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      Microfluidic paper-based chip platform for formaldehyde concentration detection

      , , ,
      Chemical Engineering Journal
      Elsevier BV

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          Advances in paper-based point-of-care diagnostics.

          Advanced diagnostic technologies, such as polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA), have been widely used in well-equipped laboratories. However, they are not affordable or accessible in resource-limited settings due to the lack of basic infrastructure and/or trained operators. Paper-based diagnostic technologies are affordable, user-friendly, rapid, robust, and scalable for manufacturing, thus holding great potential to deliver point-of-care (POC) diagnostics to resource-limited settings. In this review, we present the working principles and reaction mechanism of paper-based diagnostics, including dipstick assays, lateral flow assays (LFAs), and microfluidic paper-based analytical devices (μPADs), as well as the selection of substrates and fabrication methods. Further, we report the advances in improving detection sensitivity, quantification readout, procedure simplification and multi-functionalization of paper-based diagnostics, and discuss the disadvantages of paper-based diagnostics. We envision that miniaturized and integrated paper-based diagnostic devices with the sample-in-answer-out capability will meet the diverse requirements for diagnosis and treatment monitoring at the POC. © 2013 Elsevier B.V. All rights reserved.
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            Recent developments in paper-based microfluidic devices.

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              Passive mixers in microfluidic systems: A review

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                Author and article information

                Journal
                Chemical Engineering Journal
                Chemical Engineering Journal
                Elsevier BV
                13858947
                January 2018
                January 2018
                : 332
                : 695-701
                Article
                10.1016/j.cej.2017.09.128
                8ad33429-d474-41d2-9b9f-1e33d5d166b9
                © 2018

                https://www.elsevier.com/tdm/userlicense/1.0/

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