Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.

Browsing by Author "Su, Qun"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Supporting data for "3D Printed Self-Supporting Elastomeric Structures for Multifunctional Microfluidics"
    (2020-07-30) Su, Ruitao; Wen, Jiaxuan; Su, Qun; Wiederoder, Michael S; Koester, Steven J; Uzarski, Joshua R; McAlpine, Michael C; mcalpine@umn.edu; McAlpine, Michael C; University of Minnesota McAlpine Research Group
    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.
  • Thumbnail Image
    Item
    Understanding and Optimizing Graphene Material and Devices for Sensing Applications
    (2020-10) Su, Qun
    Graphene is a novel 2D material with extraordinary potentials in many applications including due to its outstanding electrical properties. In particular, its ultra-sensitive doping effect, outstanding carrier mobility, and large surface-area-to-volume ratio have motivated many research efforts in its chemical and biological sensing applications. However, although high-performance graphene-based sensors have been demonstrated toward various inorganic gas, volatile organic compounds (VOCs), and biomolecules, many obstacles still persist for wide application of graphene-based sensing scheme. This dissertation therefore focuses on these topics and demonstrates novel understanding of graphene-based sensors as well as methodologies for improving their sensing performance. The origin of electrical disorder in graphene was first systematically studied by correlating the doping concentration distribution of graphene to its surface topography. Disorder in graphene is attributed to contact with oxide substrate, embedded ripple structure, and contaminations. Both oxide substrate and contaminations contribute to disorder as doping source, whereas the ripples structure causes inhomogeneous doping interaction with the substrate and hence raises the disorder. Thermal annealing effectively “heals” the topographical unevenness from ripples, but also enhances disorder from the substrate. In the second part, a novel technique for transferring CVD (chemical vapor deposition) graphene from its growth substrate onto arbitrary substrates was developed using fluoropolymer (FP) poly[4,5-difluoro-2,2-bis(trifluoromethyl)-1,3-dioxole-co-tetrafluoroethylene] (Teflon AF1600) as sacrificial layer. This transfer method yields cleaner graphene surface with surface RMS of 0.6 – 1.2 nm and can be applied to larger scale. Moreover, self-assembly molecules can be used as passivation layer during AF1600 transfer process which suppresses the formation of clustered residues. Passivated transfer produces ultra-clean graphene surface with roughness of only 0.4 – 0.5 nm, while the remaining passivation layer can be removed or left to add selectivity for graphene-based sensors. Then, using graphene varactor gas sensors, the interaction mechanisms of gas molecules with graphene was comprehensively studied. Gases like H2O and VOCs can be either loosely bonded to graphene which causes immediate, reversible response in the C-Vg characteristic of varactors due to physisorption-like process, or tightly bonded to graphene which generates a strong, drift response in C-Vg characteristic due to chemisorption-like process. A charge redistribution (CR) model was proposed to explain the reversible signal, which suggests that the doping response arises from the displacement of the charge distribution of the adsorbed molecule driven by the electric fringing field when gated. On the other hand, A charge transfer (CT) model was proposed to explain the drift signal, which states that the signal is originated from the net charge flow enabled by the misalignment of the dynamic fermi energy (EF) of graphene and the highest occupied molecular orbit (HOMO) energy of the adsorbed molecule. These two mechanisms are distinguishable in their temperature dependence and Vg sweeping range dependence. Finally, high-density DEP trapping of 10 kbp DNA molecules using graphene varactors was demonstrated at low voltage. Through selective etching, the excessive edges created provide additional trapping sites. In addition, defect in graphene was found to be nanoscale trapping sites for DEP manipulation, which is promising for DEP integrated graphene sensing scheme.