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作 者:翦英红[1,2] 胡艳[3] 王婷[3] 刘建林[3] 张琛[3] 李鱼[3,1]
机构地区:[1]吉林大学环境与资源学院,长春130012 [2]吉林化工学院环境与生物工程学院,吉林132022 [3]华北电力大学能源与环境中心,北京102206
出 处:《分析化学》2010年第1期62-66,共5页Chinese Journal of Analytical Chemistry
基 金:国家“973”计划项目(NO.2004CB418501)资助
摘 要:建立了沉积物中痕量十溴联苯醚的分散液液微萃取-上浮溶剂固化-高效液相色谱-紫外法(DLLME-SFO-HPLC-UV)。以正交试验数据为训练样本,采用BP(Back propagation)神经网络模型优化了分散液液微萃取-上浮溶剂固化条件:分散剂为1.00mL甲醇、萃取剂为35.0μL十二醇、NaCl质量浓度为10.00%、萃取时间10min和pH=5,其萃取率(ER)可达62.22%。方法的线性范围为3.5~1400ng/g(r=0.9960),检出限(LOD)和定量限(LOQ)分别为2.3pg/g(S/N=2)和5.6pg/g(S/N=5),实际样品的加标回收率为97.7%~104.2%。本方法集萃取、富集、分离步骤于一体,简化了沉积物中十溴联苯醚的前处理过程。A method for the determination of decabrominated diphenyl ether(decaBDE) in sediment samples at trace level using dispersive liquid-liquid microextraction based on the solidification of floating organic drop (DLLME-SFO) and high performance liquid chromatography-ultraviolet detector(HPLC-UV) has developed.Based on the data of interactive orthogonal array design,the optimization experimental conditions were obtained with BP artificial neural network model:1.00 mL methanol as dispersive solvent,35.0 μL dodecanol as extractive solvent,10.00% NaCl,pH 5,and extraction in 10 min.The extraction recovery(ER) was 62.22% at the extraction conditions.The proposed method exhibited a wide linear range(3.5-1400 ng/g) with R2=0.9921.The limit of detection(LOD) and the limit of quantification(LOQ) of this method were 2.3 pg/g(S/N=2) and 5.6 pg/g(S/N=5),respectively.The recoveries of real samples at different spiking levels of decaBDE were 104.2%,98.4% and 97.7%,respectively.Extraction,concentration and separation procedures for decaBDE from the sediment sample were carried out by one step,and hence,the process of DLLME-SFO for decaBDE was shortened.
关 键 词:分散液液微萃取 上浮溶剂固化 高效液相色谱-紫外法 沉积物 十溴联苯醚 多层前馈神经网络
分 类 号:X830.2[环境科学与工程—环境工程]
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