In this study, As(Ⅲ) imprinted polymer (As(Ⅲ)-IIP) was synthesized by one-pot method using 1,4-dithioerythritol and 3-Mercaptopropyltrimethoxysilane as bifunctional monomer and As(III) ion as the template ion that was eluted by...

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A selective and sensitive electrode based on Au−S bonds between As(III) ion‐imprinted polymer (IIP) and the flower‐like gold nanoparticles (FL‐AuNPs) had been rationally developed for detecting As(III) by using the square wave voltammetry (SWV) method. Under optimized measurement conditions, the prepared electrochemical sensor exhibited obvious detection performance of As(III) in the range of 0.009 μg/L–0.50 μg/L with a relatively low detection limit of 0.015 μg/L. Furthermore, the imprinted electrochemical sensor displayed good reusability, excellent specificity, and demonstrated high potential for environmental control with a recovery rate between 80.7 % and 113.3 %.

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Abstract. It is one of the difficult problems encountered in the research of condition maintenance technology of transformers to recognize partial discharge (PD) pattern. According to the main physical characteristics of PD, three models of oil-paper insulation defects were set up in laboratory to study the PD of transformers, and phase resolved partial discharge (PRPD) was constructed. By using least square method, the grey-scale images of PRPD were constructed and features of each grey-scale image were 28 box dimensions and 28 information dimensions. Affinity propagation algorithm based on manifold distance (AP-MD) for transformers PD pattern recognition was established, and the data of box dimension and information dimension were clustered based on AP-MD. Study shows that clustering result of AP-MD is better than the results of affinity propagation (AP), k-means and fuzzy c-means algorithm (FCM). By choosing different k values of k-nearest neighbor, we find clustering accuracy of AP-MD falls when k value is larger or smaller, and the optimal k value depends on sample size.