水热法制备纳米TiO2纤维及光催化降解亚甲基蓝的研究

采用水热法,以锐钛矿型TiO2粉体为前驱体,与10mol/L的KOH溶液反应,成功地制备出了TiO2纳米纤维。通过XRD,HRTEM,BET和FT-IR等手段对纳米纤维的微观形貌、晶型结构及化学组成进行表征。结果表明:80℃烘干条件下所得产物为三钛酸H2Ti3O7纳米纤维,纤维的长径比达到了50以上,且生长很均匀,热处理温度为550℃时完全转变为锐钛矿。在FT-IR图中,煅烧后的纤维的吸收峰的强度远小于煅烧前的纤维,Ti-O键增强。与TiO2粉体相比,纤维的比表面积大大增加,达到372.67m2/g。在紫外光条件下,TiO2纳米纤维的降解率达到92.3%,比TiO2纳米粉体高出近40%。     

计及效率特性的电-氢混合储能直流微网经济下垂控制策略

为实现电-氢混合储能微电网在孤岛状态下的自治经济运行,并降低系统运行对通信网络的依赖,提出一种计及效率特性的直流微网经济下垂控制策略。该控制策略充分考虑电-氢混合储能系统各子系统的工作特性,研究了系统运行与效率的关系,并构建了计及效率特性的电-氢混合储能直流微电网系统成本函数。再结合电-氢混合储能系统的互补工作模式,驱动各系统基于成本分散地实施电-氢混合储能系统的运行方案,进而提高直流微电网的自治经济性能。最后,通过RT-LAB实验平台开展实时仿真。结果表明：所提控制策略相较于传统经济下垂控制,能够实现燃料电池和电解槽高效率区间的稳定运行,验证了效率特性对经济运行的必要性;在一天的运行实验下,其运行成本相较于传统经济下垂控制降低了10.38%,验证了所提出策略的经济性。     

安徽省五河县金多金属矿床地质特征及找矿预测研究

安徽省五河县金多金属矿床位于蚌埠隆起区东段。研究区构造特征复杂,岩浆活动频繁,特殊的地质条件造成本区具有良好的金多金属矿成矿背景。为了进一步揭示本区域成矿特征,指导后期找矿工作,对该区域的矿床普查资料认真总结,同时结合区域物化探数据、矿石镜下及实验室分析进行分析。研究结果表明:五河县金矿床均定位于晚太古界五河群西堌堆组地层中;矿体形状复杂,分枝复合常见,层控特征显著。本区地层中Au、Ag、Cu、Pb、Zn、As、Sb等元素的背景含量均高于全省的平均含量。根据物化探异常、地层及岩性、北东向构造等特征将本区初步划分成四个找矿远景预测区;首次提出在今后找矿工作中加强对负磁异常分布范围的研究;五河县金矿床找矿预测具有"三位一体"指示成矿特征,即地层岩性、物化探异常和北东向容矿构造。     

Tandem Design of Functional Separators for Li Metal Batteries with Long-Term Stability and High-Rate Capability

The lithium (Li) dendrite growth seriously hinders the applications of lithium metal batteries (LMBs). Numerous methods have been proposed to restrict the formation of Li dendrites by improving the Li-ion transference number (t_Li⁺) through separator modification according to Sand's time equation. However, ignoring the positive contribution of anion motion to solid electrolyte interphase (SEI) formation will result in insufficient inorganic components, which impedes practical implementation of LMBs. Herein, a “tandem” separator is constructed (ZSM-5-Poly dimethyl diallyl ammonium chloride (PDDA)/Polyethylene (PE)/SbF₃), which anchored anions and built an inorganic-rich SEI at the same time. The resulting SEI from SbF₃ (SBF) coating on side facing Li is rich in Li-Sb alloy (Li₃Sb) and LiF. Li₃Sb can significantly reduce the migration energy barrier of Li ion (Li⁺) and facilitate Li⁺ transport. Simultaneously, ZSM-5-PDDA (Z5P) coating at the other side can effectively immobilize anions and increase the t_Li⁺. Moreover, the regular pore structure is conducive to homogenizing Li⁺ flux and also capable to uniform temperature distribution, significantly improving safety. Hence, the lifespan of Li|Li and Li|Cu cells assemble with Z5P/PE/SBF separator is significantly extended. In addition, full cells with LiNi_0.8Co_0.1Mn_0.1O₂ (NCM811) and LiFePO₄ (LFP) cathodes show excellent cycle stability and superior rate performance.     

Construction of hierarchical three-dimensional interspersed flower-like nickel hydroxide for asymmetric supercapacitors

Low-cost and easily obtainable electrode materials are crucial for the application of supercapacitors.Nickel hydroxides have recently attracted intensive attention owning to their high theoretical specific capacitance,high redox activity,low cost,and eco-friendliness.In this study,novel three-dimensional (3D) interspersed flower-like nickel hydroxide was assembled under mild conditions.When ammonia was used as the precipitant and inhibitor and CTAB was used as an exfoliation agent,the obtained exfoliated ultrathin Ni(OH)2 nanosheets were assembled into 3D interspersed flower-like nickel hydroxide.In this novel 3D structure,the ultrathin Ni(OH)2 nanosheets not only provided a large contact area with the electrolyte,reducing the polarization of the electrochemical reaction and providing more active sites,but also reduced the concentration polarization in the electrode solution interface.Consequently,the utilization efficiency of the active material was improved,yielding a high capacitance.The electrochemical performance was improved via promoting the electrical conductivity by mixing the as-synthesized Ni(OH)2 with carbon tubes (N-4-CNT electrode),yielding excellent specific capacitances of 2,225.1 F·g-1 at 0.5 A·g-1 in a three-electrode system and 722.0 F·g-1 at 0.2 A·g-1 in a two-electrode system.The N-4-CNT//active carbon (AC) device exhibited long-term cycling performance (capacitance-retention ratio of 111.4％ after 10,000 cycles at 5 A·g-1) and a high specific capacitance of 180.5 F·g-1 with a high energy density of 33.5 W·h·kg-1 and a power density of 2,251.6 W·kg-1.     

VSAN: A new visualization method for super-large-scale academic networks

As a carrier of knowledge, papers have been a popular choice since ancient times for documenting everything from major historical events to breakthroughs in science and technology. With the booming development of science and technology, the number of papers has been growing exponentially. Just like the fact that Internet of Things (IoT) allows the world to be connected in a flatter way, how will the network formed by massive academic papers look like? Most existing visualization methods can only handle up to hundreds of thousands of node size, which is much smaller than that of academic networks which are usually composed of millions or even more nodes. In this paper, we are thus motivated to break this scale limit and design a new visualization method particularly for super-large-scale academic networks (VSAN). Nodes can represent papers or authors while the edges means the relation (e.g., citation, coauthorship) between them. In order to comprehensively improve the visualization effect, three levels of optimization are taken into account in the whole design of VSAN in a progressive manner, i.e., bearing scale, loading speed, and effect of layout details. Our main contributions are two folded: 1) We design an equivalent segmentation layout method that goes beyond the limit encountered by state-of-the-arts, thus ensuring the possibility of visually revealing the correlations of larger-scale academic entities. 2) We further propose a hierarchical slice loading approach that enables users to observe the visualized graphs of the academic network at both macroscopic and microscopic levels, with the ability to quickly zoom between different levels. In addition, we propose a “jumping between nebula graphs” method that connects the static pages of many academic graphs and helps users to form a more systematic and comprehensive understanding of various academic networks. Applying our methods to three academic paper citation datasets in the AceMap database confirms the visualization scalability of VSAN in the sense that it can visualize academic networks with more than 4 million nodes. The super-large-scale visualization not only allows a galaxy-like scholarly picture unfolding that were never discovered previously, but also returns some interesting observations that may drive extra attention from scientists.     

EvolveKG: a general framework to learn evolving knowledge graphs

A great many practical applications have observed knowledge evolution, i.e., continuous born of new knowledge, with its formation influenced by the structure of historical knowledge. This observation gives rise to evolving knowledge graphs whose structure temporally grows over time. However, both the modal characterization and the algorithmic implementation of evolving knowledge graphs remain unexplored. To this end, we propose EvolveKG – a general framework that enables algorithms in the static knowledge graphs to learn the evolving ones. EvolveKG quantifies the influence of a historical fact on a current one, called the effectiveness of the fact, and makes knowledge prediction by leveraging all the cross-time knowledge interaction. The novelty of EvolveKG lies in Derivative Graph – a weighted snapshot of evolution at a certain time. Particularly, each weight quantifies knowledge effectiveness through a temporarily decaying function of consistency and attenuation, two proposed factors depicting whether or not the effectiveness of a fact fades away with time. Besides, considering both knowledge creation and loss, we obtain higher prediction accuracy when the effectiveness of all the facts increases with time or remains unchanged. Under four real datasets, the superiority of EvolveKG is confirmed in prediction accuracy.