Walnut-inspired microsized porous silicon/graphene core–shell composites for high-performance lithium-ion battery anodes

Silicon is considered an exceptionally promising alternative to the most commonly used material, graphite, as an anode for next-generation lithium-ion batteries, as it has high energy density owing to its high theoretical capacity and abundant storage. Here, microsized walnut-like porous silicon/reduced graphene oxide (P-Si/rGO) core–shell composites are successfully prepared via in situ reduction followed by a dealloying process. The composites show specific capacities of more than 2,100 mAh·g^?1 at a current density of 1,000 mA·g^?1, 1,600 mAh·g^?1 at 2,000 mA·g^?1, 1,500 mAh·g^?1 at 3,000 mA·g^?1, 1,200 mAh·g^?1 at 4,000 mA·g^?1, and 950 mAh·g^?1 at 5,000 mA·g^?1, and maintain a value of 1,258 mAh·g^?1 after 300 cycles at a current density of 1,000 mA·g^?1. Their excellent rate performance and cycling stability can be attributed to the unique structural design: 1) The graphene shell dramatically improves the conductivity and stabilizes the solid–electrolyte interface layers; 2) the inner porous structure supplies sufficient space for silicon expansion; 3) the nanostructure of silicon can prevent the pulverization resulting from volume expansion stress. Notably, this in situ reduction method can be applied as a universal formula to coat graphene on almost all types of metals and alloys of various sizes, shapes, and compositions without adding any reagents to afford energy storage materials, graphene-based catalytic materials, graphene-enhanced composites, etc.

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生物模板法合成锂离子电池电极材料研究进展

锂离子电池是一类极具潜力的新型二次化学储能器件,被广泛应用于便携式电子设备、电动交通工具和智能电网等领域。高性能电极材料的设计和合成是获得高能量密度、长循环寿命、高安全性锂离子电池的关键。文章针对锂离子电池电极材料存在制备工艺复杂、结构难以控制、活性物质利用率低、循环稳定性和倍率性能差等问题,从生物资源高效利用角度出发,结合生物材料尺寸均匀、形态多变、结构精密、环境友好等优点,综述了生物模板法合成锂离子电池电极材料的研究进展,并对该领域的发展方向进行了展望。     

B掺杂g-C3N4的一步合成及可见光下降解RhB和TC研究

为研究非金属离子掺杂对g-C₃N₄光催化性能的影响,以三聚氰胺和硼酸为前驱体,采用一步煅烧法制备了B掺杂g-C₃N₄光催化剂。罗丹明B(RhB)的可见光降解实验表明,当三聚氰胺和硼酸的添加比例(质量比)为10∶0.05(0.05BCN)时显示出最好的光催化性能,表现为光照RhB 30 min降解率高达100%,远高于纯g-C₃N₄(38%)。同时,四环素(TC)降解9 min达到100%,降解速率为纯g-C₃N₄的2.09倍。基于结构表征和光学性能测量,高光催化性能可归因于B原子掺杂替代引起的带隙调制。B掺杂不仅减小了带隙且可能在带隙中引入杂质态能级,这些都能导致可见光吸收的增强和光生载流子复合的抑制,从而大大提高了光催化性能。本工作提供了一种原子级水平获取非金属元素修饰g-C₃N₄纳米片的方法,该材料可作为一种在可见光下具有良好稳定性的RhB降解光催化剂。     

基于Mega169的空调控制器的设计

采用单片机技术,设计了一种车用空调控制器.控制器可对空调的各个部件进行控制,实现制冷制热、调整风量及控制风口的变换等操作.系统采用液晶显示屏显示,由程序控制实现各个环节的功能,方便了汽车驾驶员的操作.该设计具有很高的实用价值,可适用于现有的大多数汽车以及工程车.     

氡及其子体运移仿真系统开发

基于.net开发平台,借助C讨论了仿真系统的软件实现过程,简述了系统的功能模块及关键技术实现.实现了氡及其子体运移机制研究的计算机化.     

Predication based immune network for multimodal function optimization

For the problem of indeterminate direction of local search, lacking of efficient regulation mechanism between local search and global search and regenerating new antibodies randomly in the original optimization version of artificial immune network (opt-aiNet), this paper puts forward a novel predication based immune network (PiNet) to solve multimodal function optimization more efficiently, accurately and reliably. The algorithm mimics natural phenomenon in immune system such as clonal selection, affinity maturation, immune network, immune memory and immune predication. The proposed algorithm includes two main features with opt-aiNet. The information of antibodies in continuous generations is utilized to point out the direction of local search and to adjust the balance between local and global search. PiNet also employs memory cells to generate new antibodies with high affinities. Theory analysis and experiments on 10 widely used benchmark problems show that when compared with opt-aiNet method, PiNet algorithm is capable of improving search performance significantly in successful rate, convergence speed, search ability, solution quality and algorithm stability.