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A radial basis function neural network (RBFNN) method was developed for the first time to model the nonlinear calibration curves of four hexachlorocyclohexane (HCH) isomers, aiming to extend their working calibration ranges in gas chromatography-electron capture detector (GC-ECD). Other 14 methods, including seven parametric curve fitting methods, two nonparametric curve fitting methods, and five other artificial neural network (ANN) methods, were also developed and compared. Only the RBFNN method, with logarithm-transform and normalization operation on the calibration data, was able to model the nonlinear calibration curves of the four HCH isomers adequately. The RBFNN method accurately predicted the concentrations of HCH isomers within and out of the linear ranges in certified test samples. Furthermore, no significant difference (p > 0.05) was found between the results of HCH isomers concentrations in water samples calculated with RBFNN method and ordinary least squares (OLS) method (R2 > 0.9990). Conclusively, the working calibration ranges of the four HCH isomers were extended from 0.08-60 ng/ml to 0.08-1000 ng/ml without sacrificing accuracy and precision by means of RBFNN. The outstanding nonlinear modeling capability of RBFNN, along with its universal applicability to various problems as a “soft” modeling method, should make the method an appealing alternative to traditional modeling methods in the calibration analyses of various systems besides the GC-ECD. 相似文献
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采用基于密度泛函理论的Materials Studio(MS)程序包中的CASTEP程序优化了TiO_2(101)晶面、五氯硝基苯(PCNB)结构和TiO_2(101)对五氯硝基苯的16种吸附结构.计算了最佳吸附位点,吸附能,以及稳定吸附模型的电子结构.研究结果表明:五氯硝基苯中硝基的对位上Cl原子被垂直吸附在TiO_2(101)表面的O原子时吸附能最大,吸附结构最稳定,是TiO_2吸附PCNB的最佳吸附方式,吸附后五氯硝基苯的C-Cl键长变短,其它C—Cl键和C—N键增长,吸附过程为化学吸附. 相似文献
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