Search Results (103)

This study details the design, fabrication, and experimental validation of a wearable, non-invasive microwave sensor for continuous blood glucose monitoring. It incorporates a crescent-loaded elliptical patch antenna with a complementary split-ring resonator (CSRR) tag unit to greatly improve sensing sensitivity. The sensor operates across multiple resonant frequencies, enabling broadband dielectric characterization of glucose-dependent blood permittivity. Incorporation of the CSRR tag unit leads to a marked improvement in electromagnetic coupling and field confinement, resulting in a substantial increase in sensitivity, achieving 1.14 MHz/mg/dL in resonant frequency shift and 0.015 dB/mg/dL in reflection coefficient sensitivity compared to conventional designs. The sensor was fabricated on an FR-4 substrate and experimentally characterized using a vector network analyzer (VNA), showing strong agreement between simulated and measured S11 responses, with minimal frequency deviations and consistent resonance behavior. Experimental results confirmed improved sensitivity in response to glucose concentration variations over the range of 0–500 mg/dL, validating the sensor’s performance under realistic conditions. Furthermore, a physics-informed deep learning (PI-DL) model was developed to predict glucose concentration directly from measured S11 data. The model achieved enhanced prediction accuracy, with a mean absolute error below 1 mg/dL and a strong generalization across unseen samples, demonstrating the power of combining physical modeling with data-driven approaches. These results confirm that the proposed sensor, enhanced with the CSRR tag unit and supported by a PI-DL framework, offers a promising pathway toward next-generation non-invasive, accurate, and wearable glucose monitoring solutions. Full article

This paper presents a high-sensitivity microwave sensor based on a modified Complementary Split Ring Resonator (CSRR) architecture, integrated with inductive stubs, for non-invasive blood glucose monitoring. The proposed sensor is designed to enhance the electric field localization and coupling efficiency by introducing inductive elements that strengthen the perturbation effect caused by glucose concentration changes in the blood. Numerical simulations were conducted using a multilayer finger model to evaluate the sensor’s performance under various glucose levels ranging from 0 to 500 mg/dL. The modified sensor exhibits dual-resonance characteristics and outperforms the conventional CSRR in both frequency and amplitude sensitivity. At an optimized stub gap of 2 mm, which effectively minimizes the capacitive coupling effect of the transmission line and thereby improves the quality factor, the sensor achieves a frequency shift sensitivity of 0.086 MHz/mg/dL and an amplitude sensitivity of 0.02 dB/mg/dL, compared to 0.032 MHz/mg/dL and 0.0116 dB/mg/dL observed in the standard CSRR structure. This confirms a significant enhancement in sensing performance and field confinement due to the optimized inductive loading. These results represent significant enhancements of approximately 168% and 72%, respectively. With its compact design, increased sensitivity, and potential for wearable implementation, the proposed sensor offers a promising platform for continuous, real-time, and non-invasive glucose monitoring in biomedical applications. Full article

This study explored the feasibility of noninvasive blood glucose (BG) estimation using near-infrared (NIR) spectroscopy with dog blood samples. A sensor module employing three representative wavelengths (770 nm, 850 nm, and 970 nm) was tested on an artificial blood vessel (ABV) and a thin pig skin (TPS) model. BG concentrations were adjusted through dilution and enrichment with injection-grade water and glucose solution, and reference values were obtained from three commercial invasive glucometers. Correlations between NIR spectral responses and glucose variations were quantitatively evaluated using linear, multiple, partial least squares (PLS), logistic regression, regularized linear models, and multilayer perceptron (MLP) analysis. The results revealed distinct negative correlations at 850 nm and 970 nm, identifying these wavelengths as promising candidates for noninvasive glucose sensing. Furthermore, an NIR–glucose database generated from actual dog blood was established, which may serve as a valuable resource for the development of future noninvasive glucose monitoring systems. Full article

Metabolic diseases are increasing in relevance both in health and the economy in most countries. In this direction, if gold-standard technologies are based on blood analysis, non-invasive glucose monitoring is a relevant and great challenge that has not yet been fully resolved. Sweat represents a more suitable medium for the non-invasive sensing and monitoring of glucose than other bodily fluids, such as saliva, tears, or urine. However, the measurement of glucose levels requires the use of highly precise and sensitive sensors, given the low glucose concentration in sweat. This paper provides an overview of the patent landscape related to wearable biosensors for the monitoring of glucose levels in sweat. Full article

Milk lactose concentration has been proposed as a noninvasive indicator of metabolic health in dairy cows, particularly during early lactation when metabolic demands are elevated. This study aimed to investigate the relationship between milk lactose levels and physiological, biochemical, and behavioral parameters in early-lactation Holstein cows. Twenty-eight clinically healthy cows were divided into two groups: Group 1 (milk lactose < 4.70%, n = 14) and Group 2 (milk lactose ≥ 4.70%, n = 14). Both groups were monitored over a 21-day period using the Brolis HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania) and SmaXtec intraruminal boluses (SmaXtec Animal Care Technology^®, Graz, Austria). Parameters including milk yield, milk composition (lactose, fat, protein, and fat-to-protein ratio), blood biomarkers, and behavior were recorded. Cows with higher milk lactose concentrations (≥4.70%) produced significantly more milk (+12.76%) and showed increased water intake (+15.44%), as well as elevated levels of urea (+21.63%), alanine aminotransferase (ALT) (+22.96%), glucose (+4.75%), magnesium (+8.25%), and iron (+13.41%) compared to cows with lower lactose concentrations (<4.70%). A moderate positive correlation was found between milk lactose and urea levels (r = 0.429, p < 0.01), and low but significant correlations were observed with other indicators. These findings support the use of milk lactose concentration as a practical biomarker for assessing metabolic and physiological status in dairy cows, and highlight the value of integrating real-time monitoring technologies in precision livestock management. Full article

This paper presents the design of an orientation-independent non-invasive glucose sensor based on a meta-structured antenna. The sensor is designed for blood glucose measurement through fingertip placement on the sensor and features a mushroom structure to generate zeroth-order resonance (ZOR). Moreover, the mushroom structure has a hexagonal patch for orientation-independent non-invasive sensing. The operating frequency of the sensor is 4 GHz, and the overall size is 55 mm × 55 mm. In our study, the range of glucose concentration is from 50 to 250 mg/dL, with a step size of 50 mg/dL. The simulated and measured results show a linear relationship between the resonance frequency and the glucose concentration in the solution, and the linear shift of 0.352 MHz/mg/dL has been observed. On the other hand, the reflection coefficient level variation is a nonlinear function of the glucose concentration for the considered concentration ranges. Mathematical models describing the sensor response across all fingertip orientations are developed for the designed sensor using the regression analysis (R² ≥ 0.993) relating the glucose concentration to the measured resonance frequency and reflection coefficient level. While the reflection coefficient shows a nonlinear response, the resonance frequency exhibits a strong linear correlation with glucose concentration, making it a more reliable parameter for accurate prediction in the proposed sensing model. Full article

Non-invasive near-infrared (NIR) human glucose sensors have attracted great interest in managing diabetes mellitus and those with complex sensing backgrounds due to glucose absorption spectrum overlap. Here, we propose a non-invasive and highly accurate multi-wavelength light NIR glucose sensor using a multilevel metric learning-back propagation network, i.e., “HMML-BP”, based on the narrowband multi-wavelength light NIR system. Our human glucose sensing method combines the advantages of this system and an HMML-BP network. The latter is composed of multilevel metric learning modules and a BP network to predict blood glucose concentrations. The narrowband multi-wavelength light NIR sensing system consists of six-channel NIR filters with center wavelengths of 850 nm, 940 nm, 1300 nm, 1400 nm, 1550 nm, and 1650 nm and a spectral resolution below 12 nm. The six NIR channels measured were first entered into the MML modules to build 3D multi-wavelength light data. Next, 3D multi-wavelength light data were optimized by stochastic neighbor embedding. Diffusion maps and factor analysis algorithms were used to retain effective NIR information. Finally, the optimized data were utilized as the BP network input to predict blood glucose concentrations. The predicted results showed that the factor analysis algorithm had the best performance in our HMML-BP network and that all the predicted glucose values fell into region A, with a mean absolute relative difference of 9.98%, meeting the requirements of daily glucose monitoring. Our blood glucose sensing method provides a new way of utilizing multi-wavelength light and hyperspectral information for smart human glucose monitoring. Full article

Non-invasive glucose monitoring has become a critical area of research for diabetes management, offering a less intrusive and more patient-friendly alternative to traditional methods such as finger-prick tests. This study presents a novel approach using a semi-solid tissue-mimicking phantom designed to replicate the dielectric properties of human skin and blood vessels. The phantom was simplified to focus solely on the skin layer, with embedded channels representing veins to achieve realistic glucose monitoring conditions. These channels were filled with D-(+)-Glucose solutions at varying concentrations (60 mg/dL to 200 mg/dL) to simulate physiological changes in blood glucose levels. A miniature patch antenna optimized to operate at 14 GHz with a penetration depth of approximately 1.5 mm was designed and fabricated. The antenna was tested in direct contact with the skin phantom, allowing for precise measurements of the changes in glucose concentration without interference from deeper tissue layers. Simulations and experiments demonstrated the antenna’s sensitivity to variations in glucose concentration, as evidenced by measurable shifts in the dielectric properties of the phantom. Importantly, the system enabled stationary measurements by injecting glucose solutions into the same blood vessels, eliminating the need to reposition the sensor while ensuring reliable and repeatable results. This work highlights the importance of shallow penetration depth in targeting close vessels for noninvasive glucose monitoring, and emphasizes the potential of microwave-based sensing systems as a practical solution for continuous glucose management. Full article

This research introduces a biosensor utilizing surface plasmon resonance in a photonic crystal fiber (PCF) configuration. PCF uses fused silica as the base material, with a layer of gold placed over the U-channels in the cross-section of the fiber to create a surface plasmon resonance. There are three different sizes of internal fiber optic air hole diameters, with a larger channel circle below the u-channel for the formation of an energy leakage window. COMSOL software 6.0 assisted us in tuning the fiber optic structure and performance for the study, and the structural parameters analyzed mainly include the channel circle diameter, the channel circle spacing, the profundity measurement of the polished layer, and the nanoscale size variation of metal films. The results of the simulation study show that the optical fiber sensor achieves refractive index (RI) responsiveness across the 1.30 to 1.41 range, and in the RI interval of 1.40 to 1.41, the sensor exhibits the largest resonance peak shift, and its highest sensitivity reaches 10,200 nm/RIU, and the smallest full width at half peak (FWHM) corresponds to the RI of 1.34 with a value of 4.8 nm, and the highest figure of merit (FOM) corresponds to the RI of 1.34 with a value of 895.83 (1/RIU). COMSOL 6.0 simulation software, was used to simulate the changes in blood refractive index corresponding to different glucose concentrations, and the detection performance of the sensor for different concentrations of glucose was tested. Then, the results show that the glucose concentration in 75 mg/dL–175 mg/dL with RI detection sensitivity is 3750 nm/RIU, where the maximum refractive index sensitivity is 5455 nm/RIU. It shows that the sensor can be applied in the field of biomedical applications, with its convenience, fast response, and high sensitivity, it has great potential and development prospect in the market. Full article

The widespread use of organophosphate and carbamate pesticides in agriculture poses significant health risks due to their cholinesterase (ChE) inhibitory effects. However, existing detection methods are often expensive and require specialized facilities, limiting their accessibility. This study developed a cost-effective, portable, and sensitive sensor using personal glucose meter (PGM) technology to detect ChE activity in human blood and pesticide residues in vegetables. A thiocholine-based assay was designed to measure ChE activity via PGM, enabling the assessment of enzyme inhibition caused by pesticide exposure. The optimized PGM-based sensor achieved limits of detection (LODs) of 0.138 ppm for mevinphos and 0.113 ppm for carbofuran in standard solutions, with strong correlations (R > 0.99) between standard and fortified samples, indicating high sensitivity and accuracy. The method demonstrated reliable detection of ChE inhibition at pesticide concentrations as low as 0.05 ppm. The developed sensor offers a portable and efficient tool for point-of-care diagnostics, environmental monitoring, and food safety applications. This approach enhances public health protection by enabling accessible pesticide detection. Future work will focus on expanding detection capabilities, improving specificity and stability, and conducting clinical validation for broader applications. Full article

We have fabricated a new highly reproducible, stable, and sensitive cellulose paper-based Surfaced-enhanced Raman scattering (SERS) sensor substrate for non-enzymatic label-free glucose detection. To enhance reproducibility, stability, and sensitivity, the cellulose paper (CP) substrate has been modified with a naturally derived biocompatible polymer, chitosan (CS), followed by depositing enormous amount of plasmonic silver nanoparticles (AgNPs) on CP/CS and finally forming a self-assembling monolayer of 4-mercaptophenyl boronic acid (MPBA) on CP/CS/AgNPs (CP/CS/AgNPs/MPBA). The SERS sensor substrate is characterized by scanning electron microscopy (SEM), energy dispersive X-ray (EDX), Fourier transform infrared (FT-IR), and X-ray diffraction (XRD) spectroscopy techniques. The glucose sensing is achieved by monitoring the SERS intensity of C-S and B-O stretching vibrations at 1072 cm⁻¹ in MPBA, which is gradually increased with increasing concentration of glucose due to the increasing orientation change of MPBA on AgNPs. The results show that the proposed glucose paper-based SERS sensor exhibits a high analytical enhancement factor (AEF) (3.4 × 10⁷), enhanced reproducibility (<7%), improved stability (>5 weeks), excellent selectivity towards other metabolic compounds, and high sensitivity with a limit of detection (LOD) of 0.74 mM and a linear dynamic range between 1.0 and 7.0 mM. The practical application of this SERS sensor is examined in real spiked and non-spiked human blood serum samples for the detection of glucose, and satisfactory recovery results have been obtained, demonstrating the potentiality of the present paper-based SERS sensor for non-enzymatic label-free glucose detection in real biological samples. Full article

In recent years, various types of sensors have been developed at both millimeter (mm) and micrometer (µm) scales for numerous biomedical applications. Each design has its own advantages and limitations. This study compares the electrical characteristics and sensitivity of millimeter- and micrometer-scale sensors, emphasizing the superior performance of millimeter-scale designs for detecting type-2 diabetes. Elevated glucose levels in type-2 diabetes alter the complex permittivity of red blood cells (RBCs), affecting their rheological and electrical properties, such as viscosity, volume, relative permittivity, dielectric loss, and AC conductivity. These alterations may manifest as a unique bio-impedance signature, offering a diagnostic topology for diabetes. In view of this, various concentrations (ranging from 10% to 100%) of 400 µL of normal and diabetic RBCs suspended in phosphate-buffered saline (PBS) solution are examined to record the changes in bio-impedance signatures across a spectrum of frequencies, ranging from 1 MHz to 10 MHz. In this study, simulations are performed using the finite element method (FEM) with COMSOL Multiphysics^® to analyze the electrical behavior of the sensors at both millimeter (mm) and micrometer (µm) scales. These simulations provide valuable insights into the performance parameters of the sensors, aiding in the selection of the most effective design by using this topology. Full article

Mid-infrared spectral analysis has long been recognized as the most accurate noninvasive blood glucose measurement method, yet no practical compact mid-infrared blood glucose sensor has ever passed the accuracy benchmark set by the USA Food and Drug Administration (FDA): to substitute for the finger-pricking glucometers in the market, a new sensor must first show that 95% of their glucose measurements have errors below 15% of these glucometers. Although recent innovative exploitations of the well-established Fourier-transform infrared (FTIR) spectroscopy have reached such FDA accuracy benchmarks, an FTIR spectrometer is too bulky. The advancements of quantum cascade lasers (QCLs) can lead to FTIR spectrometers of reduced size, but compact QCL-based noninvasive blood glucose sensors are not yet available. This work reports on two compact sensor system designs, both reaching the FDA accuracy benchmark. Each design commonly comprises a mid-infrared QCL for emission, a multiple attenuation total reflection prism (MATR) for data acquisition, and a computer-controlled infrared detector for data analysis. The first design translates the comb-like signals into conventional spectra, and then data-mines the resultant spectra to yield blood glucose concentrations. When a pressure actuator is employed to press the patient’s hypothenar against the MATR, the sensor accuracy is considered to reach the FDA accuracy benchmark. The second design abandons the data processing step of translating combs-to-spectra and directly data-mines the “first-hand” comb signal. Beyond increasing the measurement accuracy to the FDA accuracy benchmark, even without a pressure actuator, direct comb data-mining upgrades the sensor system with speed and data integrity, which can impact the healthcare of diabetic patients. Specifically, the sensor performance is validated with 492 glucose absorption scans in the time domain, each with 20 million datapoints measured from four subjects with glucose concentrations of 3.9–7.9 mM. The sensor data-mines 164 sets of critical singularity strengths, each comprising 4 critical singularity strengths directly from the 9840 million raw signal datapoints, and the 656 critical singularity strengths are subjected to a machine-learning regression model analysis, which yields 164 glucose concentrations. These concentrations are correlated with those measured with a standard finger-pricking glucometer. An accuracy of 99.6% is confirmed from the 164 measurements with errors not more than 15% from the reference of the standard glucometer. Full article

With the continuous advancement of contemporary medical technology, an increasing number of individuals are inclined towards self-monitoring their physiological health information, specifically focusing on monitoring blood glucose levels. However, as an emerging flexible sensing technique, continuous and non-invasive monitoring of glucose in sweat offers a promising alternative to conventional invasive blood tests for measuring blood glucose levels, reducing the risk of infection associated with blood testing. In this study, we fabricated a flexible and wearable electrochemical enzyme sensor based on a two-dimensional Ti₃C₂T_x MXene nanosheets and coral-like polyaniline (PANI) binary nanocomposite (denoted as Ti₃C₂T_x/PANI) for continuous, non-invasive, real-time monitoring of sweat glucose. The exceptional conductivity of Ti₃C₂T_x MXene nanosheets, in conjunction with the mutual doping effect facilitated by coral-like PANI, significantly enhances electrical conductivity and specific surface areas of Ti₃C₂T_x/PANI. Consequently, the fabricated sensor exhibits remarkable sensitivity (25.16 μA·mM⁻¹·cm⁻²), a low detection limit of glucose (26 μM), and an extensive detection range (0.05 mM ~ 1.0 mM) in sweat. Due to the dense coral-like structure of Ti₃C₂T_x/PANI binary nanocomposite, a larger effective area is obtained to offer more active sites for enzyme immobilization and enhancing enzymatic catalytic activity. Moreover, the sensor demonstrates exceptional mechanical performance, enabling a 60° bend in practical applications, thus satisfying the rigorous demands of human sweat detection applications. The results obtained from continuous 60 min in vitro monitoring of sweat glucose levels demonstrate a robust correlation with the data of blood glucose levels collected by a commercial glucose meter. Furthermore, the fabricated Ti₃C₂T_x/PANI/GOx sensor demonstrated agreement with HPLC findings regarding the actual concentration of added glucose. This study presents an efficient and practical approach for the development of a highly reliable MXene glucose biosensor, enabling stable and long-term monitoring of glucose levels in human sweat. Full article

A blood glucose concentration and temperature sensor with a balloon-shaped single-mode fiber (SMF) based on a core-offset structure is proposed and experimentally demonstrated. The balloon-shaped SMF is created by offset-fusing a straight-line SMF between two other SMFs, thereby forming a Mach–Zehnder interferometer (MZI). The core-offset structure can effectively excite higher-order cladding modes. The experimental results showed that the maximum sensitivity of blood glucose concentration was 0.331 nm/(mmol/l) and the maximum sensitivity of temperature was 0.216 nm/°C when the offset distance was 10 μm. Dual-parameter measurement was achieved through a dual-parameter matrix. In addition, the sensor has characteristics such as simple structure, low cost, good stability, and electromagnetic interference resistance, making it potentially valuable for diagnosing high blood glucose and related conditions. Full article