Search Results (1,048)

High-sensitivity microwave sensing plays a vital role in material characterization and nondestructive testing, with its performance being largely determined by the quality factor (Q factor) of the sensing structure. In this work, a high-Q microwave metasurface sensor based on the mechanism of bound states in the continuum (BIC) is designed and realized to overcome the intrinsic Q-factor limitations of conventional microwave resonators. By introducing a controlled asymmetric perturbation into the meta-atom, a quasi-BIC mode is successfully excited, and its sensing performance is systematically investigated through frequency-domain simulations. The results indicate that the proposed metasurface achieves an exceptionally high radiation Q factor of up to 4599.7 in the microwave band, along with a refractive index sensitivity of 31.267 GHz/RIU. These findings not only demonstrate the significant potential of the BIC mechanism for achieving ultra-high-Q microwave resonators but also provide an effective and promising approach for the development of high-performance microwave sensing systems. Full article

This work presents a novel microwave sensor system for volatile gas detection, integrating sensing elements based on rectangular cavity resonators (RCR) and multiport demodulation circuitry. Initially, a pump-through gas sensing element utilizing an RCR was developed, and its core sensing functionality was experimentally validated. Subsequently, a rat-race coupler was employed to seamlessly integrate two such rectangular cavity resonator elements—serving as reference and sensing branches—within the multiport demodulation network. This configuration enabled an in-depth investigation of the network’s operating principle, elucidating the critical relationship between the reference and sensing arms. The demodulation network translates the critical output phase shift into corresponding power readings. The quantitative relationship linking phase shift to power output was rigorously characterized and utilized as the basis for estimating volatile gas concentration. Finally, a dedicated LabVIEW-based platform was developed for real-time, quantitative volatile gas monitoring. This integrated measurement system demonstrates excellent detection limits (300 ppm for acetone, 200 ppm for ethanol) and exhibits robust mitigation of measurement artifacts caused by ambient temperature and humidity fluctuations. Comprehensive theoretical analysis and experimental results jointly validate the efficacy of the proposed multiport network and RCR volatile gas sensing architecture. Full article

Non-invasive glucose monitoring remains a significant challenge in diabetes management, with existing approaches often limited by poor accuracy, high cost, or patient discomfort. Microwave-based biosensors offer a promising label-free alternative by exploiting the dielectric contrast between glucose and water. This paper presents a compact, dual-band concentric square-shaped split-ring resonator (SRR-type) biosensor fabricated on a low-cost FR-4 substrate for aqueous glucose detection. The sensor leverages electric field confinement in inter-ring gaps to transduce glucose-induced permittivity changes into measurable shifts in resonance frequency and reflection coefficient. Experimental results demonstrate a linear, monotonic response across the clinical range up to 250 mg/dL, with a frequency-domain sensitivity of 1.964 MHz/(mg/dL) and amplitude-domain sensitivity of 0.0332 dB/(mg/dL), achieving high coefficients of determination (R² = 0.9956 and 0.9927, respectively). The design achieves a normalized size of 0.137 λ_g², combining high sensitivity and compact size within a scalable platform. Operating in the UWB-adjacent band (2.76–3.25 GHz), the proposed biosensor provides a practical, reproducible, and PCB-compatible solution for next-generation label-free glucose monitoring. Full article

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

Fill level control is one of the strict checks required when inspecting industrially packaged products. The purpose is both to ensure the content conformity according to the declared label information and to preserve the reliability of brand trust, strongly influenced by the customer’s evenness perception of the marketed items. To this aim, choosing the right technology is not an easy task: content and packaging material properties are essential to establish the suitability of a product to the fill level machine type. In this paper, we propose a novel approach, based on microwaves, to address this issue. The designed microwave inspection system consists of two Vivaldi antennas working between 1 and 18 GHz. We show its applicability to water, oil and alcohol-based products moving on conveyor belts at production speed. The performed experiments demonstrate good accuracy and efficiency of level classification and fault rejection in real-time processing. Full article

Fluorescent carbon dots (CDs) were efficiently synthesized by a one-step microwave-assisted method using diphenylamine as a carbon precursor. The obtained CDs exhibit high stability and strong water solubility. Under UV irradiation, these CDs could emit bright green photoluminescence. These synthesized CDs have an average diameter of 1.8 nm (±0.46) and quantum yield (QY) as high as 44.69% using rhodamine-B as a reference. The CDs’ intensity can be quantitatively quenched by Hg²⁺ and Fe³⁺ ions with high sensitivity and low LOD about 9.58 nM and 22.27 nM, respectively, indicating that the CDs sensors can be potentially applied for Hg²⁺ and Fe³⁺ detection in aqueous solutions. Full article

The spatial resolution of current microwave remote sensing soil moisture (SM) data is about 25 km in global scale. The coarse scale hinders the application of SM product at regional scale. The global 9 km SM can be released by radar observations of Soil moisture Active and Passive (SMAP) satellite since 2015. For the failed radar sensor, SMAP 9 km SM is less than three months. Therefore, European Space Agency Climate Change Initiative (CCI) SM data is downscaled to 9 km using spatial temporal fusion model in the study. And the 43-year 9 km SM is downscaled by CCI data from 1978 to 2020. Results display that downscaled 9 km SM gets more detailed spatial information than CCI data. Moreover, temporal variation of CCI data in anomaly can be well captured by downscaled data. The evaluations against in-situ data indicate that temporal accuracies of downscaled data (r = 0.676, μbRMSE = 0.069 m³/m³) are comparable with CCI data (r = 0.670, μbRMSE = 0.070 m³/m³). Overall, downscaled data improves the spatial resolution of CCI data and inherits the temporal accuracy with slight improvement. Higher spatial resolution SM offers greater application potential. Additionally, the model herein enriches SM downscaling techniques. Full article

Soil moisture (SM) is a key variable regulating land–atmosphere energy exchange, hydrological processes, and ecosystem functioning. Though important, there are still unresolved problems in accurate SM monitoring and the practical application and validation of existing methods. In this review, we integrate mechanistic classification and applicability and constraint discussions to develop a coherent understanding of current SM monitoring approaches. Within this framework, in situ measurements, optical and thermal infrared methods, active and passive microwave remote sensing (RS) techniques, and model-based simulations are compared, and publicly accessible SM dataset products are comparatively analyzed in terms of product characteristics and application limitations. Different from other published reviews, this study covers a large scope of SM monitoring methods varying from in situ observation to RS inversion, and classifies them based on their mechanisms, thereby constructing a complete comparative framework for SM research. Moreover, three types of open-access SM dataset products are investigated, optical and microwave RS products, model simulation and data fusion products, and reanalysis dataset products, and evaluated according to their resolution, depth, applicability, advantages, and limitations. By doing so, it is concluded that in situ observations remain essential for calibration and validation but are spatially limited. Optical and thermal infrared methods are restricted by atmospheric conditions and a shallow penetration depth, while microwave techniques exhibit varying performances under different vegetation and soil conditions. Existing datasets differ significantly in resolution, consistency, and coverage, making no single product universally applicable. Future research should focus on multi-source and spatiotemporal data fusions, the integration of machine learning with physical mechanisms, enhancement for cross-sensor consistency, the establishment of standardized uncertainty evaluation frameworks, and the refinement of high-order RTMs and parameterization. Full article

Efficient soil moisture monitoring is fundamental to precision agriculture, enabling improved irrigation management, enhanced crop productivity, and sustainable water use. This review comprehensively evaluates soil moisture sensing technologies, classifying them into invasive and non-invasive approaches. The underlying operating principles, strengths, and limitations, as well as documented practical applications, are critically discussed for each technology. Invasive methods, including dielectric sensors, matric potential devices, heat-pulse sensors, and microstructured optical fibres, offer high-resolution data but require careful installation and calibration to account for environmental and soil-specific variables such as texture, salinity, and temperature. Non-invasive technologies—such as microwave remote sensing, electromagnetic induction, and ground-penetrating radar—enable large-scale monitoring without disturbing the soil profile; however, they face challenges in terms of resolution, cost, and data interpretation. Key performance factors across all sensor types include installation methodology, environmental sensitivity, spatial representativeness, and integration with decision-support systems. The review also addresses recent innovations such as biodegradable and Micro–Electro–Mechanical Systems sensors, the incorporation of Internet of Things platforms, and the application of artificial intelligence for enhanced data analytics and sensor calibration. While sensor deployment has demonstrated tangible benefits for irrigation efficiency and yield improvement, widespread adoption remains constrained by technical, economic, and infrastructural barriers, particularly for smallholder farmers. The analysis concludes by identifying research gaps and recommending strategies to facilitate the broader uptake of soil moisture sensors, with a focus on cost reduction, calibration standardisation, and integration into climate-resilient agricultural frameworks. Full article

Reliable land surface temperature (LST) data are required for monitoring climate variability, hydrological processes, and land–atmosphere interactions. Yet existing satellite-derived LST products, such as those from thermal infrared (TIR) sensors, are limited by gaps due to clouds, while passive microwave (PMW) observations, though less affected by atmospheric interference, suffer from coarse resolution and larger uncertainty. This study presents the first validated clear-sky merged LST product for the USA and combines downscaled PMW data from AMSR-E and AMSR2 with MODIS TIR observations, using a modified U-Net deep learning network. The merged dataset covers 2004–2021 at 5 km resolution, providing a compromise between spatial detail and robustness. The model performs well, with low mean squared errors and $R^2$ values of 0.80 (day) and 0.75 (night). The merged time series captures seasonal trends and shows a marked reduction in cloud-contamination artefacts compared to MODIS and AMSR signals. Spatially, the product is consistent across sensor transitions and reduces artefacts from TIR cloud contamination. Validation against ground stations shows results between those of TIR and PMW, with better accuracy at night and moderate positive biases influenced by land cover and terrain. Although the merged product does not match the fine resolution of TIR data by choice, it enhances spatial coverage over AMSR alone and temporal completeness over MODIS alone, where single-sensor products are limited. Residual temporal and seasonal biases are moderate, with systematic warm and cold deviations linked to land cover, propagation of emissivity errors, and sampling differences. Strong positive biases remain over terrain with complex surface properties as the downscaled AMSR is closer to MODIS temperatures. Results demonstrate the combined benefits of PMW’s broader coverage and cloud tolerance with TIR’s spatial detail. Overall, results demonstrate the potential of sensor fusion for producing spatially consistent LST records suitable for long-term environmental and climate monitoring. Full article

Designing metal–semiconductor-based core–shell nanostructures with strong interactions is emerging as a unique component for chemiresistor applications. Here, we have developed an effective hydrogen (H₂) gas sensor based on Au-In₂O₃ core–shell nanostructures, which were synthesized through a short-time microwave hydrothermal process. At an optimal temperature of 375 °C, the device based on Au-In₂O₃ displays a high sensitivity of ~42, which is five times greater than that of the In₂O₃ toward 100 ppm of H₂ gas. Moreover, the Au-In₂O₃ sensor showed higher selectivity toward H₂ gas and stability over a long period. The excellent H₂ gas-sensing performance of Au-In₂O₃ core–shell nanoparticles can be credited to the sensitization and Au catalytic effect core, and their strong interaction with the In₂O₃ component. Our work not only accounts for a facile synthesis approach for Au-In₂O₃ core–shell nanoparticles by synergistic properties of Schottky heterojunctions, but also offers a new insight into how strong metal–semiconductor interactions (SMSIs) play a dynamic role in developing high-performance gas-sensing devices. Full article

Using mathematical and physical modeling, we investigate the influence of the configuration and parameters of miniature electromagnetic and mechanical resonators on their output characteristics. Such electromagnetic resonators are required for the microwave excitation of nitrogen-vacancy color centers, which are used as sensors for various physical quantities, including acceleration, force, and magnetic field induction. The mechanical resonators under consideration are designed for measuring acceleration using nitrogen–vacancy color centers. As a result of these studies, we selected the types of miniature electromagnetic and mechanical resonators that ensure the efficient operation of nitrogen–vacancy color center sensors. Full article

Accurate, non-invasive glucose monitoring remains a major challenge in biomedical sensing. We present a high-sensitivity planar microwave biosensor that progresses from a 2-cell hexagonal array to an 8-cell hexagonal array, and finally to a 16-cell double-honeycomb (DHC-CSRR) architecture to enhance field confinement and resonance strength. Full-wave simulations using Debye-modeled glucose phantoms demonstrate that the optimized 16-cell array on a Rogers RO3210 substrate substantially increases the electric field intensity and transmission response |S₂₁| sensitivity compared with FR-4 and previous multi-CSRR designs. In vitro measurements using pharmacy-grade glucose solutions (5–25%) and saline mixtures with added glucose, delivered through an acrylic channel aligned to the sensing region, confirm the simulated trends. In vivo, vector network analyzer (VNA) tests were conducted on four human subjects (60–150 mg/dL), comparing single- and dual-finger placements. The FR-4 substrate (${\mathsf{\epsilon }}_{\mathrm{r}}$ = 4.4) provided higher frequency sensitivity (2.005 MHz/(mg/dL)), whereas the Rogers RO3210 substrate (${\mathsf{\epsilon }}_{\mathrm{r}}$ = 10.2) achieved greater amplitude sensitivity (9.35 × 10⁻² dB/(mg/dL)); dual-finger contact outperformed single-finger placement for both substrates. Repeated intra-day VNA measurements yielded narrow 95% confidence intervals on |S₂₁|, with an overall uncertainty of approximately ±0.5 dB across the tested glucose levels. Motivated by the larger |S₂₁| response on Rogers, we adopted amplitude resolution as the primary metric and built a compact prototype using the AD8302-EVALZ with a custom 3D-printed enclosure to enhance measurement precision. In a cohort of 31 participants, capillary blood glucose was obtained using a commercial glucometer, after which two fingers were placed on the sensing region; quadratic voltage-to-glucose calibration yielded R² = 0.980, root–mean–square error (RMSE) = 2.316 mg/dL, overall accuracy = 97.833%, and local sensitivity = 1.099 mg/dL per mV, with anthropometric variables (weight, height, age) showing no meaningful correlation. Clarke Error Grid Analysis placed 100% of paired measurements in Zone A, indicating clinically acceptable agreement with the reference meter. Benchmarking against commercial continuous glucose monitoring systems highlights substrate selection as a dominant lever for amplitude sensitivity and positions the proposed fully non-invasive, consumable-free architecture as a promising route toward portable RF-based glucose monitors, while underscoring the need for larger cohorts, implementation on flexible biocompatible substrates, and future regulatory pathways. Full article

Optically detected magnetic resonance (ODMR) of nitrogen-vacancy centers in diamonds, in addition to optical excitation with green light, requires microwave excitation and thus a microwave structure. While many different microwave structures including microwave resonators have been presented in the past, none of them fulfilled the need to fit inside the miniaturized quantum magnetometer with limited space used in this work. This is why a novel microwave resonator design using commercially available printed circuit board technology is proposed. It is demonstrated that this design is of small form factor, highly power efficient and low-cost, with very good reproducibility, and in addition, it can be fabricated as a flexible printed circuit board to be bent and thus fit into the miniaturized sensor used in this work. The design choices made for the resonator and the way in which it was trimmed and optimized geometrically are presented and ODMR spectra made with a miniaturized quantum sensor in combination with such a resonator, which was fed by a microwave generator set to different microwave powers, are shown. These measurements revealed that a microwave power of −4 dBm is sufficient to excite the m_s = ±1 states of the nitrogen-vacancy centers, while exceeding −1 dBm already introduces sidebands in the ODMR spectrum. This underlines the efficiency of the resonator in exciting the nitrogen-vacancies of the diamond in the sensor platform used and can lead to development of low-power quantum sensors in the future. Full article