Microwave Sensors Research Articles

Simultaneously Detecting the Power and Temperature of a Microwave Sensor via the Quantum Technique

This study introduces a novel method for the simultaneous detection of microwave sensor power and temperature, leveraging nitrogen-vacancy (NV) centers as a robust quantum system. Through precise measurement of the optical detection magnetic resonance contrast in NV centers, the microwave power is accurately determined. Furthermore, the temperature of the sensor is obtained by monitoring the variations in zero-field splitting and thorough spectral analysis. This method enables the efficient real-time acquisition of synchronized data on both microwave power and temperature from the sensor, facilitating concurrent monitoring without the necessity of additional sensing devices. Finally, we verified that the magnetic sensitivity of the system is approximately 1.2 nT/Hz1/2, and the temperature sensitivity is around 0.38 mK/Hz1/2. The minimum resolution of microwave power is about 20 nW. The experimental results demonstrate that this quantum measurement technique provides stable and accurate data across a wide range of microwave power and temperature conditions. These findings indicate substantial potential for this technique in advanced applications such as aerospace, medical diagnostics, and high-frequency communications. Future studies will aim to extend the industrial applicability of this method by refining quantum control techniques within NV center systems.

Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques

Soil moisture (SM) is a critical variable influencing various environmental processes, but traditional microwave sensors often lack the spatial resolution needed for local-scale studies. This study develops a novel stacking ensemble learning framework to enhance the spatial resolution of satellite-derived SM data to 1 km in the Urmia basin, a region facing significant water scarcity. We integrated in-situ SM measurements (obtained using time-domain reflectometry [TDR]), Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM products, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, precipitation records, and topography data. Ten base machine-learning models were evaluated using the Complex Proportional Assessment (COPRAS) method, and the top-performing models were selected as base learners for the stacking ensemble. The ensemble model, incorporating Random Forest, Gradient Boosting, and XGBoost, significantly improved SM estimation accuracy and resolution compared to individual models. The XGBoost and Gradient Boosting meta-models achieved the highest accuracy, with an unbiased root mean square error (ubRMSE) of 1.23% m3/m3 and a coefficient of determination (R2) of 0.97 during testing, demonstrating the exceptional predictive capabilities of our approach. SHapley Additive exPlanations (SHAP) analysis revealed the influence of each base model on the ensemble’s predictions, highlighting the synergistic benefits of combining diverse models. This study establishes new benchmarks for soil moisture monitoring by showcasing the potential of ensemble learning to improve the spatial resolution and accuracy of satellite-derived SM data, providing crucial insights for environmental science and agricultural planning, particularly in water-stressed regions.

Nonlinearity-enhanced continuous microwave detection based on stochastic resonance.

In practical sensing tasks, noise is usually regarded as an obstacle that degrades the sensitivity. Fortunately, stochastic resonance can counterintuitively harness noise to notably enhance the output signal-to-noise ratio in a nonlinear system. Although stochastic resonance has been extensively studied in various disciplines, its potential in realistic sensing tasks remains largely unexplored. Here, we propose and demonstrate a noise-enhanced microwave sensor using a thermal ensemble of interacting Rydberg atoms. Using the strong nonlinearity present in the Rydberg ensembles and leveraging stochastic noises in the system, we demonstrate the stochastic resonance driven by a weak microwave signal (from several microvolts per centimeter to millivolts per centimeter). A substantial enhancement in the detection is achieved, with a sensitivity surpassing that of a heterodyne atomic sensor by 6.6 decibels. Our results offer a promising platform for investigating stochastic resonance in practical sensing scenarios.

Ready-To-Use Microwave Sensor Modified by Antibody-AuNPs Nanoconjugate for Highly Sensitive and Selective Detection of the SARS-CoV-2 Virus

Ready-To-Use Microwave Sensor Modified by Antibody-AuNPs Nanoconjugate for Highly Sensitive and Selective Detection of the SARS-CoV-2 Virus

Multi-parameter simultaneous extraction with a novel microwave sensor based on coupled resonators

This work presents a microwave resonant multi-parameter sensor devoted to the simultaneous extraction of three characteristics of a homogeneous solid sample: its dielectric permittivity, its loss tangent and its thickness. The device is composed of three coupled resonators in two different substrate boards, having the sample between the boards, in a sandwich configuration. Presence of the sample impacts the electrical response of the device, not only influencing resonators, but also by affecting inter-resonator couplings. A method to analyse the response of the device, allowing for the extraction of the desired characteristics of the sample is proposed, as well as an experimental calibration procedure. The model is built upon 990 simulations, calibrated with three reference-samples measurements and then tested over 18 experimental measurements, with good results, thereby validating the multi-parameter sensing approach.

An Electrically Small Patch Antenna Sensor for Salt Concentration Measurement of NaCl Solution.

In this paper, a complementary split-ring resonator (CSRR)-based patch antenna is proposed as a microwave sensor to measure the salt concentration of NaCl solution. The microwave sensor consists of an RF-4 substrate, where a small copper disc is attached on the top as the radiator, a larger copper disc integrated with two CSRRs is attached on the bottom side as the finite ground plane, and a coaxial feeding port is introduced at the ground plane center. During salt concentration sensing, only the top disc is immersed into NaCl solution. The results indicate that the proposed microwave sensor can measure salt concentrations ranging from 5‱ to 35‱ with a maximum sensitivity of 0.367 (kHz/(mg/L)). The proposed microwave sensor is low-cost, low-profile, electrically small, lightweight, and easy to fabricate, and it also can be applied to other solutions' concentration sensing.

Gelatin-Coated High-Sensitivity Microwave Sensor for Humidity-Sensing Applications.

In this paper, the humidity-sensing characteristics of gelatin were compared with those of poly(vinyl alcohol) (PVA) at L-band (1 ~ 2 GHz) microwave frequencies. A capacitive microwave sensor based on a defected ground structure with a modified interdigital capacitor (DGS-MIDC) in a microstrip transmission line operating at 1.5 GHz without any coating was used. Gelatin is a natural polymer based on protein sourced from animal collagen, whereas PVA is a high-sensitivity hydrophilic polymer that is widely used for humidity sensors and has a good film-forming property. Two DGS-MIDC-based microwave sensors coated with type A gelatin and PVA, respectively, with a thickness of 0.02 mm were fabricated. The percent relative frequency shift (PRFS) and percent relative magnitude shift (PRMS) based on the changes in the resonant frequency and magnitude level of the transmission coefficient for the microwave sensor were used to compare the humidity-sensing characteristics. The relative humidity (RH) was varied from 50% to 80% with a step of 10% at a fixed temperature of around 25 °C using a low-reflective temperature and humidity chamber manufactured with Styrofoam. The experiment's results show that the capacitive humidity sensitivity of the gelatin-coated microwave sensor in terms of the PRFS and PRMS was higher compared to that of the PVA-coated one. In particular, the sensitivity of the gelatin-coated microwave sensor at a low RH from 50% to 60% was much greater compared to that of the PVA-coated one. In addition, the relative permittivity of the fabricated microwave sensors coated with PVA and gelatin was extracted by using the measured PRFS and the equation was derived by curve-fitting the simulated results. The change in the extracted relative permittivity for the gelatin-coated microwave sensor was larger than that of the PVA-coated one for varying the RH.

Operation Temperature Effects on a Microwave Gas Sensor with and without Sensitive Material.

Microwave gas sensors have garnered attention for their high sensitivity and selectivity in the detection of volatile organic compounds (VOCs). However, traditional gas sensors generally rely on sensitive materials that degrade over time and are easily affected by the environment, compromising their stability and accuracy. This study proposes a microwave VOC gas sensor based on the condensation effect. The sensor adopts a novel design without sensitive materials, utilizing the condensation effect to detect acetone gas. The sensor system consists of a microwave sensor and a temperature control device. As the sensor temperature is lowered below the boiling point of acetone, the condensation of acetone gas on the sensor surface is achieved, enabling accurate detection of acetone gas. Experimental results indicate that the accumulated amount of acetone on the sensor surface is positively correlated with its response, with the maximum response of 3000 ppm acetone gas reaching 0.34 dB. Additionally, this study investigated the detection mechanism of the sensor after adding the sensitive material MXene and compared the performance of the sensor at different temperatures (-10 °C, 0 °C, and 60 °C). The results show that at -10 °C the sensor mainly captures acetone through physical adsorption, while at 25 and 60 °C, it primarily responds through chemical adsorption, with a maximum response of 0.29 dB. The VOC sensor based on the condensation effect without sensitive materials not only achieves the same sensitivity as traditional microwave sensors but also demonstrates stronger stability and anti-interference capabilities.

Employing a multi-resonance microwave sensor for in-line moisture monitoring of fluidized bed agglomeration

Amorphous substances are relatively common in food industry and their characteristics depend highly on the moisture content. Process parameters are adjusted based on online measurements to control the product quality of the agglomerates. This leads to two major challenges: precise moisture content measurements of amorphous particles at (i) different temperatures and for (ii) a wide moisture content range.In this study two workflows (inline fluidized bed and conditioned fixed bed) calibration of microwave sensors are compared and it is shown that for fluidized beds inline calibration is necessary to achieve accurate moisture measurements. The application of the novel microwave sensor MW 4200 working at resonance frequencies of 2.3 and 5.6 GHz is described. The temperature influence on the measurement is investigated with measurements of a fixed particle bed. Rapid, reliable in-process moisture control at a wide range of moisture contents and temperatures for agglomeration in fluidized beds, with a mean relative accuracy of 82 %, is achieved. This technology improves productivity and precision in industrial operations, minimizes waste and optimizes energy usage.

RFSoC Softwarisation of a 2.45 GHz Doppler Microwave Radar Motion Sensor

Microwave Doppler sensors are used extensively in motion detection as they are energy-efficient, small-size and relatively low-cost sensors. Common applications of microwave Doppler sensors are for detecting intrusion behind a car roof liner inside an automotive vehicle and to detect moving objects. These applications require a millisecond response from the target for effective detection. A Doppler microwave sensor is ideally suited to the task, as we are only interested in movement of a large water-based mass (i.e., a person) (FMCW Radar also detect static objects). Although microwave components at 2.45 GHz are now relatively cheap due to mass production of other Industrial Scientific and Medical application (ISM) devices, they do require tuning for temperature compensation, dielectric, and manufacturing variability. A digital solution would be ideal, as chip solutions are known to be more repeatable, but Application-Specific Integrated Circuits (ASICs) are expensive to initially prototype. This paper presents the first completely digital Doppler motion sensor solution at 2.45 GHz, implemented on the new RFSoC from Xilinx without the need to up/downconvert the frequency externally. Our proposed system uses a completely digital approach bringing the benefits of product repeatability, better overtemperature performance and softwarisation, without compromising any performance metric associated with a comparable analogue motion sensor. The RFSoC shows to give superior distance versus false detection, as the Signal-to-Noise Ratio (SNR) is better than a typical analogue system. This is mainly due to the high gain amplification requirement of an analogue system, making it susceptible to electrical noise appearing in the intermediate-frequency (IF) baseband. The proposed RFSoC-based Doppler sensor shows how digital technology can replace traditional analogue radio frequency (RF). A case study is presented showing how we can use a novel method of using multiple Doppler channels to provide range discrimination, which can be performed in both analogue and in a digital implementation (RFSoC).

Wideband CPW fed four port MIMO antenna based microwave sensor for deep brain temperature measurement

This study presents a wideband CPW fed four-port multi-input multi-output (MIMO) antenna-based microwave sensor for measuring deep brain temperature inside human head. The range of frequency is from 5.45 to 8.56 GHz at the resonance frequency of 7.25 GHz. Coplanar Waveguide (CPW) fed approach on a low-cost FR4 substrate is used in designing the planar antenna. The presented antenna is compact, having overall dimensions of 40 × 40 × 1.6 mm3. In the operational frequency range, the obtained diversity performance for MIMO antenna shows Envelope Correlation Coefficient (ECC) < 0.05, Diversity Gain > 9.95, total active reflection coefficient (TARC ) < −13 dB and channel capacity loss (CCL) < 0.125 bits/S/Hz. For deep brain temperature measurements, the proposed sensor provides electric field of 3.92 V m−1 and specific absorption rate (SAR) value of 0.16 W kg−1 at the 9 mm distance inside the human head Phantom. The proposed sensor is capable of measuring 0.1 °C change in temperature at 10 W of input power for 60 s when all four ports are active.

Scale reduction Transformer-based soft measurement of oil–water two-phase flow

In addressing the challenge of parameter measurement in high-water-content oil–water two-phase flow, we independently develop a dual-helix microwave sensor measurement system. Using this system, we conduct vertically upward experiments on oil–water two-phase flow and collect time-series signals characterizing microwave attenuation. Based on signal characteristics, we design a scale reduction Transformer soft measurement model (SRSM-Transformer). The time filter module effectively facilitates the aggregation and embedding of channel features with diverse physical significance. Additionally, the scale reduction temporal module models global information from a multi-resolution perspective, enabling the model to flexibly extract multi-scale feature information from fluid signals. Demonstrating pronounced superiority over existing models, our proposed model achieves recognition accuracies of 93.63% for total flow rate and 95.73% for water content. This research provides a new direction for modeling complex industrial oil–water two-phase flow systems and measuring multi-coupled key parameters.

Microwave Sensor for Sodium Chloride Density Measurement in Aqueous Solutions

Accurate determination of sodium chloride (NaCl) density in water is vital for assessing environmental impact, preventing soil salinization in agriculture, ensuring quality and consistency in industrial processes, facilitating medical treatments, and maintaining taste and preservation standards in the food and beverage industry. This paper introduces a novel microwave sensor design specifically tailored to accurately assess NaCl density in aqueous solutions. Starting with a standard solution of 10 g of salt dissolved in 100 ml of water, resulting in a molarity of approximately 1.71 M, five distinct samples are meticulously prepared. These samples cover a range of NaCl concentrations, with different ratios of salt solution and drinking water, including pure water, 10 ml of salt solution with 90 ml of water, 20 ml of salt solution with 80 ml of water, 30 ml of salt solution with 70 ml of water, and 40 ml of salt solution with 60 ml of water. Each sample undergoes analysis using the developed microwave sensor to determine its transmission coefficient. The magnitude of the transmission coefficient is closely tied to the density of the salt solution based on molarity. Through a detailed regression analysis, a strong quantitative relationship between the transmission coefficient and salt solution density is revealed. This correlation can be accurately represented by a third-order polynomial equation. This research is significant as it advances microwave sensor technology, allowing for accurate and efficient measurement of NaCl density in water.

Metamaterial enhanced sensor for powder material classification

In this paper, a simple and efficient approach is presented to classify different power materials based on a one port microwave sensor in X-band. This classification focuses on powder materials, unlike prior studies that focused on liquids (castor oil, neem oil, sunflower oil, sesame oil, and mahua oil), this classification represents a shift towards powdered materials. The response of the proposed sensor is enhanced by adding a metamaterial (MTM) unit cell of F-shape to focus electromagnetic waves on the sample under test. This metamaterial-based sensor is designed to differentiate between different types of materials based on the corresponding reflection coefficient. The sample under test is included inside a dielectric box inserted inside a rectangular waveguide. The MTM unit cell is added on the front face of this box towards the direction of the incident wave. The resonance frequency depends on the characteristics of the powder material inside the box. The MTM unit cell enhances this resonance to simplify the process of classification of different materials. The measured results show that the proposed sensor can detect a wide range of powder materials, including clay, cement, sand, and mixtures: cement & sand, and clay & sand. The designed sensor can be used in various applications, including detection and classification of different powder materials in industrial applications.

Flexible surface plasmon based coupled triple band UHF-microwave sensor for glucose sensing application

Although the correlation between a glucose concentration and its permittivity is somewhat weak to be measured, the glucose concentration is a strong function of the dispersion. In terahertz or microwave frequencies, dispersion can be observed or measured along the interface between an object under test and a metamaterial or surface plasmonic surface, which is basically a metal structure characterized by periodically arrayed holes, grooves, or metal grating. In this work, we have focused on the method for improving the accuracy of a glucose measurement by proposing a new triple-band microwave sensor design and by measuring the resonant frequency shift associated with a glucose concentration at three frequencies simultaneously. A new triple-band glucose sensor of dimension (30 mm x 10 mm) was designed with the main sensing region as compact as 14 mm with two conducting microstrip lines on both ends of the sensor. The sensor design has been realized on a thin flexible substrate of 0.15 mm thickness. The proposed sensor has been designed to measure glucose concentration through the measurement of a resonant frequency shift at 650 MHz, 4.45 GHz, and 10.35 GHz. Overall, the glucose concentration has been found to be correlated positively and linearly with the resonant frequency shift at these frequencies.

A microwave sensing system combination of interdigital structure (IDS)-based microstrip line and RF circuits for extracting complex permittivity of liquid samples

In this manuscript, a microwave sensing system for extracting complex permittivity of binary aqueous solutions based on a modified microstrip line with interdigital structure (IDS) etched is proposed. The passive resonance unit and active circuits constitute the microwave sensing system. The passive resonance unit is evolved from conventional microstrip line with the characteristic impedance of 50Ω, then the IDS is etched on the top surface of passive resonance unit to enhance the confinement of electrical field. The modified passive sensor can produce two resonant modes, i.e., odd-mode 1 and odd-mode 2, the electrical fields of these two modes are both concentrating at IDS, and odd-mode 1 with higher density of electrical field is adopted to retrieve complex permittivity of liquid samples. Except for the passive sensor, the sensing system consists of power divider, low noise amplifier (LNA), isolator, orthogonal hybrid coupler, phase shifter, mixer, and low-pass filter (LPF). The proposed microwave sensing system has two output DC voltages, i.e., channel-I and channel-Q. The mathematical relationship between complex permittivity and resonant frequency shift can be transformed into the relation between complex permittivity and two DC voltages by the proposed microwave sensing system, which is beneficial to discard the use of VNA. The two output voltages of microwave sensing system will be changed according to the injections of liquid samples with different complex permittivity, then the mathematical models can be established by summarizing the rule between complex permittivity and DC voltages. Finally, the established mathematical models can be adopted to predict the binary aqueous solutions with unknown complex permittivity. In measurement, the proposed microwave sensing system has the average sensitivities of about 2.478 mV/εr′ and 1.418 mV/εr′ for channel-I and channel-Q, respectively, which are several times higher than other reported ones. Low-cost, high sensitivity, convenient measurement, and easy fabrication are the merits for the proposed sensing system, and it is a good template in the region of detecting liquid samples.

Real-Time Characterization of Phosphate Powder With Microwave Sensor: Investigating the Impact of Temperature

Real-Time Characterization of Phosphate Powder With Microwave Sensor: Investigating the Impact of Temperature

Hairpin Resonator-Based Microwave Sensor for Detection of Nitrogenous Fertilizers in Soil and Water

Hairpin Resonator-Based Microwave Sensor for Detection of Nitrogenous Fertilizers in Soil and Water

Tissue Mimicking Materials for Shell-Based Phantoms in Breast Microwave Sensing

Tissue Mimicking Materials for Shell-Based Phantoms in Breast Microwave Sensing

A global analysis of ice phenology for 3702 lakes and 1028 reservoirs across the Northern Hemisphere using Sentinel-2 imagery

As existing global lake ice studies have predominantly focused on medium to large lakes, and reservoir ice studies have been limited to regional scales, very few studies of ice phenology have combined both lakes and reservoirs of different sizes. This study aims to characterize the freeze-up and break-up dates of 3702 lakes and 1028 reservoirs from 1 to 31,000 km2 across the Northern Hemisphere, and to analyze spatial patterns and relationships between ice phenological dates and driving factors. The freeze-up and break-up dates of these water bodies were retrieved from Sentinel-2 imagery using an ice detection algorithm through the Google Earth Engine platform from 2019 to 2023. The algorithm was verified by comparing phenology dates with an independent database based on observations from passive microwave sensors, with a mean absolute error of 18 days for both freeze-up and break-up dates. This newly established ice phenology database along with various geographic, morphometric, and climatic characteristics of the water bodies, was used to develop a random forest model for predicting ice phenology dates. While the predictive model performance is at a fair level (mean absolute error of 12 days for both freeze-up and break-up), challenges were encountered in certain high-elevation areas where cloudy conditions as well as black ice resulted in delayed freeze-up dates. Among the variables included in the random forest model, latitude and accumulation of freezing degree days were identified as the main drivers of ice phenology dates. Despite the challenges of applying a single, straightforward method on a global scale, this study has allowed the creation of a vast and comprehensive database of lake and reservoir freeze-up and break-up dates that can be used by the community to further analyze ice patterns.