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首页> 外文期刊>Analytical methods >Application of multi-factorial experimental design to successfully model and optimize inorganic arsenic speciation in environmental water samples by ultrasound assisted emulsification of solidified floating organic drop microextraction
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Application of multi-factorial experimental design to successfully model and optimize inorganic arsenic speciation in environmental water samples by ultrasound assisted emulsification of solidified floating organic drop microextraction

机译:多因子实验设计在凝固浮动有机滴微量萃取的超声辅助乳化中成功模型和优化环境水样中无机砷形态的应用

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Ultrasound assisted emulsification of solidified floating organic drop microextraction (USAE-SFODME), combined with electrothermal atomic absorption spectrometry (ETAAS), was developed for the preconcentration and determination of trace amounts of As(III) and As(V) in environmental water samples. At pH = 1, As(III) formed complexes with ammonium pyrrolidine dithiocarbamate (APDC) and these were extracted into the fine droplets of 1-dodecanol (extraction solvent) which were dispersed with the aid of ultrasonication into the water sample solution. After extraction, the organic phase was separated by centrifugation, and was solidified by transferring into an ice bath. The solidified solvent was transferred to a vial and melted quickly at room temperature. As(III) was determined in the melted organic phase while As(V) remained in the aqueous layer. Total inorganic As was determined after the reduction of the pentavalent forms of arsenic with sodium thiosulphate and potassium iodide. The concentration of As(V) was calculated as the difference between the concentrations of total inorganic As and As(III). The variables of interest in the USAE-SFODME method, such as the volume of extraction solvent, pH, concentration of APDC (chelating agent), sonication time and salt effect were optimized using multivariate optimization approaches. A fractional factorial design (FFD) for screening and a central composite design for optimizing the significant variables were applied. A mathematical model was presented that successfully predicts changes in the response, depending on the input variables. Under the optimum conditions, the proposed method has been successfully used for the determination of inorganic arsenic in different environmental water samples and certified reference material (NIST RSM 1643e).
机译:超声辅助熔融浮动有机液滴微萃取(USAE-SFODME),与电热原子吸收光谱(ETAAs)结合,用于预浓缩和测定环保水样中的痕量为(III)和作为(v)的痕量。在pH = 1中,如(III)用吡咯烷二硫代氨基甲酸铵(APDC)形成的复合物,将它们萃取到1-十二烷醇(萃取溶剂)的细液滴中,借助于超声波分散到水样溶液中。萃取后,通过离心分离有机相,通过转移到冰浴中固化。将固化的溶剂转移到小瓶中并在室温下快速熔化。如(iii)在熔化的有机阶段测定,而AS(v)保持在水层中。在用硫代硫酸钠和碘化钾减少砷的五价形式后测定的总无机。计算为(v)的浓度,作为总无机浓度和作为(III)的浓度之间的差异。利用多元优化方法优化了USAE-SFODME方法的兴趣变量,例如萃取溶剂,pH,APDC浓度(螯合剂),超声处理时间和盐效应。应用用于筛选和用于优化显着变量的中央复合设计的分数因子设计(FFD)。提出了一种数学模型,其成功预测响应的变化,具体取决于输入变量。在最佳条件下,所提出的方法已成功地用于测定不同环境水样和认证的参考材料(NIST RSM 1643E)中的无机砷。

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