傅里叶变换用于快扫伏安法的电流分离

根据电化学池的电学模型，推导出相应的数学模型，和傅里变变换相结合，实现了扫描伏安数据的充电电流和法拉第电流的分离。模拟数据和实验数据的处理结果表明，所提出的方法均能实现充电电流和法拉第电流的分离，得到的结果与理论模型一致。     

恒电流库仑法测定基准试剂三氧化二砷的含量

用恒电流库仑法测定基准试剂三氧化二砷的含量，以恒电流通过含有碘化钾，基准试剂三氧化二砷试样的弱碱性水溶液，利用工作电极铂阳极上电生滴定剂I2与亚砷酸盐定量反应，以死停终点法确定滴定终点，测量出电解电流和电解时间，根据法拉第电解定律并利用EXCEL计算出基准试剂三氧化二砷的含量。方法的回收率为100％。     

高精密恒电流库仑法测定CCQM-K73国际关键比对盐酸溶液中H+含量

采用高精密恒电流库仑法测量了CCQM-K73国际比对样品盐酸溶液含量。该方法是国际上公认的基准测量方法,根据法拉第电解定律,通过质量、电阻、电动势、时间等基本物理量及法拉第常数来进行样品的测定,直接溯源到国际SI单位,不需要用标准物质进行比对。中国计量科学研究院作为国家化学计量最高溯源,参加了该项关键比对,并取得了等效一致的好成绩。     

极谱的动电流

用极化的滴汞电极进行电解电流的研究是极譜学中的一个基本問題。对这类电流的理論分析,包含着解决由于电极过程所引起的、在不断增长的汞滴表面所发生的貭量轉移的問題。电极过程只涉及到电板和能直接交換电子的物貭之間的电子交換时,貭量轉移就构成了法拉第电流,这种质量的轉移,平衡了因电級反应而引起的浓度的变化。     

聚合物电流变体

本文概述了电流变体的发展概况，电流变机理、电流变效应的影响因素及聚合物电流变体的研究现状，并介绍了电流变化的组成春在机械制造、自动控制等领域的应用前景。     

关于钒电池中支路电流(shunt current)的计算

本文针对双极堆式的全钒氧化还原液流电池中,因电极1框结构所导致液流过程中存在的支路电流(shunt current)进行分析,提出了支路电流的计算方法并对5组单电池组成的电池组进行了理论计算,并探讨了支路电流对电池性能的可能影响.结果表明:1)支路电流的大小与电池位置密切相关,支路电流呈对称分布,电液通道中电流越接近电堆中心越小.主路电流充电时越靠近电池中心越小,放电时越靠近电池中心越大.2)充电时,电液通道中的支路电流会对接触溶液的双极板产生腐蚀;放电时,过大的支路电流会降低电池的电压、库仑及能量效率.     

基于改进计时电流法精确模型的数值解仿真及实验验证

根据电极表面被测物浓度变化经验公式所建立的改进计时电流法的理论模型,只能定性分析响应电流峰值与被测物浓度关系,无法准确描述响应电流峰值与反应体系及激励电势中其它参数的关系.为解决该问题,利用能斯特方程与扩散定律,建立了描述电极表面被测物浓度与响应电流关系的积分方程,通过数值分析方法求解得到该积分方程数值解.进而利用数值仿真得到精确的电流与时间关系曲线,分析了电流峰值与试剂浓度、惯性时间常数、参比电极标准电势、激励电势初始值以及稳态值之间的关系.最后,利用改进计时电流法的电路装置对K3[Fe(CN)6]试剂进行测试,实验结果表明该数值模型比经验模型更接近真实情况,也验证了由该模型推导得到的各参数之间关系的正确性.     

壳聚糖电流变体的导电特性

电流变体是由高介电颗粒分散在低介电油相中组成的悬浮液,在电场作用下,其表观粘度急剧增大甚至发生固化,这种转变过程速度快（达毫秒级）而且是可逆的［１］．由于这种独特的电场响应性,电流变体有广阔的工程应用前景．但电流变体在显示电流变效应的同时也表现出复杂...     

双极电流滴定法测定铜

在抗坏血酸存在时,两价铜离子能与硫氰酸盐生成溶解度非常小的硫氰酸亚铜沉淀（Ksp,=2×10~(-13)）,专属性非常好。因而,用硫氰酸盐直接沉淀滴定钢是可行的,问题是没有适宜的指示剂确定滴定终点。本文试验用双极电流滴定法完成测定,用两根钢丝作为双极系统,终点时的电流变化十分明显,电流滴定曲线呈锯齿波形,极易判断滴定终点。用此法测定了铜合金、铝合金及锡基合金中的铜,方法简单,结果准确。1 试验部分1.1 试剂与仪器 硫氰酸铵标准溶液:0.005mol·L~(-1),称取硫氰酸铵0.40g溶于1L水中。 乙酸缓冲溶液:pH 5.5,乙酸钠80g,冰乙酸5ml溶于1L水中。 铜标准溶液:1mg·ml~(-1),称取纯铜丝 1.000g溶于硝酸（1 1）20ml中,煮沸除去氮的氧化物,冷却,稀释至1L量瓶中。 双极电流滴定仪（自制）     

圆环电极上计时电流理论及其验证

本文推导得到了圆环电极上计时电流方程式。该式形式简单、计算方便、适用于任何电极半径和时间。用微金环电极在K4Fe(CN)6-KCl体系中对该理论进行验证,实验结果与理论十分符合。     

Comparison of several curve resolution methods for drug impurity profiling using high-performance liquid chromatography with diode array detection

The performance of five curve resolution methods was compared systematically for the identification and quantification of impurities in drug impurity profiling. These methods are alternating least-squares (ALS) with either random or iterative key-set factor analysis (IKSFA) initialisation, iterative target transformation factor analysis (ITTFA), evolving factor analysis (EFA), and heuristic evolving latent projections (HELP). Real and simulated high-performance liquid chromatography diode array detection (HPLC-DAD) data were obtained for drug mixtures containing one main compound and two impurities. The elution order of the main compound and the impurities was varied. Furthermore, resolutions were varied from 0.56 to 3.36 and impurity levels from 30% down to 0.1%. For simulated data, ALS with IKSFA initialisation and HELP perform better than ITTFA and EFA, which perform better than ALS with random initialisation. ITTFA works better than EFA for almost completely separated data, while the opposite is true for moderately or strongly overlapping data. Only ALS with IKSFA initialisation and HELP were found to resolve the required 0.1% level for moderately overlapping data. For real data, comparison of the methods provides similar results. ITTFA performs clearly better than EFA. However, none of the curve resolution methods can identify or quantify impurities at the required 0.1% level. The results for real data are worse than for simulated data because of heteroscedasticity, nonlinearity, and the acquisition resolution of the A/D-converter.     

Detection and use of selective ions for providing initial estimates of the concentration vectors in iterative target transformation factor analysis

The characteristic of discreteness in mass spectral direction distinguishes GC/MS from other spectral techniques. Based on this feature, we propose a new method to construct the initial concentration vectors for iterative target transformation factor analysis (ITTFA). For each chemical component, a search for a selective ion with good signal-to-noise ratio is first conducted using evolving factor analysis (EFA) and information entropy. The corresponding chromatogram of the selective ion is subsequently applied to construct an initial concentration vector for ITTFA. Special strategies are developed to cope with chromatographic patterns with embedded peaks and complex multicomponent structure. Results from three simulated and one real mixture and comparison with results from heuristic evolving latent projections (HELP) and a previously published method for definition of the initial profiles for ITTFA, indicate that selective ion chromatogram (SIC) ITTFA represents a fast, automatic and accurate method for resolution of GC/MS data.     

Combined kinetics and iterative target transformation factor analysis for spectroscopic monitoring of reactions

Obtaining rate constants and concentration profiles from spectroscopy is important in reaction monitoring. In this paper, we combined kinetic equations and Iterative Target Transformation Factor Analysis (ITTFA) to resolve spectroscopic data acquired during the course of a reaction. This approach is based on the fact that ITTFA needs a first guess (test vectors) of the parameters that will be estimated (target vectors). Three methods are compared. In the first, originally proposed by Furusj? and Danielsson, kinetic modelling is only used to provide the initial test vectors for ITTFA. In the second the rate constant used to provide the test vectors is optimised until a best fit is reached. In the third, a guess of the rate constant is used to provide the test vectors to ITTFA. The outcome of ITTFA is then used to fit the kinetic model and obtain a new guess of the rate constant. With this constant new concentration profiles are generated and provided to the ITTFA algorithm as new test vectors, in an iterative manner, minimising the residuals of the predicted dataset, until convergence. The second and third methods are new implementations of ITTFA and are compared to the first, established, method. First order (both one and two step) and second order reactions were simulated and instrumental noise was introduced. An experimental second order reaction was also employed to test the methods.     

基于小波变换平滑主成分分析

小波变换具有很强的信号分离能力,很容易把随机噪音从信号中分离出来,从而提高信号的信噪比。本文把小波变换引入到因子分析中,提出了基于小波变换平滑主成分分析,该算法既保留普通主成分分析的正交分解,又具备了小波变换的信号分离能力。模拟数据和实验数据的结果表明,该算法具有从低信噪比的数据中提取出有用信息,并提高信号的信噪比。迭代目标变换因子分析处理实验数据的结果表明,基于小波变换平滑主成分分析的处理结果优     

Multicomponent Chemometric Determination of Colorant Mixtures by Voltammetry

Abstract

A quantitative chemometric approach, iterative target transformation factor analysis (ITTFA), was applied to the simultaneous determination of mixtures of four food colorants (Amaranth, Sunset Yellow. Tartrazine and Ponceau 4R) by adsorptive voltammetry. The conventional and first-derivative voltammograms of the quaternary mixtures were used to perform the optimization of the calibration procedure by the ITTFA models. The proposed method was applied satisfactorily to the determination of a set of synthetic quaternary mixtures of colorant in phosphate/citric acid buffers (pH 5.7) and acceptable results were obtained. No significant advantages were found with the application of derivative technique in this voltammetric work.     

迭代目标转换因子分析与人工神经网络法用于邻、间、对硝基甲苯的分光光度同时测定的比较研究

本文将迭代目标转换因子分析与人工神经网络法用于分光光度法同时测定邻、间、对硝基甲苯,并与目标转换因子分析的结果进行了比较.结果表明,迭代目标转换因子分析法与线性网络法的效果都很好.其相对误差分别为1.3％和1.2％,而目标转换因子分析法的预测误差较大,其相对误差为10.4％.     

Resolving co-eluting chromatographic patterns by means of dissimilarity analysis in iterative target transformation factor analysis

The initialization of concentration vector for iterative target transformation factor analysis (ITTFA) and identification of pure or key variables are the important issue in MCR. In this study, dissimilarity analysis and evolving factor analysis (EFA) are combined to find the selective or key variables and subsequently obtain initial estimates of the concentration vectors for resolution of gas chromatography/mass spectrometry (GC/MS) data by ITTFA. For systems containing components with highly similar mass spectra, a new constraint setting the elements out of elution window to 0 is used to improve convergence rate and accuracy of results. Tested by standard mixture of two wax esters and real GC/MS data of gasoline 97#, dissimilarity based ITTFA could obtain accurate results (average Dot product of concentration vectors, average deviation of peak area ratio and average similarity of mass spectra are 0.9929, 0.0228 and 981.0, respectively).     

迭代目标转换因子分析光度法同时测定化探样品中钼、钨

本文以水杨基荧光酮为显色剂、采用迭代目标转换因子分析光度法,不经分离对化探样品中钼、钨进行同时测定。结果表明,各组分的相对误差一般小于10％,Mo、W的相对标准偏差分别为1.6％,1.5％。取得满意结果。对标准数据阵的构成、因子数的确定以及矿样分解,干扰消除等诸问题进行了探讨。     

Spectroelectrochemical study of the oxidative doping of polydialkylphenyleneethynylene using iterative target transformation factor analysis

Iterative target transformation factor analysis (ITTFA) was used to determine the spectra of the individual species generated during the oxidative p-doping of films of poly(para-phenyleneethynylene) (PPE). UV-visible spectra of PPE films on transparent electrodes were obtained in-situ during an anodic sweep. ITTFA identified 4 species present during the oxidation, which we assign as neutral polymer, polaron species, bipolaron species, and a species formed by further bipolaron reaction. The region of electrochemical stability for each of these species was identified and their potential-dependent profiles were obtained. This work is the first deconvolution of conjugated polymer spectroelectrochemistry.     

Simultaneous mixture analysis by using non-linear spectrophotometric data and linear iterative target transformation factor analysis

Non-linear absorption spectral data obtained from ternary mixtures of analytes are analyzed by using a linear model, iterative target transformation factor analysis (ITTFA). The use of transformed original variables is used to correct non-linearities in the original data. Absorbance below a certain limit (k) is described as linear and above this limit as non-linear. The extension of the regressor variables is the squared absorbances above the linear range. The variation of the prediction error as a function of the number of the factors and the k-values were considered and the minimum prediction error was evaluated for reaching to optimum. Except the natural non-negativity constraint the correlation constraint also is used on concentration vector in each iteration of ITTFA algorithm. The reliability of the method is evaluated using model data for ternary mixtures by spectral overlapping and different degrees of non-linearity. Simultaneous spectrophotometric determination of Eu³⁺, UO₂²⁺ and Th⁴⁺ with arsenazo III as chromogenic reagent is used as experimental model systems with non-linearity behavior of Eu³⁺and UO₂²⁺ components. The application to both synthetic and real data sets with different degrees of non-linearity demonstrate the ability of the proposed methodology to obtain better results than original data and ITTFA. The relative standard errors of prediction for proposed method in comparison with using the PLS calibration on original and extended data are nearly smaller.