Literature DB >> 24154859

A programmable and reconfigurable microfluidic chip.

Raphael Renaudot1, Vincent Agache, Yves Fouillet, Guillaume Laffite, Emilie Bisceglia, Laurent Jalabert, Momoko Kumemura, Dominique Collard, Hiroyuki Fujita.   

Abstract

This article reports an original concept enabling the rapid fabrication of continuous-flow microfluidic chips with a programmable and reconfigurable geometry. The concept is based on a digital microfluidic platform featuring an array of individually addressable electrodes. A selection of electrodes is switched on sequentially to create a de-ionized (DI) water finger specific pattern, while the surrounding medium consists of liquid-phase paraffin. The water displacement is induced by both electrowetting on dielectric and liquid dielectrophoresis phenomena. Once the targeted DI water pattern is obtained, the chip temperature is lowered by turning on an integrated thermoelectric cooler, forming channel structures made of solidified paraffin with edges delimitated by the DI water pattern. As a result, the chip can be used afterwards to conduct in-flow continuous microfluidic experiments. This process is resettable and reversible by heating up the chip to melt the paraffin and reconfigure the microchannel design on demand, offering the advantages of cost, adaptability, and robustness. This paper reports experimental results describing the overall concept, which is illustrated with typical and basic fluidic geometries.

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Year:  2013        PMID: 24154859     DOI: 10.1039/c3lc50850a

Source DB:  PubMed          Journal:  Lab Chip        ISSN: 1473-0189            Impact factor:   6.799


  3 in total

1.  Microfluidic assembly kit based on laser-cut building blocks for education and fast prototyping.

Authors:  Lukas C Gerber; Honesty Kim; Ingmar H Riedel-Kruse
Journal:  Biomicrofluidics       Date:  2015-11-18       Impact factor: 2.800

2.  Towards a Multifunctional Electrochemical Sensing and Niosome Generation Lab-on-Chip Platform Based on a Plug-and-Play Concept.

Authors:  Adnane Kara; Camille Rouillard; Jessy Mathault; Martin Boisvert; Frédéric Tessier; Hamza Landari; Imene Melki; Myriam Laprise-Pelletier; Elodie Boisselier; Marc-André Fortin; Eric Boilard; Jesse Greener; Amine Miled
Journal:  Sensors (Basel)       Date:  2016-05-28       Impact factor: 3.576

3.  Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation.

Authors:  Kyriacos Yiannacou; Vipul Sharma; Veikko Sariola
Journal:  Langmuir       Date:  2022-09-13       Impact factor: 4.331

  3 in total

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