optimisation are essential for achieving net-shape accuracy.     

基于小波变换的非对称数字水印算法研究

针对多媒体信息中的视频流采用数字水印技术实现版权保护和防攻击时存在的一些问题,提出了采用基于小波变换的非对称数字水印算法,不仅能有效地保护多媒体信息中作者的版权,还能抵抗一些常见攻击以达到防止被篡改的目的,具有很强的鲁棒性和实用性.     

医疗监护系统中WSN节点的软件设计

提出医疗监护系统中无线传感器网络节点软件设计的一些关键技术,包括基于TinyOs系统的nesC语言开发技术、特殊应用场合的MAC层设计技术,以及节点的节能技术。通过实验手段,验证了采用该软件设计技术的医疗监护系统节点具有较高的数据交互可靠性和低功耗特性。     

基于IC卡支付的GPRS网络自动售货机的设计实现

针对目前自动售货机都存在的只能接受硬币和小额面值钞票的缺点,提出采用智能IC卡与GPRS网络通信技术相结合的方案,并在此基础上设计了基于IC卡支付的无线网络自动售货终端。它包含了自动售货机终端的硬件设计与软件设计两部分。采用MDB总线与自动售货机控制器通讯。实际应用证实了其可行性,在自动售货业有广泛的应用前景。