Sensors International最新文献

This paper presents a novel capacitive sensor-based device for detecting type-2 diabetes through blood analysis. The proposed methodology measures changes in the complex permittivity of red blood cells (RBCs) caused by elevated glucose levels, affecting their rheological and electrical properties, such as viscosity, volume, relative permittivity, dielectric loss, and AC conductivity. These changes, well-documented in the literature, alter the bio-impedance signature of RBCs, serving as an indicator for type-2 diabetes. The study examines various concentrations of normal and diabetic RBCs within a frequency range of 50 kHz to 200 kHz, chosen for its relevance to bio-impedance responses. Experimental results show that healthy RBCs in a 200 $\mu$L PBS solution have a complex permittivity (${\varepsilon }_{\text{mix}}$) of 65.12 and conductivity (${\sigma }_{\text{mix}}$) of 0.63 S/m, while diabetic RBCs measure 73.44 and 0.68 S/m, respectively. Additionally, the complex permittivity decreases as the cell concentration increases for both normal and diabetic RBCs. At 100% cell concentration, the average bio-impedance for diabetic blood cells is 50.3 k$\Omega$, compared to 56.7 k$\Omega$ for healthy blood cells over the entire frequency range. The standard deviation of bio-impedance ($Z_{\text{mix}}$) between 50 kHz and 200 kHz highlights the difference between healthy and diabetic RBCs, with 200 kHz measurements proving more reliable. To detect these bio-impedance changes, an interdigitated electrode (IDE) capacitive sensor with 40 capacitive elements was simulated. The complex bio-impedance ($Z_{\text{mix}}$) was measured within the 50 kHz–200 kHz frequency range, providing clear differentiation between healthy and diabetic blood cells. Simulation using Finite Element Method (FEM) through COMSOL® software supports these findings, showcasing the sensor’s efficacy in type-2 diabetes detection.

In this work, stable, spherical silver nanoparticles (MAgNp) were prepared via a green synthesis method using flowers of Myristica fragrans (nutmeg). This flower is abundant in phytochemicals such as saponins that can be utilized as reductants to produce silver nanoparticles. The synthesized nanoparticles were examined using a variety of physico-chemical methods, including transmission electron microscopy (TEM), Dynamic light scattering (DLS), elemental dispersive X-ray spectroscopy (EDX), powder X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and UV–VIS spectrometer. EDX study confirmed the crystalline and face-centered cubic (FCC) structure of AgNP. The majority of particles are present with a higher percentage intensity at an average size of 58.77 nm as revealed in the TEM image, PDI was found to be 0.055. MAgNPs demonstrated perfect activity in the catalytic degradation of methylene blue dye (88 %) and para-nitrophenol (98 %), both anthropogenic pollutants. These nanoparticles were further used as plasmonic sensors to detect heavy metals like Fe(II) and Hg(II) in an aqueous solution. The minimum detection limit was found to be 0.2 mM for Hg(II) and 10  μM for Fe(II) with good linearity. The electrochemical properties of MAgNPs were studied using a carbon supercapacitor electrode coated with MAgNPs. Results from cyclic voltammetry were also determined, and they showed a high specific capacitance of 41 F/gm at 5 mV/s scan rate.

This paper presents a novel technique to classify the flow regimes in bubble columns. The ultrasonic velocity profiler is employed to detect the velocity deviation and echo characteristic of bubbles rising in the column. This information is set as attribute data for the machine learning algorithm. Classification-based machine learning is utilized to classify the flow regimes: bubbly, transition, and churn turbulent, which are defined as categories of the algorithm. Several classifiers were applied in this work, such as K-nearest neighbors, Decision tree, Support vector machines, Naive bayes, and Logical regression. The experimental demonstration was conducted to verify the performance of the proposed technique. Three kinds of two-phase flow with stagnant liquid that had various viscosities were used for the experiment. The air within the superficial velocity range was injected to alter the flow regime. The flow regime classification model was set. The proposed method was applicable to identify the flow regimes. The classifiers were tested, and their accuracy was evaluated.

The increasing incidence of meat adulteration and mislabeling poses significant challenges in terms of food safety and consumer trust. This study proposes an electrochemical DNA biosensor for detecting porcine mitochondrial DNA in tainted meat products, offering a novel approach to address the above challenges. Unlike conventional nucleic acid amplification tests that rely on polymerase chain reactions (PCRs), the proposed biosensor employs a molecularly amplified DNA strategy with DNA tracers that bind to two regions of the target DNA, creating an elongated hybridization structure with multiple redox-tagging molecules. This design catalyzes detection signals autonomously, eliminating the need for PCR amplification. One-step DNA probe immobilization using poly-adenine (poly-A) oligonucleotides significantly improves hybridization efficiency and reduces the necessity for extensive sample purification, thereby simplifying the detection process. The proposed biosensor exhibits a linear detection range of 10¹–10⁶ pM and a limit of detection (LOD) of 2.2 pM in controlled settings. Furthermore, the proposed biosensor distinguishes pork from beef in adulterated samples with a LOD of 1 % w/w. With its stability exceeding 9 weeks and a cost of less than 0.5 USD per test, the proposed biosensor offers a highly sensitive, economically viable solution with significant potential for widespread use in the meat industry and by end-users, effectively combating porcine adulteration.