Integrated Control-Fluidic Codesign Methodology for Paper-Based Digital Microfluidic Biochips

Abstract
Paper-based digital microfluidic biochips (P-DMFBs) have recently emerged as a promising low-cost and fast-responsive platform for biochemical assays. In P-DMFBs, electrodes and control lines are printed on a piece of photo paper using an inkjet printer and carbon nanotubes (CNTs) conductive ink. Compared with traditional digital microfluidic biochips (DMFBs), P-DMFBs enjoy significant advantages, such as faster in-place fabrication with printer and ink, lower costs, and better disposability. Since electrodes and CNT control lines are printed on the same side of the paper, a critical design challenge for P-DMFB is to prevent control interference between moving droplets and the voltages on CNT control lines. Control interference may result in unexpected droplet movements and thus incorrect assay outputs. To address this design challenge, a control-fluidic codesign methodology is proposed in this paper, along with two demonstrative design flows integrating both fluidic design and control design, i.e., the droplet-oriented codesign flow and the electrode-oriented codesign flow. The droplet-oriented flow is suitable for designing biochips with sparse electrodes and relatively larger number of droplets, whereas the electrode-oriented flow is suitable for biochips with dense electrodes and smaller number of droplets. The computational simulation results of real-life bioassays demonstrate the effectiveness of the proposed codesign flows.
Funding Information
  • National Natural Science Foundation of China (61674093)
  • Tsinghua University (20141081203)
  • Ministry of Science and Technology, Taiwan (MOST 105-2221-E-007-118-MY3, 104-2220-E-007-021)
  • Technical University of Munich-Institute for Advanced Study through the German Excellence Initiative and the European Union Seventh Framework Program (291763)
  • Leading Foreign Research Institute Recruitment Program (2013K1A4A3055268)
  • Ministry of Science, ICT and Future Planning (2016R1A2B3015239)
  • Deutsche Forschungsgemeinschaft
  • National Natural Science Foundation of China (61774091)

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