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A Displacement Flow Immunosensor for Explosive Detection Using Microcapillaries

机译:用于微毛细管爆炸检测的位移流免疫传感器

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In this paper, we report on the characterization of a capillary-based displacement flow immunosensor for the explosive, 2,4,6-trinitrotoluene (TNT). Anti-TNT antibody is immobilized onto the silanized inner walls of a fused-silica capillary using a heterobifunctional cross-linker followed by saturating the capillary with fluorophorelabeled antigen. To perform the assay, an aliquot of target analyte solution is injected into the capillary column, The target analyte competes for the binding pocket of the fluorophore-labeled antigen, which is detected down stream. We have demonstrated that the sensitivity of the capillary-based immunosensor is improved by at least 3 orders of magnitude compared to packed-column or membrane-based displacement flow immunosensors. We have investigated the effect of flow rote and length of the capillary on the performance of the capillary immunosensor. We have demonstrated that, by the appropriate choice of an antibody, one can prepare a capillary immunosensor for the specific detection of TNT or as a detector for both TNT and its major metabolites. The limit of detection for TNT using the capillary immunosensor is 440 amol (100 μL injection of 1 pg/mL TNT solution). The relative standard deviation (RSD) of the area under the peaks resulting from 10 back-to-back 100 μL injections of 5 ng/mL TNT to the capillary immunosensor is 4%. The RSD for column-to-column variation is observed to be 5% (n = 5). The entire assay can be performed in less than 3 min.
机译:在本文中,我们报告了基于炸药2,4,6-三硝基甲苯(TNT)的毛细管位移流免疫传感器的特性。使用异双功能交联剂将抗TNT抗体固定在熔融石英毛细管的硅烷化内壁上,然后用荧光团标记的抗原使毛细管饱和。为了进行分析,将等分试样的目标分析物溶液注入毛细管柱中。目标分析物竞争荧光团标记的抗原的结合袋,该结合袋在下游被检测到。我们已经证明,与基于填充柱或膜的置换流免疫传感器相比,基于毛细管的免疫传感器的灵敏度至少提高了3个数量级。我们已经研究了毛细管的流动速率和长度对毛细管免疫传感器性能的影响。我们已经证明,通过适当选择抗体,可以制备用于TNT特异性检测的毛细管免疫传感器,或者作为TNT及其主要代谢物的检测器。使用毛细管免疫传感器检测TNT的极限为440 amol(100μL注射1 pg / mL TNT溶液)。每次向毛细管免疫传感器连续5次注入5 ng / mL TNT进行10次10​​0μL注入后,峰下面积的相对标准偏差(RSD)为4%。观察到的柱间差异的RSD为5%(n = 5)。整个分析可以在不到3分钟的时间内完成。

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