内函数逼近定理及上溢原理的推广及应用

在κ-饱和的非标准模型中,首先推广了内函数逼近定理,并用这一推广定理证明了著名的A sco li定理;其次将上(下)溢原理推广到一般的定向集上,并证明了拓扑空间中单子的一些性质,给出了无穷小延伸定理的一个简单证明.     

PW11Cu/TiO2膜光催化剂的制备及可见光催化性能

以PW₁₁Cu为可见光活性组分,TiO₂为载体结构组分,采用溶胶-凝胶法制备了PW₁₁Cu/TiO₂复合膜可见光催化剂,并用UV-Vis DRS、IR、Raman、XRD、SEM、TEM等手段对催化剂的光吸收性质、化学组成、晶相、表面结构和形貌进行了表征,同时,以模型污染物RhB的可见光降解为探针评估了它的光催化活性,考察了膜处理温度、PW₁₁Cu含量和溶液酸性对催化活性的影响,最后,通过催化剂循环降解RhB试验评估了PW₁₁Cu/TiO₂膜的稳定性。实验结果表明,PW₁₁Cu/TiO₂膜对可见光有明显吸收,低温(100℃)处理的膜为无定形态,高温(500℃)处理的膜为多晶态;低温处理的膜具有较高的可见光催化活性,用于RhB的可见光催化降解,在中性条件下反应80 min,RhB的降解率为100%,TOC去除达32%(4 h);提高溶液酸性有利于催化剂活性的提高,在pH=2.5的条件下,达到100%的RhB降解仅需30 min。在本实验条件下,PW₁₁Cu的最佳剂量是3.0 g。经过10次循环降解RhB,催化剂的光催化活性仍保留约90%。     

原子吸收光谱法测定烟叶中的重金属总量及形态分析

采用火焰原子吸收光谱法测定烟叶中Mn,Cu,Zn元素,石墨炉原子吸收光谱法测定Cd,Cr,Pb元素,以HNO3-H2O2微波消解法获取烟叶中的重金属总量,以超声水提取法获取水溶态重金属进行初级形态分析,以Tessier逐级提取法获取5种形态的重金属进行次级形态分析。实验结果表明,原子吸收光谱法检测6种重金属线性良好,相关系数(r2)不低于0.998 8,检出限为0.16~3.1μg·L-1。烟叶样品中Mn,Cu,Zn,Cd元素主要以可交换态和碳酸盐结合态的形式存在;Cr主要以残渣态的形式存在;而Pb主要以碳酸盐结合态和残渣态的形式存在。     

利用基于Gouy-Chapman模型的离子有效电荷定量表征离子特异性效应

离子特异性效应在固-液界面反应中是普遍存在的. 近期研究指出, 在较低电解质浓度的某些体系中, 离子特异性效应可能并非来源于色散力、经典诱导力、离子半径或水合半径的大小等, 而是界面附近强电场中的离子极化作用. 这种作用可使界面附近的吸附态反号离子被强烈极化(高达经典极化的10⁴倍). 强烈极化的结果将导致离子在界面附近受到的库仑力远远超过离子电荷所能产生的库仑力, 这体现在离子的有效电荷将远大于离子的实际电荷. 因此胶体体系中基于这种强极化的离子有效电荷可以用来定量表征离子特异性效应的强度. 本研究在蒙脱石-胡敏酸混合悬液凝聚过程中发现了Na⁺、K⁺、Ca²⁺、Cu²⁺四种离子的离子特异性效应, 提出了基于激光散射技术测定离子有效电荷的方法, 并成功获得了被强烈极化后的离子有效电荷数值. 实验测得的Na⁺、K⁺、Ca²⁺、Cu²⁺四种离子的有效电荷值分别为: Z_{Na(effective)}=1.46, Z_K(effective)=1.86, Z_{Ca(effective)}=3.92, Z_{Cu(effective)}=6.48.该结果表明: (1) 离子在强电场中的极化将大大提高离子的有效电荷, 从而极大地增强离子所受的库仑作用力;(2) 离子的电子层数越多, 离子极化越强烈, 离子的有效电荷增加越多.     

一元强碱滴定一元弱酸溶液的林邦滴定曲线方程及其应用

以林邦副反应思想为基础,用质子条件式PBE、物料平衡式MBE、滴定分数和条件稳定常数等知识推导出一元强碱滴定一元弱酸溶液的林邦滴定曲线方程,并用滴定曲线方程讨论了滴定开始至化学计量点时任意一点p H的计算。     

气体节流膨胀过程的热力学分析

探讨气体节流膨胀过程中的熵(S)的变化,指出任何气体的节流膨胀都是熵增过程,并且是不可逆的。在此基础上,讨论和总结了节流膨胀过程的全部热力学特征。     

Natural and artificial light-harvesting systems utilizing the functions of carotenoids

Carotenoids are essential pigments in natural photosynthesis. They absorb in the blue–green region of the solar spectrum and transfer the absorbed energy to (bacterio-)chlorophylls, and so expand the wavelength range of light that is able to drive photosynthesis. This process is an example of singlet–singlet energy transfer and so carotenoids serve to enhance the overall efficiency of photosynthetic light reactions. Carotenoids also act to protect photosynthetic organisms from the harmful effects of excess exposure to light. In this case, triplet–triplet energy transfer from (bacterio-)chlorophyll to carotenoid plays a key role in this photoprotective reaction. In the light-harvesting pigment–protein complexes from purple photosynthetic bacteria and chlorophytes, carotenoids have an additional role, namely the structural stabilization of those complexes. In this article we review what is currently known about how carotenoids discharge these functions. The molecular architecture of photosynthetic systems will be outlined to provide a basis from which to describe the photochemistry of carotenoids, which underlies most of their important functions in photosynthesis. Then, the possibility to utilize the functions of carotenoids in artificial photosynthetic light-harvesting systems will be discussed. Some examples of the model systems are introduced.     

运用系统动力学模型预测稀土战略储备规模

基于全球稀土资源的供需形势和中国稀土资源减少的趋势,运用系统动力学的模型来预测未来17年我国稀土资源储备量。选取了2000~2013的生产和消费数据,通过定量模拟计算中国稀土供需差波动系数,设计了稀土资源供需差波动范围分别为0%,20%的两个方案,模拟计算了两个方案的年储备量、累计储备量和储备量绝对值。方案一范围是(-0%,+0%),仿真预测出稀土未来17年累积储备量为720551 t,即战略储备规模达到720551 t,该方案下不论供需差发生如何波动,国家都会进行储备或释放调节;方案二范围是(-20%,+20%),预测出未来17年战略储备规模为461331 t,国家只需在这个范围外进行收储释放调节,从而调节市场供求,降低价格波动幅度。本文从不同供需差的波动系数来模拟计算储备量,目的是为国家相关部门在进行稀土储备决策时提供参考,可以根据我国经济景气状况和供需形势,以及稀土产业发展状况,进行不同规模的稀土储备。     

Tutorial review on validation of liquid chromatography–mass spectrometry methods: Part I

This is the part I of a tutorial review intending to give an overview of the state of the art of method validation in liquid chromatography mass spectrometry (LC–MS) and discuss specific issues that arise with MS (and MS/MS) detection in LC (as opposed to the “conventional” detectors). The Part I briefly introduces the principles of operation of LC–MS (emphasizing the aspects important from the validation point of view, in particular the ionization process and ionization suppression/enhancement); reviews the main validation guideline documents and discusses in detail the following performance parameters: selectivity/specificity/identity, ruggedness/robustness, limit of detection, limit of quantification, decision limit and detection capability. With every method performance characteristic its essence and terminology are addressed, the current status of treating it is reviewed and recommendations are given, how to determine it, specifically in the case of LC–MS methods.     

Modeling hydrate formation conditions in the presence of electrolytes and polar inhibitor solutions

In this paper, a new predictive model is proposed for prediction of gas hydrate formation conditions in the presence of single and mixed electrolytes and solutions containing both electrolyte and a polar inhibitor such as monoethylene glycol (MEG), diethylene glycol (DEG) and triethylene glycol (TEG). The proposed model is based on the γ–φ approach, which uses modified Patel–Teja equation of state (VPT EOS) for characterizing the vapor phase, the solid solution theory by van der Waals and Platteeuw for modeling the hydrate phase, the non-electrolyte NRTL-NRF local composition model and Pitzer–Debye–Huckel equation as short-range and long-range contributions to calculate water activity in single electrolyte solutions. Also, the Margules equation was used to determine the activity of water in solutions containing polar inhibitor (glycols). The model predictions are in acceptable agreement with experimental data. For single electrolyte solutions, the model predictions are similar to available models, while for mixtures of electrolytes and mixtures of electrolytes and inhibitors, the proposed model gives significantly better predictions. In addition, the absolute average deviation of hydrate formation pressures (AADP) for 144 experimental data in solutions containing single electrolyte is 5.86% and for 190 experimental data in mixed electrolytes solutions is 5.23%. Furthermore, the proposed model has an AADP of 14.13%, 5.82% and 5.28% in solutions containing (Electrolyte + MEG), (Electrolyte + DEG) and (Electrolyte + TEG), respectively.