凝胶色谱/超高效液相色谱-串联质谱法测定饲料中16种脂溶性染料的研究

应用凝胶色谱/超高效液相色谱-串联三重四极杆质谱建立了饲料中16种脂溶性染料测定的新方法。样品经20 m L乙酸乙酯-环己烷(1∶1)提取,凝胶色谱系统净化后,以0.1%甲酸溶液-甲醇为流动相进行梯度洗脱,各脂溶性染料经C18柱得到良好分离。16种染料在2~100μg/L范围内线性关系良好,相关系数(r2)不小于0.99。其检出限为25μg/kg,定量下限为50μg/kg。饲料样品在低、中、高3个加标浓度下,16种脂溶性染料的回收率为66.4%~108.5%,结果表明方法适用于饲料中16种脂溶性染料的检测。该方法操作简单、准确、杂质干扰少。     

Intercalation of metals and silicon at the interface of epitaxial graphene and its substrates

Intercalations of metals and silicon between epitaxial graphene and its substrates are reviewed. For metal intercala- tion, seven different metals have been successfully intercalated at the interface of graphene/Ru（O001） and form different intercalated structures. Meanwhile, graphene maintains its original high quality after the intercalation and shows features of weakened interaction with the substrate. For silicon intercalation, two systems, graphene on Ru（O001） and on Ir（l I 1）, have been investigated. In both cases, graphene preserves its high quality and regains its original superlative properties after the silicon intercalation. More importantly, we demonstrate that thicker silicon layers can be intercalated at the interface, which allows the atomic control of the distance between graphene and the metal substrates. These results show the great potential of the intercalation method as a non-damaging approach to decouple epitaxial graphene from its substrates and even form a dielectric layer for future electronic applications.     

高效液相色谱-串联质谱法检测牛奶中头孢洛宁残留

建立了牛奶中头孢洛宁残留检测的高效液相色谱-串联质谱方法。1 g牛奶经乙腈沉淀蛋白质后,上清液于37 ℃水浴下氮气吹干,用1 mL甲醇-0.1%甲酸水溶液（3：7,v/v）复溶,正己烷除脂净化后检测。流动相为乙腈和0.1%甲酸水溶液,梯度洗脱,经C₁₈色谱柱分离,采用多反应监测正离子模式对头孢洛宁进行定性定量分析。采用基质匹配法对牛奶中头孢洛宁的含量进行标准校正,在2~200 μg/L范围内,头孢洛宁质量浓度与其对应峰面积的线性关系良好,相关系数>0.999。牛奶中加标样品的检出限（按S/N≥3计）为0.5 μg/kg,定量限（S/N≥10计）为2 μg/kg。在定量限、1/2最高残留限量、最高残留限量、2倍最高残留限量添加水平下,牛奶中头孢洛宁的平均回收率为78.5%~86.2%,日内相对标准偏差为1.5%~6.2%,日间相对标准偏差为2.9%~5.6%。该方法可用于牛奶中头孢洛宁的残留检测。     

LCH: A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks

Identifying influential nodes in complex networks is one of the most significant and challenging issues, which may contribute to optimizing the network structure, controlling the process of epidemic spreading and accelerating information diffusion. The node importance ranking measures based on global information are not suitable for large-scale networks due to their high computational complexity. Moreover, they do not take into account the impact of network topology evolution over time, resulting in limitations in some applications. Based on local information of networks, a local clustering H-index (LCH) centrality measure is proposed, which considers neighborhood topology, the quantity and quality of neighbor nodes simultaneously. The proposed measure only needs the information of first-order and second-order neighbor nodes of networks, thus it has nearly linear time complexity and can be applicable to large-scale networks. In order to test the proposed measure, we adopt the susceptible-infected-recovered (SIR) and susceptible-infected (SI) models to simulate the spreading process. A series of experimental results on eight real-world networks illustrate that the proposed LCH can identify and rank influential nodes more accurately than several classical and state-of-the-art measures.     

新型机械解理方法在二维材料研究中的应用

自从石墨烯被发现以来,机械解理技术已经成为制备高质量二维材料的重要方法之一,在二维材料本征物性的研究方面展现出了独特的优势.然而传统机械解理方法存在明显的不足,如制备效率低、样品尺寸小等,阻碍了二维材料领域的研究进展.近些年我们在机械解理技术方面取得了一系列的突破,独立发展了一套具有普适性的新型机械解理方法.这种新型机械解理方法的核心在于通过改变解理过程中的多个参数,增强层状材料与基底之间的范德瓦耳斯相互作用,从而提高单层样品的产率和面积.本文着重以石墨烯为例,介绍了该技术的过程和机理.相比于传统机械解理方法,石墨烯的尺寸从微米量级提高到毫米量级,面积提高了十万倍以上,产率大于95%,同时石墨烯依然保持着非常高的质量.这种新型机械解理方法具有良好的普适性,目前已经在包括MoS₂,WSe₂,MoTe₂,Bi2212等几十种材料体系中得到了毫米量级以上的高质量单层样品.更重要的是,在解理过程中,通过调控不同的参数,可以在层状材料中实现一些特殊结构的制备,如气泡、褶皱结构等,为研究这些特殊材料体系提供了重要的物质保障.未来机械解理技术还有很多值得深入研究的科学问题,该技术的突破将会极大地推动二维材料领域的研究进展.