Literature DB >> 29405456

A Capillary Flow Dynamics-Based Sensing Modality for Direct Environmental Pathogen Monitoring.

Katherine E Klug1, Kelly A Reynolds2, Jeong-Yeol Yoon1,3.   

Abstract

Toward ultra-simple and field-ready biosensors, we demonstrate a novel assay transducer mechanism based on interfacial property changes and capillary flow dynamics in antibody-conjugated submicron particle suspensions. Differential capillary flow is tunable, allowing pathogen quantification as a function of flow rate through a paper-based microfluidic device. Flow models based on interfacial and rheological properties indicate a significant relationship between the flow rate and the interfacial effects caused by target-particle aggregation. This mechanism is demonstrated for assays of Escherichia coli K12 in water samples and Zika virus (ZIKV) in blood serum. These assays achieved very low limits of detection compared with other demonstrated methods (1 log CFU/mL E. coli and 20 pg/mL ZIKV whole virus) with an operating time of 30 s, showing promise for environmental and health monitoring.
© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  aggregation; biosensors; capillary; microfluidic device; pathogens

Mesh:

Substances:

Year:  2018        PMID: 29405456     DOI: 10.1002/chem.201800085

Source DB:  PubMed          Journal:  Chemistry        ISSN: 0947-6539            Impact factor:   5.236


  6 in total

1.  Smartphone based on-chip fluorescence imaging and capillary flow velocity measurement for detecting ROR1+ cancer cells from buffy coat blood samples on dual-layer paper microfluidic chip.

Authors:  Tiffany-Heather Ulep; Ryan Zenhausern; Alana Gonzales; David S Knoff; Paula A Lengerke Diaz; Januario E Castro; Jeong-Yeol Yoon
Journal:  Biosens Bioelectron       Date:  2020-01-22       Impact factor: 10.618

2.  Natural killer cell detection, quantification, and subpopulation identification on paper microfluidic cell chromatography using smartphone-based machine learning classification.

Authors:  Ryan Zenhausern; Alexander S Day; Babak Safavinia; Seungmin Han; Paige E Rudy; Young-Wook Won; Jeong-Yeol Yoon
Journal:  Biosens Bioelectron       Date:  2021-12-24       Impact factor: 10.618

3.  Machine learning classification of bacterial species using mix-and-match reagents on paper microfluidic chips and smartphone-based capillary flow analysis.

Authors:  Sangsik Kim; Alexander S Day; Jeong-Yeol Yoon
Journal:  Anal Bioanal Chem       Date:  2022-03-28       Impact factor: 4.142

4.  Human sensor-inspired supervised machine learning of smartphone-based paper microfluidic analysis for bacterial species classification.

Authors:  Sangsik Kim; Min Hee Lee; Theanchai Wiwasuku; Alexander S Day; Sujittra Youngme; Dong Soo Hwang; Jeong-Yeol Yoon
Journal:  Biosens Bioelectron       Date:  2021-05-14       Impact factor: 12.545

5.  Smartphone-based sensitive detection of SARS-CoV-2 from saline gargle samples via flow profile analysis on a paper microfluidic chip.

Authors:  Patarajarin Akarapipad; Kattika Kaarj; Lane E Breshears; Katelyn Sosnowski; Jacob Baker; Brandon T Nguyen; Ciara Eades; Jennifer L Uhrlaub; Grace Quirk; Janko Nikolich-Žugich; Michael Worobey; Jeong-Yeol Yoon
Journal:  Biosens Bioelectron       Date:  2022-03-17       Impact factor: 12.545

Review 6.  Microfluidic devices for detection of RNA viruses.

Authors:  Arefeh Basiri; Arash Heidari; Melina Farshbaf Nadi; Mohammad Taha Pahlevan Fallahy; Sasan Salehi Nezamabadi; Mohammadreza Sedighi; Amene Saghazadeh; Nima Rezaei
Journal:  Rev Med Virol       Date:  2020-08-26       Impact factor: 11.043

  6 in total

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