Multi‐resonators, microwave microfluidic sensor for liquid characterization

Abduljabar, Ali A.; Hamzah, Hayder; Porch, Adrian

doi:10.1002/mop.32675

Cited by 16 publications

(4 citation statements)

References 17 publications

(29 reference statements)

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“…Sensors of different kinds have already been reported in previous literature [ 3 , 4 , 5 ]. Among all kinds of these sensors, microwave sensors are one of the most widely used sensors [ 6 , 7 , 8 , 9 , 10 ]. In particular, non-invasive sensors for industrial applications are in an increasing need because they can work without physical contact and respond rapidly since they don’t require markers or labels [ 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

High-Sensitivity Liquid Dielectric Characterization Differential Sensor by 1-Bit Coding DGS

Xie

Gao

Wang

et al. 2022

Sensors

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This paper presents two devices to detect the liquid dielectric characterization. The differential method was used to enhance the robustness and reduce tolerance. A basic sensor based on defected ground structure (DGS) was designed and the optimization for the squares of the DGS via adaptive genetic algorithm was applied to enhance the performance of the microwave sensor, which was shown by the difference of the two resonant frequencies. Furthermore, the electric field distribution was enhanced. Glass microcapillary tubes were used to hold samples to provide an environment of non-invasive. The optimized device exhibited the sensitivity of 0.076, which is more than 1.52 times than the basic structure. It could be considered a sensitive and robust sensor with quick response time for liquid dielectric characterization.

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Section: Introductionmentioning

confidence: 99%

High-Sensitivity Liquid Dielectric Characterization Differential Sensor by 1-Bit Coding DGS

Xie

Gao

Wang

et al. 2022

Sensors

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“…Their exceptional performance in non-contact and distant sensing is desired for microfluidics since the need for chemical modification or physical contact with the sample under the test is eliminated [14,16,17]. Their applications for exploring the dielectric properties of materials, especially liquid materials [18], hazardous gas concentration monitoring [19,20], biomedical detection [21], and chemical solutions sensing [22][23][24] have also been demonstrated. It is promising to leverage their sensing potential for droplet microfluidic applications.…”

Section: Introductionmentioning

confidence: 99%

Crosstalk analysis and optimization in a compact microwave-microfluidic device towards simultaneous sensing and heating of individual droplets

Cui

Abbasi²,

Ren³

2022

J. Micromech. Microeng.

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Non-invasive contactless simultaneous sensing and heating of individual droplets would allow droplet microfluidics to empower a wide range of applications. However, it is challenging to realize simultaneous sensing and heating of individual droplets as the resonance frequency of the droplet fluid, which is decided by its permittivity, must be known so that energy is only supplied at this frequency for droplet heating with one resonator. To tailor the energy transfer in real-life heating applications, the droplet has to be sensed first to identify its corresponding resonance frequency, which is used to dynamically tune the frequency for supplying the required energy for heating this particular droplet. To achieve this goal, two resonators are needed, one for sensing and one for heating. Integrating multiple resonators into one typical microfluidic device limits the placement of the resonators to be as close as possible, which would raise the concern of crosstalk between them. The crosstalk would result in inaccurate sensing and heating. This study focuses on numerically and experimentally investigating the effect of influencing parameters on the crosstalk between two adjacent resonators with the ultimate goal of providing guidance for multiplexing the resonators in a typical microfluidic device. ANSYS HFSS is used to perform the electromagnetic analysis based on the finite element method. Experimental studies are conducted on a microfluidic chip integrated with two resonators to validate the numerical results. An optimal distance between two resonators is suggested, with the recommendation for the resonator size and heating power towards simultaneous sensing and heating of individual droplets.

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“…Microwave methods are commonly used to measure the dielectric properties of liquids. A resonator has been designed to characterize the dielectric properties of liquids [12,13]. It was used to determine the alcohol concentration in alcoholic liquids [14].…”

Section: Introductionmentioning

confidence: 99%

Classification of Liquids Using a Patch Antenna and Hierarchical Clustering Algorithms

Efeoglu

Tuna

2021

Mugla Journal of Science and Technology

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Detection of hazardous liquids used in explosive production is important in terms of public safety and health. Because many threats can be prevented by detecting these liquids at security controls points. As existing methods have some disadvantages in terms of accuracy, practicality or reliability, there is a demand for new methods for hazardous liquid detection. In this paper, a circular patch antenna for hazardous liquid detection was designed and by connecting to a vector network analyzer a group of measurements was made. Then, this dataset was used by hierarchical clustering algorithms employed in this study to detect hazardous liquids. The results show that high classification accuracy can be achieved when Ward linkage method is preferred.

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Multi‐resonators, microwave microfluidic sensor for liquid characterization

Cited by 16 publications

References 17 publications

High-Sensitivity Liquid Dielectric Characterization Differential Sensor by 1-Bit Coding DGS

High-Sensitivity Liquid Dielectric Characterization Differential Sensor by 1-Bit Coding DGS

Crosstalk analysis and optimization in a compact microwave-microfluidic device towards simultaneous sensing and heating of individual droplets

Classification of Liquids Using a Patch Antenna and Hierarchical Clustering Algorithms

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