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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3

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Department: search.filters.department.Department of Chemical and Biological EngineeringSubject: search.filters.subject.Electrical electronic engineering

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    Characterization of fluid mixtures at high pressures using frequency response of microcantilevers
    (2017) Bozkurt, Asuman Aşıkoğlu; Jonas, Alexandr; Department of Chemical and Biological Engineering; N/A; Department of Physics; Department of Mechanical Engineering; Department of Chemical and Biological Engineering; Eris, Gamze; Baloch, Shadi Khan; Kiraz, Alper; Alaca, Burhanettin Erdem; Erkey, Can; Researcher; PhD Student; Faculty Memeber; Faculty Member; Faculty Member; Department of Physics; Department of Mechanical Engineering; Department of Chemical and Biological Engineering; Koç University Tüpraş Energy Center (KUTEM) / Koç Üniversitesi Tüpraş Enerji Merkezi (KÜTEM); Koç University Surface Science and Technology Center (KUYTAM) / Koç Üniversitesi Yüzey Teknolojileri Araştırmaları Merkezi (KUYTAM); College of Engineering; Graduate School of Sciences and Engineering; College of Sciences; College of Engineering; College of Engineering; N/A; N/A; 22542; 115108; 29633
    The frequency response of ferromagnetic nickel microcantilevers immersed in binary mixtures of carbon dioxide (CO2) and nitrogen (N-2) at 308 K and pressures up to 23 MPa was investigated. Experimental data were analyzed using the model developed by Sader for a clamped oscillatory beam immersed in a fluid and a very good agreement between the measured resonant frequencies and quality factors (Q factors) and the theoretical predictions of the model with cantilever characteristic parameters regressed from experimental data was observed. This suggested that the data could be used to simultaneously measure the density and the viscosity of fluid mixtures over a wide range of pressures. Subsequently, density and viscosity of binary mixtures of CO2 and N-2 were determined using N-2 as the reference fluid and compared to the predictions of Gerg equation of state and Chung equation, respectively. For the studied fluids with different compositions, the average relative difference between the experimental density values and the values predicted using Gerg equation of state and NIST database ranged from 1.0 to 13%. The average relative difference between the experimental viscosity values and the values obtained using Chung equation and NIST database ranged from 2.4 to 15%. Since the resonant frequency and Q factor were found to vary with composition at a fixed temperature and pressure, the technique can in principle also be used to measure the composition of a binary mixture at a fixed temperature and pressure. The study represents the first systematic attempt to use microcantilevers for the characterization of high-pressure fluid mixtures and paves the way for devising portable sensors for in-line monitoring of thermophysical properties and composition of fluid mixtures under a wide range of environmental conditions. (C) 2017 Elsevier B.V. All rights reserved.
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    Simulation of temporal stochastic phenomena in electronic and biological systems: a comparative review, examples and synergies
    (Institute of Electrical and Electronics Engineers (IEEE), 2013) Department of Electrical and Electronics Engineering; Department of Chemical and Biological Engineering; Demir, Alper; Erman, Burak; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Department of Chemical and Biological Engineering; College of Engineering; College of Engineering; 3756; 179997
    We concentrate on the temporal stochastic behavior of electronic and biological systems due to mainly intrinsic noise and fluctuation phenomena as opposed to extrinsic crosstalk-like interference or statistical variations in system parameters. We provide an overview of modeling and analysis techniques for noise and fluctuation phenomena in biological systems with comparisons to electronic systems. We present several examples where a synergistic cross-fertilization between the two disciplines seems promising. In particular, we discuss the characterization of conformational fluctuations in proteins, the simulation of stochastic behavior in intracellular processes, and the modeling of noise in biological neurons, neuronal networks, nervous system and the brain.