Jump to main content
Jump to site search
Access to RSC content Close the message box

Continue to access RSC content when you are not at your institution. Follow our step-by-step guide.


Issue 15, 2016
Previous Article Next Article

Acoustically-driven thread-based tuneable gradient generators

Author affiliations

Abstract

Thread-based microfluidics offer a simple, easy to use, low-cost, disposable and biodegradable alternative to conventional microfluidic systems. While it has recently been shown that such thread networks facilitate manipulation of fluid samples including mixing, flow splitting and the formation of concentration gradients, the passive capillary transport of fluid through the thread does not allow for precise control due to the random orientation of cellulose fibres that make up the thread, nor does it permit dynamic manipulation of the flow. Here, we demonstrate the use of high frequency sound waves driven from a chip-scale device that drives rapid, precise and uniform convective transport through the thread network. In particular, we show that it is not only possible to generate a stable and continuous concentration gradient in a serial dilution and recombination network, but also one that can be dynamically tuned, which cannot be achieved solely with passive capillary transport. Additionally, we show a proof-of-concept in which such spatiotemporal gradient generation can be achieved with the entire thread network embedded in a three-dimensional hydrogel construct to more closely mimic the in vivo tissue microenvironment in microfluidic chemotaxis studies and cell culture systems, which is then employed to demonstrate the effect of such gradients on the proliferation of cells within the hydrogel.

Graphical abstract: Acoustically-driven thread-based tuneable gradient generators

Back to tab navigation

Supplementary files

Article information


Submitted
07 Aug 2015
Accepted
12 Jun 2016
First published
14 Jun 2016

Lab Chip, 2016,16, 2820-2828
Article type
Paper
Author version available

Acoustically-driven thread-based tuneable gradient generators

S. Ramesan, A. R. Rezk, K. W. Cheng, P. P. Y. Chan and L. Y. Yeo, Lab Chip, 2016, 16, 2820
DOI: 10.1039/C5LC00937E

Social activity

Search articles by author

Spotlight

Advertisements