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2010年03月07日

【期刊论文】Determination of tetrandrine and fangchinoline in plasma samples using hollow fiber liquid-phase microextraction combined with high-performance liquid chromatography

张海霞, Cailing Yang, Linyuan Guo, Xiaoyan Liu, Haixia Zhang*, Mancang Liu

Journal of Chromatography A, 1164(2007)56-64,-0001,():

-1年11月30日

摘要

Tetrandrine (TET) and fangchinoline (FAN) are basic and highly hydrophobic drugs with log P>5.7. In this work, a simple, inexpensive and efficient liquid-phase microextraction (LPME) technology combined with high-performance liquid chromatography (HPLC) was developed for the simultaneous analysis of tetrandrine and fangchinoline in plasma samples. Tetrahydropalmatine was used as internal standard. Several parameters influencing the efficiency of LPME were investigated and optimized including organic solvent, stirring rate, extraction time, salt concentration, organic modifier and pH. Under the optimal conditions, extraction recoveries from plasma samples were 46% for tetrandrine and 50% for fangchinoline, corresponding to the drugs enriched by a factor of 23 and 25 by LPME, respectively. Excellent sample clean-up was observed and good linearities with correlation coefficients (r) of 0.9979 (FAN) and 0.9995 (TET) were obtained in the range of 15-1000 ngmL−1. The limits of detection (LOD, S/N=3) were 3.0ngmL−1 for FAN and 2.0ngmL−1 for TET.

Hollow fiber liquid-phase microextraction, High-performance liquid chromatography, Tetrandrine and fangchinoline, Plasma sample

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2010年03月07日

【期刊论文】Oxidized multiwalled carbon nanotubes as a novel solid-phase microextraction fiber for determination of phenols in aqueous samples

张海霞, Xiaoyan Liu, Yongsheng Ji, Yonghui Zhang, Haixia Zhang*, Mancang Liu

Journal of Chromatography A, 1165(2007)10-17,-0001,():

-1年11月30日

摘要

A simple and environmentally friendly method for determination of seven phenols using solid-phase microextraction (SPME) coupled to highperformance liquid chromatography (HPLC) has been developed. Several materials were used as stationary phase of SPME fibers and an oxidized multiwalled carbon nanotubes material was found to be effective in carrying out simultaneous extraction of phenols in aqueous samples. Compared with the widely used commercially available SPME fibers, this proposed fiber had much lower cost, longer lifetime (over 150 times), shorter analysis time (30 min of extraction and 3 min of desorption time) and comparable or superior extraction efficiency for the investigated analytes. The extraction and desorption conditions were evaluated and the calibration curves of seven phenols were linear (R2≥0.9908) in the range from 10.2 to 1585 ngmL−1. The limits of detection at a signal-to-noise (S/N) ratio of 3 were 0.25-3.67 ngmL−1, and the limits of quantification calculated at S/N=10 were 0.83-12.25 ngmL−1 for these compounds. The possibility of applying the proposed method to environmental water samples analysis was validated.

Phenols, Oxidized multiwalled carbon nanotubes, Solid-phase microextraction-high performance liquid chromatography

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2010年03月07日

【期刊论文】Preparation and characterization of octyl and octadecyl-modified mesoporous SBA-15 silica molecular sieves for adsorption of dimethyl phthalate and diethyl phthalate

张海霞, Huayu Huang, Cailing Yang, Haixia Zhang*, Mancang Liu

Microporous and Mesoporous Materials 111(2008)254-259,-0001,():

-1年11月30日

摘要

Octyl (C8) or octadecyl (C18)-modified mesoporous SBA-15 silica molecular sieves have been prepared by adding SBA-15 silica molecular sieves to octyltrimethoxysilane or octadecyltrimethoxysilane in toluene at 100 C, and characterized by Fourier transform infrared (FTIR) spectroscopy, powder X-ray diffraction (XRD), nitrogen adsorption-desorption measurements, scanning electron microscopy (SEM) and transmission electron microscopy (TEM). FTIR spectra shows the presence of methylene (-CH2-) and methyl (-CH3) bands on the modified SBA-15. Powder XRD data indicate the structure of modified SBA-15 with octyl or octadecyl groups still remains twodimensional hexagonal mesostructrure. Brunauer-Emmett-Teller (BET) surface area analysis presents that surface area of octyl-and octadeyl-SBA-15 changed from 647 to 449 and 321 m2g-1, respectively, and SEM images show the decreased size of modified SBA-15 particles. TEM images of modified materials with alkyl groups show the structures remain the same as the parent SBA-15 silica. We also have studied the adsorption capacity of the materials to phthalate esters (dimethyl and diethyl phthalate) by dynamic adsorption experiments on high performance liquid chromatography (HPLC). It is found that the modified materials can increase the adsorption of phthalate esters compared to SBA-15 particles, and the adsorption capacity increased with the increased length of alkyl chain on SBA-15. The maximum dynamic adsorption capacity for diethyl phthalate was 3.9 (C8-SBA-15) or 4.3 (C18-SBA-15) times higher than that of SBA-15 particles, respectively. The results indicate that alkyl SBA-15 particles could be used for enrichment of phthalate esters in water samples before the further analysis.

Mesoporous silica molecular sieves, Alkyl group, Phthalates, Adsorption, High performance liquid chromatography

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2010年03月07日

【期刊论文】A novel sol-gel-material prepared by a surface imprinting technique for the selective solid-phase extraction of bisphenol A

张海霞, Xiaoman Jiang, Wei Tian, Chuande Zhao, Haixia Zhang*, Mancang Liu

Talanta 72(2007)119-125,-0001,():

-1年11月30日

摘要

A novel and simple imprinted amino-functionalized silica gel material was synthesized by combining a surface molecular imprinting technique with a sol-gel process on the supporter of activated silica gel for solid-phase extraction-high performance liquid chromatography (SPE-HPLC) determination of bisphenol A (BPA). Non-imprinted silica sorbent was synthesized without the addition of BPA using the same procedure as that of BPA-imprinted silica sorbent. The BPA-imprinted silica sorbent and non-imprinted silica sorbent were characterized by FT-IR and the static adsorption experiments. The prepared BPA-imprinted silica sorbent showed high adsorption capacity, significant selectivity and good site accessibility for BPA. The maximum static adsorption capacity of the BPA-imprinted and non-imprinted silica sorbent for BPA was 68.9 and 34.0mg g−1, respectively. The relatively selective factor value of this BPA-imprinted silica sorbent was 4.5. Furthermore, the difference of the retention characteristics of BPA on the C8 SPE column and BPA-imprinted silica SPE (MIP-SPE) was compared. The MIP-SPE-HPLC method showed higher selectivity to BPA than the traditional SPE-HPLC method. At last, the BPA-imprinted polymers were used as the sorbent in solid-phase extraction to determine BPA in water samples with satisfactory recovery higher than 99% (R.S.D. 3.7%).

Molecularly imprinted polymers, Bisphenol A, Solid-phase extraction, High performance liquid chromatography

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2010年03月07日

【期刊论文】Application of support vector machine (SVM) for prediction toxic activity of different data sets

张海霞, C.Y. Zhao a, H.X. Zhang a, *, X.Y. Zhang a, M.C. Liu a, Z.D. Hu a, B.T. Fan b

Toxicology 217(2006)105-119,-0001,():

-1年11月30日

摘要

As a new method, support vector machine (SVM) were applied for prediction of toxicity of different data sets compared with other two common methods, multiple linear regression (MLR) and RBFNN. Quantitative structure-activity relationships (QSAR) models based on calculated molecular descriptors have been clearly established. Among them, SVM model gave the highest q2 and correlation coefficient R. It indicates that the SVM performed better generalization ability than the MLR and RBFNN methods, especially in the test set and the whole data set. This eventually leads to better generalization than neural networks, which implement the empirical risk minimization principle and may not converge to global solutions. We would expect SVM method as a powerful tool for the prediction of molecular properties.

Quantitative structure-activity relationship (, QSAR), , Toxicity, Multiple linear regression (, MLR), , Radical basis function neural network (, RBF), , Support vector machine (, SVM),

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