金属材料分析(Ⅰ)

评述了2006～2007年两年间,金属材料分析领域的国内现状与进展概况.内容包括:称量分析法;滴定分析法;分子光谱分析(分光光度法、催化动力学分光光度法);原子光谱分析(原子吸收光谱法、原子荧光光谱法、原子发射光谱法、质谱分析法、X-射线荧光光谱法);电化学分析;金属中气体分析;原位分析;标准分析方法制定与标准物质(标准样品)研制;不确定度评定等在金属材料分析中的应用进展.     

金属材料分析(Ⅱ)

评述了2006～2007年两年间, 金属材料分析领域的国内现状与进展概况. 内容包括: 称量分析法;滴定分析法;分子光谱分析(分光光度法、催化动力学分光光度法);原子光谱分析(原子吸收光谱法、原子荧光光谱法、原子发射光谱法、质谱分析法、 X-射线荧光光谱法);电化学分析;金属中气体分析;原位分析;标准分析方法制定与标准物质(标准样品)研制;不确定度评定等在金属材料分析中的应用进展.参考文献420篇.     

《中国无机分析化学》征稿征订启事

<正>●技术交流的平台●无机分析的阵地●企业联系的桥梁●同行朋友的纽带《中国无机分析化学》是由北京矿冶研究总院主办的无机分析化学专业性学(技)术期刊。本刊包括岩矿分析、冶金分析、材料分析、环境分析、化工分析、生物医药分析、食品分析、仪器研制、综述评论、技术交流、信息之窗等栏目。读者对象为从事无机分析化学及相关技术的广     

德国分析界近讯

1　欧洲分析会议2 0 0 2年欧洲分析界的学术活动首先应是第 12届欧洲分析会议。本届会议于 2 0 0 2年 9月 8～ 13日在德国Ｄｏｒｔｍｕｎｄ市召开 ,由德国化学会分析化学学组主办 ,德国本生氏物理化学学会、Ｄｏｒｔｍｕｎｄ大学和分光化学暨应用光谱学研究所协办。大会内容涉及面很广 ,包括①生物传感器、基因组学和蛋白组学 ;②生物分析学和基因技术 ;③复杂体系、环境和医疗诊断分析 ;④高产筛选和化学成像 ;⑤仪器和方法 ;⑥微型化 ,纤 (或纳 )级分析和芯片上实验 ;⑦过程分析 (指各种类型的在线分析 ) ;⑧分析化学的新领域 ;⑨分析化学…     

《中国无机分析化学》征稿征订启事

<正>技术交流的平台无机分析的阵地企业联系的桥梁同行朋友的纽带《中国无机分析化学》是由北京矿冶研究总院主办的无机分析化学专业性学(技)术期刊。本刊包括岩矿分析、冶金分析、材料分析、环境分析、化工分析、生物医药分析、食品分析、仪器研制、综述评论、技术交流、信息之窗等栏目。读者对象为从事无机分析化学及相关技术的广大科研人员、工程技术人员、管理人员、大专院校师生、相关公司及企事业单位。本刊也是相关     

一个简单的测定溶质分子在不同吸附剂上吸附参数的前沿分析方程

本文给出了一个简单的测定溶质分子在不同吸附剂上吸附参数的前沿分析方程，它给出了平均突破时间和进口溶质浓度之间的关系。通过它的线性形式能够测定两个重要的吸附参数，一个是溶质分子在吸附剂表面吸附的热力学平衡常数K_SL，一个是分布在吸附剂表面的总的吸附点的数目n_t。通过一些芳香烃在RP-C₁₈反相介质和一些蛋白质在RP-C₁₈反相介质、WCX-1离子交换介质、PEG-400疏水介质以及Zn²⁺ 和 Cu²⁺螯合的Che1ating SepharoseFast-flow亲和介质的前沿分析对此方程进行了验证，并测定了它们的吸附参数。结果表明，所有这些前沿分析体系都能够用此方程良好描述，它们的参数n_t分别近似保持一个常数且独立于所用的溶质分子，而它们的参数K_SL不仅与所用的介质有关，而且与所用的溶质分子有关。     

硫化环氧天然橡胶的热分析

环氧化天然橡胶;热重分析;动力学;硫化环氧天然橡胶的热分析     

文物铜车马部分残件的分析测试

对秦陵一号铜车马材质取样,采用电感偶合等离子体发射光谱法和塞曼石墨炉原子吸收光度法对其元素定性和定量分析;用光学金相显微镜扫描电镜观察金相组织状态;用能谱分析研究工艺;用X－射线衍射研究铜合金的组成结构和物相;配合电子探针做微区分析。分析的结果为铜车马的修复和保护起到了至关作用。     

洁净钢痕量分析最新进展

评述了当前钢铁痕量分析的一些成果和应用前景：炉前及连铸现场临线快速分析新技术;超痕量氧、氮、碳、硫的高精度检测;各种夹杂分析方法的组合改进和适用场合;精炼过程中夹杂物在线和临线分析技术的开发;钢中极微细析出相的鉴定和分离定量。     

铁与氯气反应的产物分析

应用高温传感器测定了铁在氯气中燃烧时的温度;基于实际反应温度、压强与物质状态,对铁与氯气的相关反应进行了热力学分析,发现FeCl₃(Fe₂Cl₆)是铁与氯气反应过程中绝大多数情况下的热力学稳定产物,而铁与氯气反应生成FeCl₂的条件则较为苛刻;反应的动力学分析,FeCl₂和FeCl₃的结构分析,也表明铁与氯气反应生成FeCl₃更加有利。     

广义二面角在定量结构/活性相关分析中的应用

化合物的结构与其活性具有相关性 .建立相关性需要由结构提取特征 ,并运用这些特征作为自变量去构造数学模型 ,以便预测未知的化合物及其活性 .目前 ,在诸多 3D- QSAR方法中 ,应用最为广泛的是 Co MFA( Comparasive Molecular Field Analysis)方法 [1～ 3] ,但新近的研究发现 ,Co MFA方法对于某类化合物 (如苯胺类化合物 )适应性较差 ,但分子在三维空间投影面积及量化参数的引入可大大改善所得结果[4 ] .本文从另一角度 (即广义二面角 )研究了分子在三维空间的形状 ,得到了满意的结果 .1　实验部分本文选用 34个 1 - [( 2 - Hydroxy…     

分子三维投影法在苯酚类化合物构效关系研究中的应用

对苯酚类化合物进行三维投影得到了5个形状参数,将其与3个Am指数及8个量子化学参数相结合.由最佳变量子集回归法对变量进行了压缩与选择,运用多元回归分析和人工神经网络法分别构造了预测数学模型,得到了满意的结果.     

The predicting study for chromatographic retention index of saturated alcohols by MLR and ANN

A QSRR method was followed to relate the observed Kovats retention indexes of saturated alcohol compounds with their molecular connectivity indices by means of multilinear regression analysis and artificial neural networks technique. The alcohols included linear, branched with hydroxyl group on a primary, secondary, or tertiary carbon atom. At first, models were generated for six OV (Ohio Valley) series columns separately, with high value of R and F statistics. Then a combined model, added a polarity term of stationary phase (M), was also developed for all these columns, and the result was satisfactory. For comparison, the neural network of BP algorithm was applied, and it was found that the neural network could exceed the level of the multiple regression method. The stability and validity of both models were tested by cross-validation technique and by prediction response values for the prediction set.     

人工神经网络在纸浆卡伯值光学定量分析中的应用

卡伯值 (硬度 )是纸浆的重要质量指标 ,是制浆过程控制的关键参数 .目前的测量方法包括化学分析法和光学分析法两大类型 ,国内大多数的制浆造纸厂采用离线的传统化学分析法来测定纸浆的卡伯值 ,需要比较长的时间 .而光学分析法因具有实时性好、精度和可靠性高等优点 ,已逐步用于卡伯值的在线测量和控制 .研究 [1] 发现 ,在 460～ 580 nm的可见光谱范围内 ,蒸煮液吸光度的变化可以表征纸浆中木素含量的变化 .本文将可见分光光谱技术应用于蒸煮液中木素含量的在线测量 ,根据蒸煮液在所选波段的吸光度来预测纸浆的卡伯值 ,建立纸浆卡伯值与蒸煮…     

原子对空间距离法及其应用

采用原子对空间距离法表征化合物的三维结构信息. 提出了计算端点的频数的方法, 与段的频数相比, 该方法能更好地描述原子对频数. 以不同段的距离, 分别采用上述两种频数计算得到化合物的相似度矩阵, 然后把相似度衍生为新的变量. 运用多元回归分析和人工神经网络分别构造了预测数学模型, 并对所得到的预测结果进行了比较. 这两种频数均较好地预测了HEPT类化合物的活性.     

Simultaneous determination of composition of human urinary calculi by use of artificial neural networks

A new chemometric method, which uses artificial neural networks (ANN), is presented for determination of the composition of urinary calculi. The selected constituents were whewellite, weddellite, and uric acid from which approximately 40% of the urinary calculi obtained from Macedonia patients are composed. The results for the synthetic mixtures were better then those obtained by partial least squares (PLS) regression or by the principal component regression (PCR), because neural networks have better prediction capacity. The generalization abilities of the optimized neural networks were checked using the standard addition method on carefully selected real natural samples.     

Mass spectrometry and partial least-squares regression: a tool for identification of wheat variety and end-use quality

Rapid methods for the identification of wheat varieties and their end-use quality have been developed. The methods combine the analysis of wheat protein extracts by mass spectrometry with partial least-squares regression in order to predict the variety or end-use quality of unknown wheat samples. The whole process takes approximately 30 min. Extracts of alcohol-soluble storage proteins (gliadins) from wheat were analysed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Partial least-squares regression was subsequently applied using these mass spectra for making models that could predict the wheat variety or end-use quality. Previously, an artificial neural network was used to identify wheat varieties based on their protein mass spectra profiles. The present study showed that partial least-squares regression is at least as useful as neural networks for this identification. Furthermore, it was demonstrated that partial least-squares regression could be used to predict wheat end-use quality, which has not been possible using neural networks.     

Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction of electrophoretic mobility of a series of sulfonamides in capillary zone electrophoresis. The cross-validation method was used to evaluate the prediction ability of the generated networks. The mobility of sulfonamides as positively charged species at low pH and negatively charged species at high pH was investigated. The results obtained using neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression (MLR) technique. Comparison of the results shows the superiority of the neural network models over the regression models.     

Optimisation of chromatographic separations by use of a chromatographic response function, empirical modelling and multivariate analysis

Summary The chiral separation of the drug substance R,S-oxybutynin chloride on a reversed phase HPLC system has been optimised by use of empirical modelling and multivariate analysis. The separation was characterised by a new chromatographic response function developed to modulate both quality of separation and retention time. The study includes a comparison between three different multivariate techniques (multi-layer feed-for-ward neural networks, multiple linear regression and partial least squares regression) of their capabilities to model the new chromatographic response function and predict its value for new experiments. It was indicated that the most accurate models were achieved with neural networks, although partial least squares regression could also be used to solve the problem since it gives the major directions for the optimal settings of the variables.