Quantitative Analysis of Berberine in Processed Coptis by Near-Infrared Diffuse Reflectance Spectroscopy

The near-infrared（NIR） diffuse reflectance spectroscopy was used to study the content of Berberine in the processed Coptis. The allocated proportions of Coptis to ginger, yellow liquor or Evodia rutaecarpa changed according to the results of orthogonal design as well as the temperature. For as withdrawing the full and effective information from the spectral data as possible, the spectral data was preprocessed through first derivative and multiplicative scatter correetion（MSC） according to the optimization results of different preprocessing methods. Firstly, the model was established by partial least squares（PLS）; the coefficient of determination（R2） of the prediction was 0.839, the root mean squared error of prediction（RMSEP） was 0.1422, and the mean relative error（RME） was 0.0276. Secondly, for reducing the dimension and removing noise, the spectral variables were highly effectively compressed via the wavelet transformation（WT） technology and the Haar wavelet was selected to decompose the spectral signals. After the wavelet coefficients from WT were input into the artificial neural network（ANN） instead of the spectra signal, the quantitative analysis model of Berberine in processed Coptis was established. The R^2 of the model was 0.9153, the RMSEP was 0.0444, and the RME was 0.0091. The values of appraisal index, namely R^2, RMSECV, and RME, indicate that the generalization ability and prediction precision of ANN are superior to those of PLS. The overall results show that NIR spectroscopy combined with ANN can be efficiently utilized for the rapid and accurate analysis of routine chemical compositions in Coptis. Accordingly, the result can provide technical support for the further analysis of Berberine and other components in processed Coptis. Simultaneously, the research can also offer the foundation of quantitative analysis of other NIR application.     

Zr掺杂对La（Ba）ZrxCo1-xO3-δ钙钛矿催化N2O分解性能的影响（英文）

采用溶胶凝胶法制备了Zr掺杂的钴基钙钛矿La(Ba)ZrxCo1-xO3-δ,并将其用于航天推进剂领域的高浓度N2O催化分解反应.发现Zr的引入明显提高了钙钛矿La(Ba)ZrxCo1-xO3-δ的催化活性,尤其是当Zr掺杂量分别为0.05和0.2时,LaZrxCo1-xO3-δ和BaZrxCo1-xO3-δ催化剂性能较为优异.应用N2物理吸附、X射线衍射、H2程序升温还原、O2程序升温脱附和氧脉冲吸附技术表征了Zr掺杂对La(Ba)ZrxCo1-xO3-δ催化剂的物化性质的影响.结果表明,Zr掺杂增大了钴基钙钛矿的比表面积,改善了晶格结构,从而提高了钴物种的还原及氧吸附脱附能力,因而催化剂上N2O分解活性增加.     

银纳米片的研究进展

金属纳米材料具有不同于宏观块体材料的特殊性质. 在银纳米结构中, 银纳米片因其独特的形貌依赖光学性质备受关注, 该特性已在离子检测、分子染色、表面增强拉曼光谱(SERS)、表面荧光增强、生物医学等领域显示了重要应用价值. 本文从银纳米片的制备方法入手, 首先综述了银纳米片的各种制备方法以及实验条件(如光照的波长、表面活性剂种类、还原剂种类)对产物形貌的影响. 其次介绍了银纳米片的奇特光学性质, 总结了银纳米片的几种重要生长机制, 最后介绍了银纳米片的应用价值, 并对银纳米片的研究前景做了展望.     

不同方法掺杂Au对纳米α-Fe2O3气敏性能的影响

分别采用共沉淀法、浸渍法、紫外辐照法制备了掺杂不同Au含量的α-Fe₂O₃纳米粉体, 并制作了旁热式厚膜型气敏元件. 用XRD、TG-DTA和TEM技术对纳米晶的晶型、晶粒大小及形貌进行了表征. 考察了掺杂方法、Au含量及焙烧温度对α-Fe₂O₃气敏性能的影响. 结果表明, 采用三种方法掺杂适量Au后, 都使α-Fe₂O₃的气敏性有了显著提高, 其中采用共沉淀法, 在400 ℃焙烧的Au质量分数为1.5％的α-Fe₂O₃的气敏性最佳.     

人因飞行事故诱发因素的分析与Bootstrap预测仿真

提出一套系统的人因飞行事故诱发因素的分析和Bootstrap预测仿真的方法。首先,在"瑞士奶酪"模型基础上构建空军某机种的人因飞行事故诱因分析分类体系;然后,在此基础上采用数据挖掘技术辨识重要的单诱因、两两诱因组合及诱发模式;最后,使用Bootstrap分析方法对辨识出的单诱因、两两诱因组合及诱发模式执行预测仿真,并据此制订出针对性强、易于操作的预防策略。研究成果可为指导和加强安全管理,保证飞行安全提供决策参考依据。     

基质固相分散高效液相色谱法检测河豚毒素

建立了河豚鱼肉中河豚毒素的基质固相分散萃取的提取方法,并采用高效液相色谱-二极管阵列检测器进行测定。通过样品前处理的优化,确立选取0.5g样品与2.0g阳离子交换(SCX)吸附剂混合研磨,并采用10mL 5%氨水:甲醇溶液(5:95,V/V)作为洗脱剂。经HPLC-PDA检测,结果表明,河豚毒素在1—100mg/L浓度范围内呈现良好的线性关系(R2=0.9990),检出限为3.8ng,定量下限为12.7ng;加标回收率为77.9%—108.1%,RSD为2.23%—5.45%。该方法操作简单、耗时少,符合河豚毒素分析检测的要求。