461795 Analysis of Graphene Membranes and Time-Varying, Stochastic Gas Transport

Wednesday, November 16, 2016: 3:35 PM
Plaza B (Hilton San Francisco Union Square)
Lee Drahushuk1, Luda Wang2,3, Steven P. Koenig4,5, Kumar Varoon Agrawal1,6, J. Scott Bunch7,8 and Michael S. Strano9, (1)Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, (2)Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, (3)Department of Mechanical Engineering, University of Colorado, Boulder, CO, (4)Graphene Research Centre, National University of Singapore, Singapore, Singapore, (5)Department of Physics, National University of Singapore, Singapore, Singapore, (6)EPFL, Sion, Switzerland, (7)Division of Materials Science and Engineering, Boston University, Brookline, MA, (8)Department of Mechanical Engineering, Boston University, Boston, MA, (9)Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA

Single layer graphene is a promising system as a separation membrane. We consider mechanisms and methods of analysis for gas transport through graphene. We also present a detailed analysis of experimental gas permeation data through single layer graphene membranes under batch depletion conditions parametric in starting pressure for He, H2, Ne, and CO2 between 100 and 670 kPa. We show mathematically that the observed intersections of the membrane deflection curves parametric in starting pressure are indicative of a time dependent membrane permeance (pressure normalized molecular flow). Analyzing these time dependent permeance data for He, Ne, H2, and CO2 shows remarkably that the latter three gases exhibit discretized permeance values that are temporally repeated. Such quantized fluctuations (called “gating” for liquid phase nanopore and ion channel systems) are a hallmark of isolated nanopores, since small, but rapid changes in the transport pathway necessarily influence a single detectable flux. We analyze the fluctuations using a Hidden Markov model to fit to discrete states and estimate the activation barrier for switching at 1.0 eV. This barrier is and the relative fluxes are consistent with a chemical bond rearrangement of an 8–10 atom vacancy pore. Furthermore, we use the relations between the states given by the Markov network for few pores to determine that three pores, each exhibiting two state switching, are responsible for the observed fluctuations; and we compare simulated control data sets with and without the Markov network for comparison and to establish confidence in our evaluation of the limited experimental data set.

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See more of this Session: Carbon-Based Nanostructured Membranes
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