Supporting data for "3D Printed Self-Supporting Elastomeric Structures for Multifunctional Microfluidics"

  • Ruitao Su (Creator)
  • Jiaxuan Wen (Creator)
  • Qun Su (Creator)
  • Michael S. Wiederoder (Creator)
  • Steven J Koester (Creator)
  • Joshua R. Uzarski (Creator)
  • Michael McAlpine (Creator)

Dataset

Description

Abstract
Microfluidic devices fabricated via soft lithography have demonstrated compelling applications in areas such as rapid biochemical assays, lab-on-a-chip diagnostics, DNA microarrays and cell analyses. These technologies could be further developed by directly integrating microfluidics with electronic sensors and curvilinear substrates as well as reducing the human-centric fabrication processes to improve throughput. Current additive manufacturing methods, such as stereolithography and multi-jet printing, tend to contaminate substrates due to uncured resins or supporting materials that are subsequently evacuated to create hollow fluid passages. Here we present a printing methodology based on precisely extruding viscoelastic inks into self-supporting structures, creating elastomeric microchannels and chambers without requiring sacrificial materials. We demonstrate that, in the sub-millimeter regime, the yield strength of the as-extruded silicone ink is sufficient to prevent creep under the gravitational loading within a certain angular range. Printing toolpaths are specifically designed to realize leakage-free connections between channels and chambers, T-shaped intersections and overlapping channels. The self-supporting microfluidic structures enable the automatable fabrication of multifunctional devices, including multi-material mixers, microfluidic-integrated sensors, automation components and 3D microfluidics.

Description
This data set includes the supporting data for the Science Advances article, 3D Printed Self-Supporting Elastomeric Structures for Multifunctional Microfluidics (DOI: 10.1126/sciadv.abc9846).

Funding information
Sponsorship: Army Research Office, Cooperative Agreement Number: W911NF1820175; Basic research funding from the US Army Combat Capabilities Development Command Soldier Center; National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health, Award number: DP2EB020537; The State of Minnesota MnDRIVE; National Science Foundation through the National Nano Coordinated Infrastructure Network, Award Number: ECCS-1542202
Date made available2020
PublisherData Repository for the University of Minnesota
Date of data productionOct 1 2018 - Jul 20 2020

Cite this