[C3mim][NTf2]/DEC/[Li][NTf2]体系的基础性质

本文通过传统方法合成了1-丙基-3-甲基咪唑双三氟甲基磺酸亚胺疏水型离子液体,在离子液体中加入碳酸二乙酯和双三氟甲基磺酸亚胺锂盐制备了八个体系。通过差式扫描量热仪考察了上述体系的热力学性质。考察了体系的半稳定态温度随着锂盐的加入后的变化情况,以及玻璃化温度随离子液体与碳酸二乙酯的比例的变化情况。测量结果并没有观测到体系的熔点温度,表明碳酸二乙酯和锂盐能够在较低的温度下溶解在离子液体中。系统研究了不同温度下八个体系的基础性质,如:锂离子([Li]^+)自扩散系数随碳酸二乙酯浓度的变化规律,密度、粘度和电导率随温度的变化规律,利用Vogel Fulcher Tamman (VFT),Final Vogel Fulcher Tamman (FVFT)和Arrhenius方程计算得到八个体系的粘度活化能、电导活化能等。在性质实验的基础上,通过分子动力学模拟讨论了[Li]^+与[NTf2]^-/碳酸二乙酯之间的相互作用情况,及碳酸二乙酯的引入对Li NTf_2/离子液体体系的微观结构影响,其中阴离子[NTf2]^-通过氧原子与锂离子之间发生相互作用。通过径向分布函数和对[Li]^+周围O原子的配位数的分析表明,碳酸二乙酯的引入,削弱了[Li]^+与[NTf2]^-之间的相互作用。因此,碳酸二乙酯的引入有利于[Li]^+的扩散,该结论与实验结果相符。

Effect of Sn addition on the mechanical properties and bio-corrosion behavior of cytocompatible Mg-4Zn based alloys

The mechanical properties and bio-corrosion behaviors of as-extruded Mg-4Zn alloys after Sn addition were investigated,systemati-cally.A small amount of Sn addition to Mg-4Zn alloy slightly improved the mechanical properties for solid solution strengthening,and significantly controlled the bio-corrosion rates.Sn participating in the outer layer film formation as SnO/SnO_(2)resisted the bio-corrosion proceeding.Especially,Mg-4Zn-1.5Sn alloy,with a weight loss rate of 0.45 mm/y and hydrogen evolution rate of 0.099 mL/cm^(2)/day,showed cytotoxicity grade of 0 to MC3T3-E1 cells.The perfect alliance of cytocompatibility,suitable mechanical properties and low bio-corrosion rate demonstrates that this Mg-4Zn-1.5Sn alloy is a promising biodegradable magnesium alloy for orthopedic implants.

Distributed Optimization Algorithm for Multi-Robot Formation with Virtual Reference Center

Dear editor,In this letter,we use a distributed optimization approach to solve a class of multi-robot formation problem with virtual reference center.We investigate the design and analysis of the constrained consensus algorithm to solve the optimization problem with a sum of objective functions with some local constraints.In the multi-robot system with virtual reference center,each robot has messages on its own constraints and objective function,as well as the message about the formation that interacts with the virtual reference center.At the same time,all the robots collaborate to find the minimum value of the function defined by the formation.To find the optimal formation,we propose an algorithm with fixed step size with better performance.In addition,we use a combination of the Hungarian assignment algorithm and the proposed formation algorithm to get the optimal matching formation of the multi-robot system.

Thinking on Orientation of Engineering Research of China's Sorghum Industry

To solve engineering problems restricting development of China's sorghum industry in the whole chain,this paper firstly introduces functions of sorghum industry to national economic and social development.Then,it analyzes current situations of engineering research of sorghum industry.Finally,it discusses countermeasures for engineering research of sorghum industrial development.On the basis of current situations,it proposes 7 pertinent countermeasures.(i)Collection and storage of sorghum germplasm resource;(ii)Innovation on germplasm resource for sorghum breeding;(iii)Seed selection for new variety of special sorghum;(iv)Integrated innovation on high yield,high quality and high efficient cultivation technology;(v)Research and development of integrated prevention and control technology for disease,pests and weeds;(vi)Improvement in technological extension service system;(vii)Research of sorghum deep processing and use technology.It is intended to promote rapid,sustainable and healthy development of sorghum industry in China.

Intellectual property protection for deep semantic segmentation models

Deep neural networks have achieved great success in varieties of artificial intelligent fields. Since training a good deep model is often challenging and costly, such deep models are of great value and even the key commercial intellectual properties. Recently, deep model intellectual property protection has drawn great attention from both academia and industry, and numerous works have been proposed. However, most of them focus on the classification task. In this paper, we present the first attempt at protecting deep semantic segmentation models from potential infringements. In details, we design a new hybrid intellectual property protection framework by combining the trigger-set based and passport based watermarking simultaneously. Within it, the trigger-set based watermarking mechanism aims to force the network output copyright watermarks for a pre-defined trigger image set, which enables black-box remote ownership verification. And the passport based watermarking mechanism is to eliminate the ambiguity attack risk of trigger-set based watermarking by adding an extra passport layer into the target model. Through extensive experiments, the proposed framework not only demonstrates its effectiveness upon existing segmentation models, but also shows strong robustness to different attack techniques.

Demonstration of a diode-pumped dual-wavelength metastable krypton laser

Diode-pumped rare gas lasers are potential candidates for high-energy and high-beam quality laser systems.Currently,most investigations are focused on metastable Ar lasers.The Kr system has the unique advantages of higher quantum efficiency and lower discharge requirements for comparison.In this paper,a diode-pumped metastable Kr laser was demonstrated for the first time.Using a repetitively pulsed discharge at a Kr/He pressure of up to approximately1500 Torr,metastable Kr atoms of more than 10^(13)cm^(-3)were generated.Under diode pumping,the laser realized a dual-wavelength output with an average output power of approximately 100 mW and an optical conversion efficiency of approximately 10% with respect to the absorbed pump power.A kinetics study involving population distribution and evolution was conducted to analyze the laser performance.

3D Single Object Tracking with Multi-View Unsupervised Center Uncertainty Learning

Center point localization is a major factor affecting the performance of 3D single object tracking.Point clouds themselves are a set of discrete points on the local surface of an object,and there is also a lot of noise in the labeling.Therefore,directly regressing the center coordinates is not very reasonable.Existing methods usually use volumetric-based,point-based,and view-based methods,with a relatively single modality.In addition,the sampling strategies commonly used usually result in the loss of object information,and holistic and detailed information is beneficial for object localization.To address these challenges,we propose a novel Multi-view unsupervised center Uncertainty 3D single object Tracker(MUT).MUT models the potential uncertainty of center coordinates localization using an unsupervised manner,allowing the model to learn the true distribution.By projecting point clouds,MUT can obtain multi-view depth map features,realize efficient knowledge transfer from 2D to 3D,and provide another modality information for the tracker.We also propose a former attraction probability sampling strategy that preserves object information.By using both holistic and detailed descriptors of point clouds,the tracker can have a more comprehensive understanding of the tracking environment.Experimental results show that the proposed MUT network outperforms the baseline models on the KITTI dataset by 0.8%and 0.6%in precision and success rate,respectively,and on the NuScenes dataset by 1.4%,and 6.1%in precision and success rate,respectively.The code is made available at https://github.com/abchears/MUT.git.

Improving in vitro and in vivo corrosion resistance and biocompatibility of Mg-1Zn-1Sn alloys by microalloying with Sr

Magnesium(Mg)and its alloys have attracted attention as potential biodegradable materials in orthopedics due to their mechanical and physical properties,which are compatible with those of human bone.However,the effect of the mismatch between the rapid material degradation and fracture healing caused by the adverse effect of hydrogen(H2),which is generated during degradation,on surrounding bone tissue has severely restricted the application of Mg and its alloys.Thus,the development of new Mg alloys to achieve ideal degradation rates,H2 evolution and mechanical properties is necessary.Herein,a novel Mg-1Zn-1Sn-xSr(x=0,0.2,0.4,and 0.6 wt%)quaternary alloy was developed,and the microstructure,mechanical properties,corrosion behavior and biocompatibility in vitro/vivo were investigated.The results demonstrated that a minor amount of strontium(Sr)(0.2 wt%)enhanced the corrosion resistance and mechanical properties of Mg-1Zn-1Sn alloy through grain refinement and second phase strengthening.Simultaneously,due to the high hydrogen overpotential of tin(Sn),the H2 release of the alloys was significantly reduced.Furthermore,Sr-containing Mg-1Zn-1Sn-based alloys significantly enhanced the viability,adhesion and spreading of MC3T3-E1 cells in vitro due to their unique biological activity and the ability to spontaneously form a network structure layer with micro/nanotopography.A low corrosion rate and improved biocompatibility were also maintained in a rat subcutaneous implantation model.However,excessive Sr(>0.2 wt%)led to a microgalvanic reaction and accelerated corrosion and H2 evolution.Considering the corrosion resistance,H2 evolution,mechanical properties and biocompatibility in vitro and in vivo,Mg-1Zn-1Sn-0.2Sr alloy has tremendous potential for clinical applications.

Face image retrieval based on shape and texture feature fusion

Humongous amounts of data bring various challenges to face image retrieval. This paper proposes an efficient method to solve those problems. Firstly,we use accurate facial landmark locations as shape features. Secondly, we utilise shape priors to provide discriminative texture features for convolutional neural networks. These shape and texture features are fused to make the learned representation more robust.Finally, in order to increase efficiency, a coarse-tofine search mechanism is exploited to efficiently find similar objects. Extensive experiments on the CASIAWeb Face, MSRA-CFW, and LFW datasets illustrate the superiority of our method.

Real-time manifold regularized context-aware correlation tracking

Despite the demonstrated success of numerous correlation filter(CF)based tracking approaches,their assumption of circulant structure of samples introduces significant redundancy to learn an effective classifier.In this paper,we develop a fast manifold regularized context-aware correlation tracking algorithm that mines the local manifold structure information of different types of samples.First,different from the traditional CF based tracking that only uses one base sample,we employ a set of contextual samples near to the base sample,and impose a manifold structure assumption on them.Afterwards,to take into account the manifold structure among these samples,we introduce a linear graph Laplacian regularized term into the objective of CF learning.Fortunately,the optimization can be efficiently solved in a closed form with fast Fourier transforms(FFTs),which contributes to a highly efficient implementation.Extensive evaluations on the OTB100 and VOT2016 datasets demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms in terms of accuracy and robustness.Especially,our tracker is able to run in real-time with 28 fps on a single CPU.

Entropy-modulated and interlayer-doped transition metal layered oxides enable high-energy-density sodium-ion capacitors

In recent years,sodium-ion capacitors have attracted attention due to their cost-effectiveness,high power density and similar manufacturing process to lithium-ion capacitors.However,the utilization of oxide electrodes in traditional sodium-ion capacitors restricts their further advancement due to the inherent low operating voltage and electrolyte consumption based on their energy storage mechanism.To address these challenges,we incorporated Zn,Cu,Ti,and other elements into Na_(0.67)Ni_(0.33)Mn_(0.67)O_(2) to synthesize P2-type Na_(0.7)Ni_(0.28)Mn_(0.6)Zn_(0.05)Cu_(0.02)Ti_(0.05)O_(2) with a modulated entropy and pillaring Zn.Through the synergistic interplay between the interlayer pillar and the entropy modulation within the layers,the material exhibits exceptional toughness,effectively shielding it from detrimental phase transitions at elevated voltage regimes.As a result,the material showcases outstanding kinetic properties and long-term cycling stability across the voltage range.By integrating these materials with hierarchical porous carbon nanospheres to form a"rocking chair"sodium-ion capacitor,the hybrid full device delivers a high energy density(171 Wh·kg^(-1))and high power density(5245 W·kg^(-1)),as well as outstanding cycling stability(77% capacity retention after 3000 cycles).This work provides an effective material development route to realize simultaneously high energy and power for next-generation sodium-ion capacitors.