Multivariate analysis of the selectivity for a pentachlorophenol-imprinted polymer

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• 作者：Baggiani ; C. ; Anfossi ; L. ; Giovannoli ; C. ; Tozzi ; C.
• 关键词：Multivariate analysis ; Selectivity ; Pentachlorophenol ; Molecularly imprinted polymers
• 刊名：Journal of Chromatography B, Analytical Technologies in the Biomedical and Life Sciences
• 出版年：2004
• 出版时间：May 5, 2004
• 年：2004
• 卷：804
• 期：1
• 页码：31-41
• 全文大小：207 K

文摘

A pentachlorophenol (PCP)-imprinted polymer (MIP) was obtained by thermal polymerization of a mixture of template, 4-vinylpyridine and ethylene glycol dimethacrylate with molar ratio 1+3+27, using as porogenic solvent methanol–water (3+1 (v/v)). The polymer was packed in an HPLC column and selectivity towards 52 PCP-related phenols (22-chloro-, 21-alkyl-, 4-aryl-, 3-methoxy- and 6-polyphenols) was measured using acetonitrile–acetic acid (99+1 (v/v)) as mobile phase. The same was made for a reference polymer obtained without pentachlorophenol (NIP). The molecular recognition properties of the imprinted polymer were expressed in terms of selectivity index (SI), calculated for each phenol as k_NIP/k_MIP. Sixteen molecular descriptors were calculated for each molecule: qO, the partial charge of the phenolic oxygen atom; qH, the partial charge of the phenolic hydrogen atom; Δq, the absolute value of the difference qO − qH; HOMO, the highest occupied molecular orbital; LUMO, the lowest unoccupied molecular orbital; Δorb, absolute value of the difference HOMO − LUMO; μ², the square of total dipole moment; MW, the molecular weight; SAS, the solvent-accessible molecular surface area; hSAS, the hydrophobic solvent-accessible molecular surface area; Svdw, the van der Waals molecular surface area; hSvdw, the hydrophobic part of Svdw; MOv, the molecular ovality; RG, the radius of gyration; logP, the logarithm of n-octanol–water partition coefficient; pK, the phenolic dissociation constant. Correlations between selectivity index and these descriptors were searched utilizing multivariate principal component analysis (PCA). The multivariate model obtained by regression on the principal components correlate collectively several of the calculated descriptors with the polymer selectivity. The magnitude of the model’s parameters shows that selectivity is strongly influenced by molecular descriptors having structural character, such as MW, hSvdw and logP, while the effect of molecular descriptors having electronic character, such as qO and pK, is much less marked.