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    Enzyme-linked immunosorbent assay is a widely used analytical technique for detecting and quantifying disease-specific protein biomarkers. Despite recent progresses in disease-specific protein biomarkers detection with microfluidic chips, many devices still suffer from the limited mass transport of target molecules, and consequently low detection efficiency or long incubation time. In this work, we present a novel strategy to significantly enhance the sensing efficiency of a chip-based biosensor by exploiting micro-streaming in an acoustofluidic device, which boosts intermolecular interactions and a hybridization chain reaction to increase the fluorescent signals. This device was made of a microfluidic chip that contains an array of PDMS micropillars in a ship-shaped microchannel. And the inner surface of the channel was functionalized with capture aptamers that bind with thrombin, chosen as a model target molecule. An ultrasonic transducer underneath the chip operating at 150 kHz generates circular micro-streaming flows around the pillars that significantly improves the binding efficiency of thrombin with capture aptamers by 1) increasing the retention time and 2) enhancing mass transport via local convection versus diffusion. The effects of ultrasound parameters, such as operating frequencies and voltages, on the distribution and magnitude of flows were optimized to obtain a better performance of the sensor chip. Under the optimized conditions, the detection limit was increased by one order of magnitude. Although this work has focused on the detection of thrombin as a model molecule, this streaming-enhanced, microstructure-based sensing strategy can be applied to detect a wide range of molecules or even cells. Copyright © 2020 Elsevier B.V. All rights reserved.

    Citation

    Min Zhou, Dan Gao, Zhou Yang, Chao Zhou, Ying Tan, Wei Wang, Yuyang Jiang. Streaming-enhanced, chip-based biosensor with acoustically active, biomarker-functionalized micropillars: A case study of thrombin detection. Talanta. 2021 Jan 15;222:121480

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    PMID: 33167205

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