电阻抗
线性
微流控
灵敏度(控制系统)
电子工程
频道(广播)
材料科学
炸薯条
声学
工程类
电气工程
纳米技术
物理
作者
Martin Hantschke,Iasonas F. Triantis
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:22 (1): 16-24
被引量:3
标识
DOI:10.1109/jsen.2021.3127320
摘要
Label-free measurements using impedance sensing could enable microfluidic chip electrophoresis (ME) to be used in point-of-care (POC) diagnostics. However, impedance sensing methods reported in this field need considerable optimisation. GOAL: Develop a novel design process for optimising tetrapolar electrical impedance measurement (TEIM) sensor performance in ME applications through a systematic investigation a) of the impact of a TEIM sensor’s design parameters on its performance in ME and b) of the relationship between the above parameters with those of the microfluidic channel and the sample to be sensed. METHODS: 3D FEM sensitivity simulations, verified experimentally, were carried out to study the impact of sensor and channel parameters on the measured impedance and their interrelationship. Subsequently, the impact of sensor parameters on sensing a sample band’s conductivity and size was investigated. RESULTS: The impact of channel dimensions on transfer impedance measurements is significant. The non-linearity reported for transfer impedance measurement of volume conductors can be manipulated by appropriate sensor parameter design. The sensor performance can be optimised by designing electrode length and measurement electrode distance in relation to the channel height and sample band length, respectively. The sensor performance is not affected by the injection electrode distance. CONCLUSION: There is a relationship between sensor, channel and band parameters and this warrants establishing a systematic design process of TEIM sensors in ME. SIGNIFICANCE: This paper presents a novel approach to optimising the design of TEIM sensors in ME potentially providing significant performance improvements and thus allowing for label-free POC electrophoresis diagnostics.
科研通智能强力驱动
Strongly Powered by AbleSci AI