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天文系 Colloquium: Probing Planet Formation with the Most Extreme Cases
清华大学材料科学与工程研究院《材料科学论坛》:Magnetocaloric effect: from the...
Data-driven Discovery of Optimization-based Decision-making Models
Toward the Efficient Operation of an Electrified Chemical Industry
报告题目:
Admittance Detection in Microsystems
 报告人:
Purnendu K. (Sandy) Dasgupta
Professor
University of Texas at Arlington
报告时间:
2014-09-19 15:00
报告地点:
何添楼 406 会议室
主办单位:
化学系
  简介:

Abstract

Techniques that have been variously termed oscillometric detection or (capacitively coupled) contactless conductivity detection (C4D) are known actually to respond to the admittance. It is not often appreciated that the frequency range (f) over which such systems respond (quasi)linearly with the cell conductance decreases acutely with increasing cell resistance. Guidance on optimum operating conditions for high cell resistance, such as for very small capillaries/channels and/or solutions of low specific conductance (σ), is scant. Several theoretical models exist but none take the capacitance of the solution being measured into account. At high frequencies and low σ values, much of the current passes through the solution behaving as a capacitor and the capacitance is not very dependent on the exact solution capacitance, resulting in poor, zero, or even negative response. We investigated, both theoretically and experimentally capillaries of inner radii 5-160 μm and σ = ~1-1400 μS/cm, resulting in cell resistances of 51 GΩ-176 kΩ. A 400-element discrete model was used for simulating the behavior. As model inputs, both the wall capacitance and the stray capacitance were measured. The solution and leakage capacitances were estimated from extant models. The model output was compared to the measured response of the detection system over broad ranges of f and σ. Other parameters studied include capillary material and wall thickness, electrode spacing and length, Faraday shield thickness, excitation wave forms and amplitude. The simulations show good qualitative agreement with experimental results and correctly predict the negative response behavior observed under certain conditions. We provide optimum frequencies for different operating conditions. We utilize these learnings to build detectors that cost less than US $100 and can sensitively perform in systems as small as 1 μm in radius

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