离子液体水热法辅助合成稀土Ce掺杂ZnO纳米材料的制备及其应用研究

以1-甲基咪唑和氯代正丁烷为原料,合成1-丁基-3-甲基咪唑氯盐离子液体;以醋酸锌[Zn(Ac)2]、硫酸锌(ZnSO4)和氯化锌(ZnCl2)为锌源,在1-丁基-3-甲基咪唑氯盐离子液体和丙氨酸体系中与硝酸铈反应,经水热合成法制备得到Ce掺杂的纳米ZnO。采用扫描电子显微镜(SEM)、紫外-可见光吸收光谱(UV-Vis)、X射线衍射仪(XRD)、X射线光电子能谱分析(XPS)和红外光谱(FT-IR)对产品进行表征。以亚甲基蓝(MB)为目标降解物,采用UV-Vis检测,考察了Ce掺杂的纳米ZnO的光催化活性。研究表明,焙烧温度对光催化的晶体结构和光催化活性产生较大的影响;2%Ce/ZnO、焙烧温度为500℃、催化时间为30 min、亚甲基蓝用量0.05 g、pH值为10时降解率可达99.5%以上。     

荷花山铅锌矿与铜陵地区其它矿床成矿控制因素的对比研究

为了探讨荷花山铅锌矿控矿因素上与铜陵地区其它矿床异同，运用宏、微观相结合，地质观察研究同位素地球化学以及流体包裹体分析相结合的方法，通过地层岩性、地质构造、特定地质体及成矿流体的来源和性质等几个方面与成矿关系的比较，揭示了这种新类型矿床的控矿因素的特点是与岩浆关系不密切的低温热液、角砾岩控矿。得出了这种新类型矿床既具有铜陵地区其它矿床的控矿因素，同时也具有自己独特的控矿因素的结论。     

Development of Core-Shell Nanoparticle Drug Delivery Systems Based on Biomimetic Mineralization

Among various drug-delivery systems, core-shell nanoparticles have many advantages. Inspired by nature, biomimetic synthesis has emerged as a new strategy for making core-shell nanoparticles in recent years. Biomimetic mineralization is the process by which living organisms produce minerals based on biomolecule templating that leads to the formation of hierarchically structured organic–inorganic materials. In this minireview, we mainly focus on the synthesis of core-shell nanoparticle drug-delivery systems by biomimetic mineralization. We review various biomimetic mineralization methods for fabricating core-shell nanoparticles including silica-based, calcium-based and other nanoparticles, and their applications in drug delivery. We also summarize strategies for drug loading in the biomolecule-mineralized core-shell NPs. Current challenges and future directions are also discussed.     

机制砂混凝土耐磨性的主要影响因素分析及多因素计算模型

本文研究了砂类型、砂率、石粉含量和抗压强度对机制砂混凝土耐磨性的影响,建立了磨损量的多因素计算模型。结果表明:由于含石粉及具有更高的粗糙度和坚固性,石灰岩与辉绿岩机制砂制备的C30、C40混凝土耐磨性比河砂混凝土提高20%以上;在0.40~0.44范围内选取较低的砂率可获得较优的耐磨性;利用石粉含量为5%~11%(质量分数)的机制砂制备混凝土,石粉含量为9%时可获得最佳的混凝土耐磨性,微观分析表明此时混凝土密实度最佳;通过灰色系统理论确定了耐磨性影响因素的影响程度排序为:砂率R₃>压碎值R₂>粗糙度R₁>抗压强度R₅>石粉含量R₄>0.6;对比验证表明提出的混凝土磨损量多因素计算模型具有较高的预测精度和良好的适用性。     

Electrocatalytic Reduction of CO2 to Ethylene by Molecular Cu‐Complex Immobilized on Graphitized Mesoporous Carbon

The electrochemical reduction of carbon dioxide (CO₂) to hydrocarbons is a challenging task because of the issues in controlling the efficiency and selectivity of the products. Among the various transition metals, copper has attracted attention as it yields more reduced and C2 products even while using mononuclear copper center as catalysts. In addition, it is found that reversible formation of copper nanoparticle acts as the real catalytically active site for the conversion of CO₂ to reduced products. Here, it is demonstrated that the dinuclear molecular copper complex immobilized over graphitized mesoporous carbon can act as catalysts for the conversion of CO₂ to hydrocarbons (methane and ethylene) up to 60%. Interestingly, high selectivity toward C2 product (40% faradaic efficiency) is achieved by a molecular complex based hybrid material from CO₂ in 0.1 m KCl. In addition, the role of local pH, porous structure, and carbon support in limiting the mass transport to achieve the highly reduced products is demonstrated. Although the spectroscopic analysis of the catalysts exhibits molecular nature of the complex after 2 h bulk electrolysis, morphological study reveals that the newly generated copper cluster is the real active site during the catalytic reactions.     

D-limonene Inhibits Pentylenetetrazole-Induced Seizure via Adenosine A2A Receptor Modulation on GABAergic Neuronal Activity

Background: Epilepsy is a chronic neurological disorder characterized by the recurrence of seizures. One-third of patients with epilepsy may not respond to antiseizure drugs. Purpose: We aimed to examine whether D-limonene, a cyclic monoterpene, exhibited any antiseizure activity in the pentylenetetrazole (PTZ)-induced kindling mouse model and in vitro. Methods: PTZ kindling mouse model was established by administering PTZ (30 mg/kg) intraperitoneally to mice once every 48 h. We performed immunoblot blots, immunohistochemistry (IHC), and high-performance liquid chromatography (HPLC) analysis after the behavioral study. Results: An acute injection of PTZ (60 mg/kg) induced seizure in mice, while pretreatment with D-limonene inhibited PTZ-induced seizure. Repeated administration of PTZ (30 mg/kg) increased the seizure score gradually in mice, which was reduced in D-limonene (10 mg/kg)-pretreated group. In addition, D-limonene treatment increased glutamate decarboxylase-67 (GAD-67) expression in the hippocampus. Axonal sprouting of hippocampal neurons after kindling was inhibited by D-limonene pretreatment. Moreover, D-limonene reduced the expression levels of Neuronal PAS Domain Protein 4 (Npas4)-induced by PTZ. Furthermore, the adenosine A2A antagonist {"type":"entrez-protein","attrs":{"text":"SCH58261","term_id":"1052882304","term_text":"SCH58261"}}SCH58261 and ZM241385 inhibited anticonvulsant activity and gamma-aminobutyric acid (GABA)ergic neurotransmission-induced by D-limonene. Conclusion: These results suggest that D-limonene exhibits anticonvulsant activity through modulation of adenosine A2A receptors on GABAergic neuronal function.     

Collection efficiency of liquid‐based samplers for fungi in indoor air

This study assessed the collection efficiency (CE) of two popularly used sampling devices (BioSampler and Coriolis sampler) for fungal aerosols. Phosphate‐buffered saline (PBS) supplemented with or without surfactant (Tween‐20, Tween‐80, or Triton X‐100) and antifoam agent was prepared and used as collection liquids. The agar impactor (BioStage) was simultaneously operated with liquid‐based samplers to collect fungi from seven sites located at a university building, public library, and animal farming. Fungal concentrations determined by liquid samplers were divided by those by BioStage, and the ratio values represented CE. Results indicate that the CE of BioSampler was superior to that of Coriolis (P = 0.0001) and the PBS containing surfactant collected fungi better than that without surfactant (P < 0.0001), whereas antifoam agent showed no influence (P = 0.8). Moreover, fungal concentrations determined by BioSampler with surfactant‐added PBS were statistically indifferent from those by BioStage (P > 0.05) with a Spearman correlation coefficient of 0.81‐0.83 (P < 0.01). In addition to sampler and collection liquid, sampling location was also identified as a significant CE factor (P = 0.006), implying potential influences by fungal genera in the studied fields. Overall, BioSampler with surfactant‐supplemented PBS (eg, Triton X‐100) is recommended considering the great CE and compatibility with a variety of analytical assays.     

Predicting visual similarity between colour palettes

This work is concerned with the prediction of visual colour difference between pairs of palettes. In this study, the palettes contained five colours arranged in a horizontal row. A total of 95 pairs of palettes were rated for visual difference by 20 participants. The colour difference between the palettes was predicted using two algorithms, each based on one of six colour-difference formulae. The best performance (r² = 0.86 and STRESS = 16.9) was obtained using the minimum colour-difference algorithm (MICDM) using the CIEDE2000 equation with a lightness weighing of 2. There was some evidence that the order (or arrangement) of the colours in the palettes was a factor affecting the visual colour differences although the MICDM algorithm does not take order into account. Application of this algorithm is intended for digital design workflows where colour palettes are generated automatically using machine learning and for comparing palettes obtained from psychophysical studies to explore, for example, the effect of culture, age, or gender on colour associations.     

基于注意力机制与文本信息的用户关系抽取

随着社交媒体的发展,用户之间的关系网络对于社交媒体的分析有很大的帮助。因此,该文主要研究用户好友关系检测。以往的关于用户好友关系抽取的研究主要基于社交媒体上的结构化信息,比如其他好友关系,用户的不同属性等。但是,很多时候用户本身并没有大量的好友信息存在,同时也不一定有很多确定的属性。因此,我们希望基于用户发表的文本信息来对用户关系进行预测。不同于以往的潜在好友推荐算法,该文提出了一种基于注意力机制以及长短时记忆网络(long short-term memory,LSTM)的好友关系预测模型,将好友之间的评论分开处理,通过分析用户之间的评论来判断是否具备一定的好友关系。该模型将好友双方信息拼接后的结果作为输入,并将注意力机制应用于LSTM的输出。实验表明,用户之间的评论对于好友关系预测确实有较大的实际意义,该文提出的模型较之于多个基准系统的效果,取得了明显的提升。在不加入任何其它非文本特征的情况下,实验结果的准确率达到了77%。