Literature DB >> 33969565

3D Printing of Monolithic Capillarity-Driven Microfluidic Devices for Diagnostics.

Clement Achille1, Cesar Parra-Cabrera1, Ruben Dochy1, Henry Ordutowski2, Agnese Piovesan3, Pieter Piron1, Lore Van Looy1, Shashwat Kushwaha4,5, Dominiek Reynaerts4,5, Pieter Verboven3, Bart Nicolaï3, Jeroen Lammertyn2, Dragana Spasic2, Rob Ameloot1.   

Abstract

Rapid diagnostic testing at the site of the patient is essential when a fully equipped laboratory is not accessible. To maximize the impact of this approach, low-cost, disposable tests that require minimal user-interference and external equipment are desired. Fluid transport by capillary wicking removes the need for bulky ancillary equipment to actuate and control fluid flow. Nevertheless, current microfluidic paper-based analytical devices based on this principle struggle with the implementation of multistep diagnostic protocols because of fabrication-related issues. Here, 3D-printed microfluidic devices are demonstrated in a proof-of-concept enzyme-linked immunosorbent assay in which a multistep assay timeline is completed by precisely engineering capillary wetting within printed porous bodies. 3D printing provides a scalable route to low-cost microfluidic devices and obviates the assembly of discrete components. The resulting rapid and seamless transition between digital data and physical objects allows for rapid design iterations, and opens up perspectives on distributed manufacturing.
© 2021 Wiley-VCH GmbH.

Entities:  

Keywords:  3D printing; capillarity; microfluidics; point-of-care testing; porous materials

Mesh:

Year:  2021        PMID: 33969565     DOI: 10.1002/adma.202008712

Source DB:  PubMed          Journal:  Adv Mater        ISSN: 0935-9648            Impact factor:   30.849


  2 in total

Review 1.  Lab-on-Chip Microsystems for Ex Vivo Network of Neurons Studies: A Review.

Authors:  Hongyong Zhang; Guoguang Rong; Sumin Bian; Mohamad Sawan
Journal:  Front Bioeng Biotechnol       Date:  2022-02-16

Review 2.  Circuit-Based Design of Microfluidic Drop Networks.

Authors:  Nassim Rousset; Christian Lohasz; Julia Alicia Boos; Patrick M Misun; Fernando Cardes; Andreas Hierlemann
Journal:  Micromachines (Basel)       Date:  2022-07-16       Impact factor: 3.523

  2 in total

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