Microwave Microfluidic Sensor for Detection of High Equol Concentrations in Aqueous Solution

An Integrated Microfluidic Microwave Array Sensor with Machine Learning for Enrichment and Detection of Mixed Biological Solution

In this work, an integrated microfluidic microwave array sensor is proposed for the enrichment and detection of mixed biological solution. In individuals with urinary tract infections or intestinal health issues, the levels of white blood cells (WBCs) and Escherichia coli (E. coli) in urine or intestinal extracts can be significantly elevated compared to normal. The proposed integrated chip, characterized by its low cost, simplicity of operation, fast response, and high accuracy, is designed to detect a mixed solution of WBCs and E. coli. The results demonstrate that microfluidics could effectively enrich WBCs with an efficiency of 88.3%. For WBC detection, the resonance frequency of the sensing chip decreases with increasing concentration, while for E. coli detection, the capacitance value of the sensing chip increases with elevated concentration. Furthermore, the measurement data are processed using machine learning. Specifically, the WBC measurement data are subjected to a further linear fitting. In addition, the prediction model for E. coli concentration, employing four different algorithms, achieves a maximum accuracy of 95.24%. Consequently, the proposed integrated chip can be employed for the clinical diagnosis of WBCs and E. coli, providing a novel approach for medical and biological research involving cells and bacteria.

De-embedding method for a sensing area characterization of planar microstrip sensors without evaluating error networks

A de-embedding method for determining all scattering (S-) parameters (e.g., characterization) of a sensing area of planar microstrip sensors (two-port network or line) is proposed using measurements of S-parameters with no calibration. The method requires only (partially known) non-reflecting line and reflecting line standards to accomplish such a characterization. It utilizes uncalibrated S-parameter measurements of a reflecting line, direct and reversed configurations of a non-reflecting line, and direct and reversed configurations of the sensing area. As different from previous similar studies, it performs such a characterization without any sign ambiguity. The method is first validated by extracting the S-parameters of a bianisotropic metamaterial slab, as for a two-port network (line), constructed by split-ring-resonators (SRRs) from waveguide measurements. Then, it is applied for determining the S-parameters of a sensing area of a microstrip sensor involving double SRRs next to a microstrip line. The root-mean-square-error (RMSE) analysis was utilized to analyze the accuracy of our method in comparison with other techniques in the literature. It has been observed from such an analysis that our proposed de-embedding technique has the lowest RMSE values for the extracted S-parameters of the sensing area of the designed sensor in comparison with those of the compared other de-embedding techniques in the literature, and have similar RMSE values in reference to those of the thru-reflect-line calibration technique. For example, while RMSE values of real and imaginary parts of the forward reflection S-parameter of this sensing area are, respectively, around 0.0271 and 0.0279 for our de-embedding method, those of one of the compared de-embedding techniques approach as high as 0.0318 and 0.0324.

Microwave Sensor for the Determination of DMSO Concentration in Water-DMSO Binary Mixture

This research aims to develop a microwave sensor to accurately measure the concentration of dimethyl sulfoxide (DMSO) in water-DMSO binary mixtures. The proposed sensor will utilize microwave frequency measurements to determine the DMSO concentration, providing a non-invasive and efficient method for analyzing DMSO solutions. The research will involve the design, fabrication, and testing of the sensor, as well as the development of an appropriate calibration model. The outcomes of this study will contribute to improved monitoring and quality control in various fields, including pharmaceuticals, chemical synthesis, and biomedical research. The binary mixtures of dimethyl sulfoxide (DMSO) and water with varying concentrations were investigated in the frequency range of 1 GHz to 5 GHz at room temperature using a microwave sensor. The proposed microwave sensor design was based on an interdigital capacitor (IDC) microstrip antenna loaded with a hexagonal complementary ring resonator (HCRR). The performance of the sensor, fabricated using the print circuit board (PCB) technique, was validated through simulations and experiments. The reflection coefficient (S₁₁) and resonance frequency (F_r) of binary mixtures of DMSO and water solutions were recorded and analyzed for DMSO concentrations ranging from 0% v/v to 75% v/v. Mathematical models were developed to analyze the data, and laboratory tests showed that the sensor can detect levels of DMSO/water binary mixtures. The sensor is capable of detecting DMSO concentrations ranging from 0% v/v to 75% v/v, with a maximum sensitivity of 0.138 dB/% for S₁₁ and ΔS₁₁ and 0.2 MHz/% for F_r and ΔF_r at a concentration of 50% v/v. The developed microwave sensor can serve as an alternative for detecting DMSO concentrations in water using a simple and cost-effective technique. This method can effectively analyze a wide range of concentrations, including highly concentrated solutions, quickly and easily.

Sensing High 17β-Estradiol Concentrations Using a Planar Microwave Sensor Integrated with a Microfluidic Channel

The global issue of pollution caused by endocrine-disrupting chemicals (EDCs) has been gaining increasing attention. Among the EDCs of environmental concern, 17β-estradiol (E2) can produce the strongest estrogenic effects when it enters the organism exogenously through various routes and has the potential to cause harm, including malfunctions of the endocrine system and development of growth and reproductive disorders in humans and animals. Additionally, in humans, supraphysiological levels of E2 have been associated with a range of E2-dependent disorders and cancers. To ensure environmental safety and prevent potential risks of E2 to human and animal health, it is crucial to develop rapid, sensitive, low cost and simple approaches for detecting E2 contamination in the environment. A planar microwave sensor for E2 sensing is presented based on the integration of a microstrip transmission line (TL) loaded with a Peano fractal geometry with a narrow slot complementary split-ring resonator (PF-NSCSRR) and a microfluidic channel. The proposed technique offers a wide linear range for detecting E2, ranging from 0.001 to 10 mM, and can achieve high sensitivity with small sample volumes and simple operation methods. The proposed microwave sensor was validated through simulations and empirical measurements within a frequency range of 0.5-3.5 GHz. The E2 solution was delivered to the sensitive area of the sensor device via a microfluidic polydimethylsiloxane (PDMS) channel with an area of 2.7 mm² and sample value of 1.37 µL and measured by a proposed sensor. The injection of E2 into the channel resulted in changes in the transmission coefficient (S₂₁) and resonance frequency (F_r), which can be used as an indicator of E₂ levels in solution. The maximum quality factor of 114.89 and the maximum sensitivity based on S₂₁ and F_r at a concentration of 0.01 mM were 1746.98 dB/mM and 40 GHz/mM, respectively. Upon comparing the proposed sensor with the original Peano fractal geometry with complementary split-ring (PF-CSRR) sensors without a narrow slot, several parameters were evaluated, including sensitivity, quality factor, operating frequency, active area, and sample volume. The results showed that the proposed sensor exhibited an increased sensitivity of 6.08% and had a 40.72% higher quality factor, while the operating frequency, active area, and sample volume showed decreases of 1.71%, 25%, and 28.27%, respectively. The materials under tests (MUTs) were analyzed and categorized into groups using principal component analysis (PCA) with a K-mean clustering algorithm. The proposed E2 sensor has a compact size and simple structure that can be easily fabricated with low-cost materials. With the small sample volume requirement, fast measurement with a wide dynamic range, and a simple protocol, this proposed sensor can also be applied to measure high E2 levels in environmental, human, and animal samples.