一氧化氮的合成的演示及其方法的讨论

一、反应原理在放电条件下,N2和O2能直接化合生成无色的一氧化氮。放电N2+O2（?）2NO一氧化氮在常温下很容易和O2化合,生成棕色并有刺激性气味的NO2。二、仪器及药品打气球、贮气瓶2个（一个标出刻度）、两口玻璃球（容积140ml）、铁架台、铁丝电。     

氨的制造的教学法

苏联共产党第十九次代表大会的决议指示出化学教师应该提高关于综合技术教育的教学质量,更密切地联系学习与生活,使学生认识重要化学生产的科学原理。氨的制造是属于重要化学生产之列的。研究氨的制造是具有重要的教育教养意义。这课题的材料,可以提供作复习和更深入地去认识以前所熟悉的物质的性质和扩大瞭解关于反应进行的条件,以及在工业上的管理方法.在八年级末学期已经学过硫酸的制造,从而熟悉了一些化学工业制造原理,如阶段的划分和流水作业的装置,扩大反应物质的表面,逆流原理,温度和压力的影响,热的交换等等,在研究氨的制造中巩固了这些原理,并且增加了关于适当的压力和循环作业的利用等概念。     

伟大的门捷列夫的预见

今年2月8日是俄罗斯天才的化学家门捷列夫(1834—1907)诞生125周年纪念。特选此译文刊出以为纪念。     

有机试剂的结构与性质的关系的研究

前言有机试剂是用于化学元素和化合物的测定、分离与浓集、掩蔽的有机化合物。这类化合物数量很多,在分析化学中得到极其广泛的应用。有机试剂结构及其与反应性能之间关系的研究是有机试剂理论研究的基本问题。早在本世纪二十年代Feigl就提出了分析功能团的概念。这个概念广泛地应用于有机试剂的研究中,并不断得到丰富和发展。这一概念的出现,为人们研究有机试剂结构与反应性能的关系奠定了基础。随着现代科学技术的不断发展,X射线衍射、红外光谱、拉曼光谱等现代技术也相继用于有机试剂的研究,使人们对有机试剂的认识进一步加深。此外近代迅速发展起来的络合物结构理论、量子化学方法以及计算技术也被用于有机试剂的研究中,从而使得从理论上     

对公认的Clapeyron方程的推导的不同意见

对公认的Ｃｌａｐｅｙｒｏｎ方程的推导的不同意见郭余年，赵凤云（吉林工学院轻化工程系长春１３００１２）Ｃｌａｐｅｙｒｏｎ方程问世干热力学第二定律成立之前。第二定律建立以后，赋予了它以新的内容，使其成为描述单元系任意温度（压力）、任意两相平衡的普遍公式。...     

基于Matlab的二元共聚合反应的竞聚率的求解

以直线交叉法为依据,根据最小二乘原理,采用Matlab GUI工具设计了一款用于计算二元共聚合反应竞聚率的图形用户程序。与传统的求解竞聚率方法相比,该图形用户程序具有设计原理简单、计算快捷的特点;同时程序提供界面简洁、交互友好的数据输入平台,实用性强。实际应用表明:采用该款图形用户程序所测得的数据与微机动态搜索法、Tidwell-Mortimer法相近,而比采用斜率截距法计算竞聚率的最小差方和更小,并且也避免了采用斜率截距法由于所用方程的非对称性造成的计算结果的不一致性。     

紫精的负离子对耐水的紫精—聚合物膜的光致变色性能的影响

将含不同负离子的苄基紫精分散在混合有ＰＶＰ的ＭＭＡ－ＨＥＭＡ共聚物基质中可制成耐水的光致变色膜。它们的光致变色速度的大小随紫精负离子的不同而有如下序列：Ｖ６（ＰＦ６＾－）〉Ｖ５（ＢＦ４＾－）￣Ｖ４（ＣｌＯ４＾－）〉Ｖ３（ＣＨ３－苯环－ＳＯ３＾－）〉Ｖ２（Ｂｒ＾－）￣Ｖ１（Ｃｌ＾－）。这与这些紫精在ＤＭＦ中的溶解度以及在共聚物基质中的溶解性大小的序列相一致。负离子对这些光致变色膜在空气中的氧化退色速     

具有好的溶解性和高的热稳定性的富勒烯-苝化合物的合成

在氩气保护下,利用N-(正丁基)-N’(4-甲酰基苯氧戊基)-1．6,7,12-四-(4-叔丁基苯氧基)-3,4,9,10-苝酰亚胺和N-甲基甘氨酸产生的亚胺叶立德与富勒烯反应,合成了含富勒烯的苝化合物,用NMR、FT—IR、UV-Vis及TGA等方法对其结构和性能进行了表征和测试。研究结果表明此类化合物具有好的溶解性和高的热稳定性。     

金的快速测定方法的改进

自从本刊1975年第二期“金的快速测定”一文发表以后,读者来信提出了宝贵的意见,认为此法测定金时铊有干扰,且试样分解方法可能使金的结果偏低。过去,我们分析的样品中不含有铊,故对铊的干扰未作考虑。现经试验,在溴氢酸存在下,三价金在低温蒸干时,随时间、温度、溴氢酸用量的不同,均有部份金被还原造成结果偏低。铊的存在对金的测定有干扰。因此对金的快速测定作如下的补充。     

砷的光度分析法的进展

文中着重介绍了近年来砷的光度测定方法的新进展,包括砷斑法、Ag（DDC）光度法、分单质银分散体系光度法、砷钼蓝光度法等。引用文献85篇。     

稳健线性回归法探讨分析精度与浓度之间的关系

研究分析精度与浓度之间的关系对于分析温度的质量控制具有重要意义。本文以土壤及水系沉积中微量元素为例,研究了不同实验室之间分析精度与被测组分浓度之间的关系。应用重新加权迭代最小二乘法（ＩＲＬＳ）成功地对分析结果的标准偏差与含量之间的关系进行了线性拟合,表明线性关系良好,与经典最小二乘法比较发现,ＩＲＬＳ法具有很好的稳健性,受离群值的影响小,回归结果也更符合实际。     

Regression with multiple regressor arrays

Extension of standard regression to the case of multiple regressor arrays is given via the Kronecker product. The method is illustrated using ordinary least squares regression (OLS) as well as the latent variable (LV) methods principal component regression (PCR) and partial least squares regression (PLS). Denoting the method applied to PLS as mrPLS, the latter was shown to explain as much or more variance for the first LV relative to the comparable L‐partial least squares regression (L‐PLS) model. The same relationship holds when mrPLS is compared to PLS or n‐way partial least squares (N‐PLS) and the response array is 2‐way or 3‐way, respectively, where the regressor array corresponding to the first mode of the response array is 2‐way and the second mode regressor array is an identity matrix. In a comparison with N‐PLS using fragrance data, mrPLS proved superior in a validation sense when model selection was used. Though the focus is on 2‐way regressor arrays, the method can be applied to n‐way regressors via N‐PLS. Copyright © 2007 John Wiley & Sons, Ltd.     

Development of Gradient Retention Model in Ion Chromatography. Part III: Fuzzy Logic QSRR Approach

In this paper, the authors tested methodology that overcame the most common limitation of quantitative structure-retention relationship (QSRR) models: their limited applicability at the specific conditions for which models were developed. The modeling was performed on ion chromatographic analysis of “wood sugars”. Adaptive neuro-fuzzy interference system, an advanced artificial intelligence regression tool, was applied in combination with genetic algorithm scanning to obtain good and reliable QSRR models. The obtained QSRR models were applied for predicting data that were required for further development of general isocratic and gradient retention models. All three developed models (QSRR, isocratic, and gradient) indicated good prediction ability with root mean square error of prediction ≤0.1557. The performances of the methodology were compared with those presented in previous research—namely genetic algorithm in combinations with—stepwise multiple linear regression, partial least squares, uninformative variable elimination–partial least squares, and artificial neural network regression.

     

组合偏最小二乘回归方法在近红外光谱定量分析中的应用

针对近红外光谱数据局部效应显著,变量个数多,彼此间常存在严重的复共线性,并多与样品组分含量呈非线性关系,构建一种组合非线性偏最小二乘回归(E-S-QPLSR)方法。它采用无重复采样技术(subag-ging),从训练样本中生成若干子样,然后每个子样通过二次多项式偏最小二乘回归(QPLSR),建立其子模型,并实现对训练样本因变量的定量预测,再将它们交由线性PLS算法用于计算各子模型的组合权系数。将该法应用于80个玉米样品的水组分含量与其近红外光谱的定量关系建模,效果良好,显示出很强的学习能力,所建模型的预报性能也优于其它方法。     

Beyond linear least squares regression

Regression is a collection of statistical methods that are used to study relationships among predictor and response variables. In addition to the most popular linear model, solved by least squares, several other techniques have found an application in analytical chemistry. Biased methods such as stepwise regression, ridge regression, principal components regression, and partial least squares regression are especially useful in cases of poorly or underdetermined systems with collinearity. When structural and/or distributional assumptions associated with linear least squares are violated, nonlinear regression, robust regression or generalized least squares estimators may offer potential remedies.     

A hybrid genetic algorithm for estimating the equilibrium potential of an ion-selective electrode

Non-linear equations can be used to model the measured potential of ion-selective electrodes (ISEs) as a function of time. This can be done by using non-linear least squares regression to fit parameters of non-linear equations to an ISE response curve. In iterative non-linear least squares regression (which can be considered as local optimisers), the determination of starting parameter estimates that yield convergence to the global optimum can be difficult. Starting values away from the global optimum can lead to either abortive divergence or convergence to a local optimum. To address this issue, a global optimisation technique was used to find initial parameter estimates near the global optimum for subsequent further refinement to the absolute optimum. A genetic algorithm has been applied to two non-linear equations relating the measured potential from selected ISEs to time. The parameter estimates found from the genetic algorithm were used as starting values for non-linear least squares regression, and subsequent refinement to the absolute optimum. This approach was successfully used for both expressions with measured data from three different ISEs; namely, calcium, chloride and lead ISEs.     

Comparison of regression techniques for linear calibration

A recently presented regression technique for linear calibration, which is based on a variance component model for univariate quantitative measurement data, is compared with the conventional and far spread regression techniques ordinary least squares regression and weighted least squares regression. The associated statistical models and estimations are represented. Its application is demonstrated at some practical examples. With consideration of special variation causes, like matrix influence or the influence of several operating conditions on the measurement response, it can be shown that the application of the variance component model is an advantage.     

Envirometrics. Part I: Modeling of water salinity and air quality data

Envirometrics utilises advanced mathematical, statistical and information tools to extract information. Two typical environmental data sets are analysed using MVATOB (Multi Variate Analysis TOol Box). The first data set corresponds to the variable river salinity. Least median squares (LMS) detected the outliers whereas linear least squares (LLS) could not detect and remove the outliers. The second data set consists of daily readings of air quality values. Outliers are detected by LMS and unbiased regression coefficients are estimated by multi-linear regression (MLR). As explanatory variables are not independent, principal component regression (PCR) and partial least squares regression (PLSR) are used. Both examples demonstrate the superiority of LMS over LLS.     

Linear calibrations in chromatography: The incorrect use of ordinary least squares for determinations at low levels,and the need to redefine the limit of quantification with this regression model

Ordinary least squares is widely applied as the standard regression method for analytical calibrations, and it is usually accepted that this regression method can be used for quantification starting at the limit of quantification. However, it requires calibration being homoscedastic and this is not common. Different calibrations have been evaluated to assess whether ordinary least squares is adequate to quantify estimates at low levels. All calibrations evaluated were linear and heteroscedastic. Despite acceptable values for precision at limit of quantification levels were obtained, ordinary least squares fitting resulted in significant and unacceptable bias at low levels. When weighted least squares regression was applied, bias at low levels was solved and accurate estimates were obtained. With heteroscedastic calibrations, limit values determined by conventional methods are only appropriate if weighted least squares are used. A “practical limit of quantification” can be determined with ordinary least squares in heteroscedastic calibrations, which should be fixed at a minimum of 20 times the value calculated with conventional methods. Biases obtained above this “practical limit” were acceptable applying ordinary least squares and no significant differences were obtained between the estimates measured using weighted and ordinary least squares when analyzing real‐world samples.