Innovative Design of a Microwave Sensor for Non-Invasive Monitoring of Blood Glucose Level with High Sensitivity Using Electromagnetic Properties

Innovative Design of a Microwave Sensor for Non-Invasive Monitoring of Blood Glucose Level with High Sensitivity Using Electromagnetic Properties | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Innovative Design of a Microwave Sensor for Non-Invasive Monitoring of Blood Glucose Level with High Sensitivity Using Electromagnetic Properties Alireza Jamili, Majid Tayarani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7494729/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract This paper introduces a novel, portable microwave sensor for rapid, non-invasive blood glucose monitoring. The design features an octagonal array of complementary slotted ring resonators (CSRRs) on a dielectric substrate, operating safely in the industrial, scientific, and medical (ISM) frequency band. Its key innovation, an engineered 180 ∘ phase difference between adjacent unit cells, generates a highly concentrated electromagnetic (EM) field at the sample interface. This focused interaction significantly enhances measurement sensitivity and overall detection capability. The sensor accurately detects glucose concentrations across the 50–500 mg/dL clinical range, demonstrating a remarkable sensitivity of 2.3 MHz/(mg/dL) in laboratory settings and 1.78 MHz/(mg/dL) in realistic scenarios, surpassing existing microwave sensors. This superior performance is attributed to the CSRR architecture, which maximizes the sample's EM field interaction, enabling the precise quantification of subtle dielectric changes corresponding to varying glucose levels. Laboratory verification using a vector network analyzer (VNA) confirmed significant frequency shifts with glucose samples from 80 to 340 mg/dL. Beyond its high sensitivity, the sensor’s compact size, simple fabrication, affordability, and non-ionizing operation establish it as a promising candidate for developing practical, real-time, non-invasive glucose monitoring systems to advance diabetes management. Physical sciences/Engineering Physical sciences/Physics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Diabetes is a chronic disorder in which the body cannot correctly produce or use insulin to regulate blood glucose levels 1 . Maintaining blood glucose within the normal range (70-130 mg/dL before meals and under 180 mg/dL after meals) is crucial, as failing to do so can lead to hyperglycemia or hypoglycemia, resulting in severe complications, including cardiovascular disease, nerve damage, and death 2 . Consequently, frequent glucose monitoring is vital for management. Traditional finger-prick testing is often painful and inconvenient. An alternative, Continuous Glucose Monitoring (CGM), uses a sensor inserted under the skin to measure glucose in the interstitial fluid. However, CGM systems are often limited by high costs, discomfort, low stability, and inaccuracies arising from the difference between interstitial fluid and blood glucose levels 3-6 . These challenges create a strong demand for non-invasive monitoring techniques to improve patient comfort and adherence. The urgency is emphasized by data from the International Diabetes Federation (IDF), which shows that 589 million adults (1 in 9) have diabetes as of 2025, with over 40% being undiagnosed. The IDF projects this number will rise to 853 million by 2050. This growing prevalence, combined with the flaws of current methods, underscores the critical need for accurate, non-invasive blood glucose sensors 7 . Non-Invasive Blood Glucose Monitoring: Recent Advances and Challenges Significant research has been conducted to develop innovative non-invasive (NI) glucose detection systems, aiming to provide more comfortable and continuous monitoring. Most of these systems are based on a variety of optical, spectroscopic, and electromagnetic methods, including near-infrared (NIR) technologies like IoT-enabled R-NIR sensors 8 , mbNIR sensors paired with neural networks 9 , photoplethysmography (PPG) 10 , custom diode arrays 11 , and commercial NIRS systems 12 . Other approaches utilize mid-infrared (MIR) spectroscopy through passive imaging 13 , tuneable quantum cascade lasers (QCLs) 14,15 , and IR sensors with fuzzy logic 16 . Foundational work has also been conducted to create high-power MIR frequency combs for spectroscopy 17 . Further techniques include photoacoustic multispectral systems 18 and microscopic photoacoustic spectroscopy 19 , Raman spectroscopy with individualized calibration models 20 , polarization-based sensing with machine learning 21 , and low-power polarization-switching 22 . Additionally, AI-driven terahertz (THz) metasurface biosensors 23 , and multisensor systems measuring impedance and temperature 24 . Additionally, readily accessible biological fluids, such as tears 25 , saliva 26-29 , sweat 30-34 , urine 35,36 , and respiratory moisture, have been employed in numerous enzyme-based electrochemical techniques to establish a correlation between their glucose content and glycemic levels. However, these methods often exhibit a delayed correlation with actual blood glucose variations and are susceptible to metabolic fluctuations. Radio frequency (RF)/microwave techniques are a promising avenue for non-invasive glucose monitoring. By measuring how glucose affects the dielectric properties of blood, these methods circumvent issues that plague optical sensors, such as interference from skin pigmentation or temperature fluctuations. RF waves also penetrate tissue more deeply than light, allowing for more reliable and real-time measurements. Among these technologies, resonant-based sensors are highly effective. They operate by detecting shifts in resonance characteristics (like frequency, amplitude, or quality factor) caused by changes in a sample's permittivity. A significant advantage of this approach is its potential for simple, low-cost physical structures that can be miniaturized for wearable devices 37,38 . However, the primary challenge remains achieving sufficient sensitivity to detect the minimal variations in blood glucose found in the human body 39 . For a sensor to be viable, it must be validated under realistic conditions. Testing in simple water-based solutions is insufficient, as it doesn't replicate the complex, "lossy" nature of biological fluids. Therefore, performance must be evaluated with tissue-mimicking phantoms 40 and, most critically, with actual human blood samples to ensure dependable results 41,42 . Practical factors, such as operating within the physiological glucose range and considering the impact of sample volume and sensor placement are also crucial for demonstrating real-world performance 43 . Overcoming these challenges necessitates sophisticated engineering based on the principles of electromagnetic wave interaction with biological tissues 44 . A cornerstone of this approach is the strategic use of specialized resonant structures, notably Split-Ring Resonators (SRR) and Complementary Split-Ring Resonators (CSRR). These meticulously engineered structures exhibit a sharp resonant response and can confine a highly intense electric field within a tiny region. By placing the sample in this "hotspot," the wave-sample interaction is maximized, producing a larger and more easily detectable resonance shift even for tiny changes in glucose, thereby dramatically boosting sensitivity. Successful applications of this principle are evident in high-sensitivity chipless sensors for wearables and advanced CSRR structures, which achieve exceptional performance through impedance matching 45 . The next generation of these sensors integrates this core principle with other technologies. This includes using active electronic components to compensate for signal loss in blood 46 and applying advanced data processing, such as Convolutional Neural Networks (CNNs), to interpret complex sensor data accurately 41 . The ultimate goal is to develop a highly sensitive and reliable sensor platform that is structurally simple, cost-effective, and suitable for integration into wearable devices. We present a novel microwave sensor designed for the non-invasive measurement of blood glucose levels. As depicted in Fig. 1a, the sensor comprises a microstrip structure with two unit cells in a specific octagonal pattern, which are formed by etching complementary slit ring resonators (CSRRs) onto a copper ground plate. The cells are excited by two feeding lines engineered to create a 180-degree phase difference between them. This innovative approach, which combines octagonal geometry with a 180-degree phase difference, significantly enhances the sensitivity of traditional CSRR structures by concentrating the resonant electric field in the target area. The corresponding electric field distribution at the resonance frequency of 5.5 GHz (Fig.1b) confirms a highly concentrated and intense field within the CSRR elements. This focused interaction enhances sensitivity, enabling the detection of subtle changes in dielectric properties associated with different glucose concentrations. The sensor's ability to detect these changes is demonstrated by tracking changes in its transmission frequency characteristics. The proposed sensor exhibits excellent glucose sensitivity and a frequency resolution of approximately 2.3 MHz/(mg/dL) under laboratory conditions, which involve testing a blood-mimicking solution in a glass container as shown in Fig.1a. To assess its practical viability, the sensor's performance was also investigated under more realistic conditions that account for the complexities of tissue structure and the effect of a sensor enclosure. Evaluating performance under such conditions provides greater confidence for its potential clinical use and commercialization. The high-resolution frequency shifts provided by the sensor increase its robustness against noise and measurement uncertainties. Furthermore, its design ensures compatibility with wide dynamic range readout circuits, which facilitates simpler, more convenient, and accurate readings. The designed sensor is capable of detecting glucose concentrations across the physiological range of 50 to 500 mg/dL with high resolution, a level of performance sufficient for accurate real-time glucose monitoring. The integrated octagonal cell sensor offers several advantages for assessing glycemic levels, including portability, non-invasiveness, low cost, compact dimensions, and ease of fabrication. This research represents a significant milestone toward developing a sensor for intermittent glucose monitoring, specifically designed for strategic placement on the wrist. The current design can be readily adapted for wearable applications by fabricating the CSRR sensing elements on a flexible substrate and integrating the device with a suitable electronic reader. Table 1 . Parameter values Parameter Value(mm) Parameter Value(mm) Parameter Value(mm) a 5 D 5.2 L 1 7.45 b 4.12 X 30 L 2 2.6 c 0.2 Y 30 L 3 11.1 d 0.2 h 0.8 L 4 9.1 g 0.2 t 1.1 L 5 26 Results and discussion This section provides a detailed description of the proposed microwave glucose sensor, covering its design, key parameters, numerical simulations, and experimental validation. The sensor is designed to operate at approximately 5.5 GHz within the ISM band, a frequency chosen to ensure that electromagnetic waves penetrate sufficiently to reach the tissue. This choice optimizes penetration depth while minimizing tissue losses, thereby enhancing the sensor's sensitivity to subtle changes in blood's dielectric properties associated with varying glucose concentrations 47 . These variations in the dielectric constant and loss tangent were studied from 1 to 7 GHz using the first-order Debye relaxation model, which shows a slight increase in dielectric constant and a corresponding decrease in loss tangent as glucose concentration rises 48 . As depicted in Fig. 1 c, the electric field lines at the resonant frequency of 5.5 GHz form a closed loop between two unit cells. This configuration, where the lines originate from one unit cell, traverse the glass and blood, and subsequently return to another unit cell, creates a highly concentrated electric field and a strong coupling between the unit cells. Consequently, the microstrip structure's frequency response reaches its maximum potential effectiveness. The sensor's physical architecture features two identical octagonal Complementary Split-Ring Resonators (CSRRs). As illustrated in Fig. 1 a, these resonators are etched onto the 35 µm thick copper ground plane of a Rogers 4003 PCB, which has a dielectric permittivity (ɛ r′ ) of 3.55 and a loss tangent (tanδ) of 0.0027. For practical application and test repeatability, the sensor is housed within an aluminum enclosure. Excitation of the CSRRs is achieved through efficient coupling to a microstrip transmission line (MTL), which was optimized with a width of 1.1 mm, a thickness to 0.035 mm, and an impedance of 50 Ω for optimal power transmission. Two distinct topologies of this design were implemented and optimized: the first for blood samples contained in glass vessels (Fig. 1 a) and the second for a simplified wrist model (Fig. 5 a). In this configuration, the two octagonal cells are positioned vertically along the MTL axis, separated by a distance of D = 5.2 mm. Each CSRR unit cell comprises two concentric octagonal rings with precisely defined geometric parameters: an outer ring diagonal length (a) of 5 mm, an inner ring length (b) of 4.12 mm, a coupling-split (d) of 0.2 mm, a dielectric split (c) of 0.2 mm, and a metal split-gap (g) of 0.2 mm, with the splits for each ring on opposite diagonal sides. A comprehensive schematic of the sensor configuration, detailing all geometric parameters, is shown in Fig. 1 b, with specific values provided in Table 1 . To significantly boost sensor sensitivity, a novel approach incorporating a 180° phase shift between the two unit cells was introduced. This phase difference creates the closed-loop electric field configuration, effectively trapping electromagnetic energy within the sensor's immediate vicinity. As a result, the dielectric properties of loaded samples, such as glucose-laden blood tissue, can be measured with high accuracy. The design and geometric parameters of the planar transmission line and the etched unit cells were therefore meticulously optimized to achieve a sharp transmission resonance around f₀ = 5.5 GHz when the sensor is loaded with a sample. Glucose detection mechanism To simulate the frequency behavior of the integrated CSRR sensor, a lumped-element model was developed, as illustrated in Fig. 2 49,50 . In this model, each of the two identical octagonal cell resonators is represented by a parallel RLC resonant circuit (L R1 , C R1 , R R1 and L R2 , C R2 , R R2 ). The inductance (L R ) arises from the structure's dielectric rings, the capacitance (C R ) is formed by the metal splits and spacers, and the resistance (R R ) accounts for conductive and dielectric losses. A coupling inductance L c1 models the microstrip transmission line (MTL) that excites the resonators. In contrast, the coupling between the MTL and the CSRR structure is represented by a shunt capacitor, C c1 , in parallel with a resistor, R c1 , to account for substrate and conduction losses. A key aspect of the model is the method for creating the 180° phase shift: while the first cell is excited via L c1 , the second cell is coupled to the feed line through an additional, distinct inductor (L c2 ), which effectively models the two different excitation paths. Finally, when a sample is loaded onto the sensor, its dielectric properties are incorporated by adding a parallel RC circuit (C M , R M ) coupled to each resonator. The capacitance (C M1 , C M2 ) is directly related to the sample's relative permittivity, and the resistance (R M1 , R M2 ) corresponds to its loss characteristics. Due to the symmetrical design, the values of these sample-related components are identical for both cells. Changes in the dielectric permittivity of the blood samples affect the electric field distribution, which can be observed in the resonance frequency f R through changes in the effective capacitor C e CSRR (C e =C m ||C R ). Therefore, changes in resonance frequency can be used to determine the glucose concentration of the sample. The resistor R e (R e =R m ||R R ), which represents the combined resistance of R c and the CSRR-part R R , is mainly affected by the loss characteristics of the blood sample. Changes in tanδ are reflected as changes in the amplitude of the resonance profile. These changes in resonance properties are a signature of the dielectric properties of the blood sample, which can be related to the glucose level through analysis of the modified resonance behavior. The arrangement of the lumped elements stores oscillating electric and magnetic energy in the inductance and capacitance. These are caused by the induced charges and currents within the patterned dielectric loops or slots when the CSRRs are excited. When the electric and magnetic energies are balanced, the microwave sensor resonates at a specific frequency, as shown in Eq. ( 1 ). This resonance is directly seen as the lowest point in the transmission coefficient S 21 . $$\:{f}_{R}\left({\epsilon\:}_{r}^{{\prime\:}}\right)=\frac{1}{2\pi\:\sqrt{{L}_{R}\left({C}_{e}\left({\epsilon\:}_{r}^{{\prime\:}}\right)+{C}_{c}\right)}}$$ 1 To evaluate the performance and resonance frequencies of the proposed sensor under loaded conditions, numerical simulations were conducted using CST Microwave Studio. The simulation modeled a cylindrical glass container, designed to hold 0.5 mL of blood samples on the CSRR surface, as illustrated in Fig. 1 a. The container had an outer diameter of 11 mm, an inner diameter of 9 mm, a wall thickness of 1 mm, and a height of 25 mm. The unipolar Debye model (first order), presented by Eq. ( 2 ), was used to create numerical models for the dielectric properties of dispersed glucose-blood samples at different concentrations. This model was developed in 48 based on spectroscopic measurements of 50, 250, 1000, and 2000 mg/dL aqueous solutions collected using a commercial coaxial probe kit connected to a VNA. This model is the most reasonable approximation for the behavior of blood glucose. $$\:{\epsilon\:}_{r}\left({w}_{9}\xi\:\right)={\epsilon\:}_{\infty\:}\left(\xi\:\right)+\left(\frac{{\epsilon\:}_{stat}\left(\xi\:\right)-{\epsilon\:}_{\infty\:}\left(\xi\:\right)}{1+jw\tau\:\left(\xi\:\right)}\right)+\frac{{\sigma\:}_{s}}{jw{\epsilon\:}_{0}}$$ 2 Equation ( 2 ) defines the complex permittivity of the blood solution of glucose concentration \(\:\xi\:\) (in mg/dL) at the angular frequency \(\:w\) . The parameters \(\:{\epsilon\:}_{stat}\) , \(\:{\epsilon\:}_{\infty\:}\) , and τ are concentration-dependent Debye coefficients 51 , \(\:{\sigma\:}_{s}\) is static conductivity, and \(\:{\epsilon\:}_{0}\) is the permittivity of free space. Blood samples with glucose concentrations ranging from S 1 -S 14 (50–500 mg/dL) were simulated above the sensing area within the glass container. This concentration range encompasses a broad spectrum of diabetic conditions, including hypoglycemia ( 130 mg/dL). As can be observed in Fig. 3 , five distinct resonances were identified in the transmission scattering parameters, centered approximately at f 1 = 2.05 GHz, f 2 = 3.4 GHz, f 3 = 4.15 GHz, f 4 = 5.5 GHz, and f 5 = 6.7 GHz. Resonance frequency shifts were simulated for selected concentrations (Table 2 ), and the resulting transmission frequency response changes are depicted in Figs. 3 b and c. The most pronounced resonance frequency changes were observed at the fourth resonance (approximately 5.5 GHz), prompting a more detailed analysis of this frequency range. In addition, the resonances exhibited damping characteristics, characterized by a significant decrease in the amplitude of the resonance peaks. This attenuation is attributed to the absorptive properties of the blood sample. To highlight the linear correlation between glucose concentration and the frequencies of the second to fifth resonances, Fig. 4a and 4b present linear regression models for each blood glucose range (the first resonance shows almost no change). These results demonstrate a strong linear relationship, suggesting that the sensor can be calibrated for individual patients to accurately measure blood glucose levels continuously. Table 2 Parameter values Parameter S 1 S 2 S 3 S 4 S 5 S 6 S 7 Value (mg/dL) 50 60 70 80 90 100 110 Parameter S 8 S 9 S 10 S 11 S 12 S 13 S 14 Value (mg/dL) 120 130 140 200 300 400 500 To simulate a more realistic scenario, a second analysis was performed using a simplified wrist model placed in the sensing area, as shown in Fig. 5 a. This model consisted of a = 1 mm thick skin layer, a = 1 mm thick fat layer, and a = 1 mm radius cylinder filled with blood. The resulting electric field lines at the resonant frequency of 5.2 GHz (Fig. 5 b) show a diminished interaction with the blood compared to the previous lab-based scenario. This reduction in intensity is an expected consequence of the high-permittivity skin (ɛ r =40) and fat (ɛ r =10) layers, which absorb and disperse the field. Nevertheless, a significant coupling between the two unit cells persists, ensuring that the electric field intensity interacting with the blood is maximized under these more challenging, realistic conditions. The sensitivity of many prior sensors has been characterized in vitro using phantoms, where performance relies on the stark dielectric contrast between the blood sample and the surrounding air. This high contrast naturally concentrates the electric field within the sample, yielding a strong response. However, this mechanism is less effective for realistic in-vivo applications. The proximity of skin and fat tissue, which have high dielectric constants, disperses the electric field and diminishes its intensity in the target blood vessels, rendering the simple contrast-based approach unreliable. An effective in-vivo sensor must therefore achieve field confinement through deliberate design, creating the focus of the electric field that is robust to such environmental loading effects. Our work introduces a novel approach that accomplishes this by leveraging a 180-degree phase differential between two unit cells. This configuration induces strong electromagnetic coupling, which becomes the primary mechanism for focusing the electric field into the target region. Consequently, the sensor's high sensitivity is an engineered feature arising from this internal coupling, rather than a passive reliance on the dielectric properties of its surroundings. This methodology represents a significant and necessary departure from previous designs. To investigate the influence of skin thickness on sensor sensitivity, simulations were conducted using a wrist model with varying skin thicknesses of 0.5 mm, 1 mm, and 1.5 mm. The corresponding transmission responses were analyzed. While skin thickness varies due to factors like body position, gender, skin type, age, race, and geographic location, the chosen thicknesses represent a practical range for the human wrist. As depicted in Fig. 6 a, three distinct resonances were observed in the scattering responses, centered at specific frequencies: f 1 = 3.1 GHz, f 2 = 5.2 GHz, and f 3 = 6.3 GHz. As illustrated for the 1 mm case in Figs. 6 b and 6 c, shifts in glucose concentration correlate directly with shifts in the resonant frequency. Notably, as depicted in Figs. 6 b and 6 c, the second resonance in each response provided valuable information about the glucose concentration of the simulated samples beneath the skin and fat layers. However, the analysis confirmed that signal attenuation is directly proportional to skin thickness. This phenomenon weakens the electric field's interaction with the glucose sample, leading to a decline in sensor sensitivity. The average sensitivity was measured to be 1.85 MHz/(mg/dL) for 0.5 mm skin, 1.78 MHz/(mg/dL) for 1 mm skin, and 1.6 MHz/(mg/dL) for 1.5 mm skin. Crucially, while this decline in sensitivity is relatively small, it highlights a significant source of inter-individual variability. Such variations, if unaddressed, could lead to inaccurate measurements between different users. Therefore, to ensure clinical accuracy in practical applications, a subject-specific calibration protocol is essential. Figures 7a and 7b present linear regression models for each blood glucose range. In-vitro experiments. To validate the proposed glucose sensor, rigorous laboratory testing was conducted using a Vector Network Analyzer (VNA). As illustrated in Fig. 8 , two identical prototypes were fabricated to assess manufacturing tolerances and record sensor transmission responses for various glucose samples. Real blood samples from three individuals aged 24, 27, and 32 were used to mimic the behavior of blood at various glucose concentrations within the clinically relevant range for type 2 diabetes (80–320 mg/dL). The blood samples were divided into multiple identical containers, each containing 0.5 ml of blood, and different concentrations of blood glucose were obtained by adding varying amounts of dextrose powder. Repeating these tests with three distinct blood samples from individuals of different ages and blood characteristics helped to ensure the reproducibility of the measured scattering data. While many previous studies have employed glucose-aqueous solutions for initial experiments in non-invasive glucose detection using RF sensors, this study utilized more realistic sample conditions by using actual blood samples. The choice of real blood samples was motivated by the understanding that different blood concentrations, blood groups, and other individual blood characteristics can influence measurement results. Two of the individuals had blood group O+, while one had blood group B+. Figure 9 presents the measured transmission coefficients (S 21 ) for the fabricated sensors in both unloaded and loaded states within the 1–6 GHz frequency range. The measured resonant frequencies for both prototypes closely matched the values predicted by simulations. The two sensors exhibited nearly identical performance, with only minor variations in resonance depth and frequency, which are attributed to normal manufacturing tolerances. For loaded measurements, cylindrical glass containers were used to hold the prepared blood samples on the sensor's surface, as shown in Fig. 8 b. Placing the empty container on the sensor introduced a baseline frequency shift of a few megahertz from the unloaded resonance frequency. In each experiment, a micropipette was used to dispense a precise 0.5 mL volume of each sample to minimize errors arising from volume uncertainty, and the corresponding shifts in the transmission resonance were recorded. The actual glucose concentration of each prepared sample was independently verified using a commercial blood glucose level (BGL) monitoring device. The experimental transmission response of the sensor was measured across a range of glucose concentrations (80–340 mg/dL), as shown in Fig. 9 . These results revealed two key phenomena: a consistent downward shift in resonant frequency with increasing glucose concentration, and detectable changes in resonance amplitude due to slight variations in the sample's loss tangent. As predicted by simulations, the fourth resonance (f 4 ) exhibited the highest sensitivity to these changes. A linear regression model of the frequency response, illustrated in Fig. 10, was used to quantify this performance. The analysis yielded an experimental sensitivity of approximately 2.3 MHz/(mg/dL) at f 4 . This result confirms the value predicted in our simulations and validates the sensor's high-frequency resolution for detecting small changes in dielectric properties. To ensure accuracy, all glucose measurements were repeated three times, and the average values were reported. This averaging process helped eliminate possible random noise from the power supply (i.e., VNA) or other unrelated sources, thereby improving the signal-to-noise ratio (SNR). Considering that the sensor frequency reading for 10 mg/dL is approximately 23 MHz (with an average sensitivity of 2.3 MHz/(mg/dL)), the proposed measurement platform can reliably identify glucose concentrations as low as 1 mg/dL. Since the scattering response of the sensor is strongly dependent on the electromagnetic properties of the sample, which are temperature-dependent, all prepared glucose samples were kept in a temperature-controlled room of 25 ± 1°C. Therefore, small changes in ambient temperature have a negligible effect on the resonance measurement of the CSRR sensor. This is because most of the electromagnetic energy is concentrated in the octagonal cell area and the near field region, which interacts with the sample tissue. Additionally, the high concentration of the electric field at the sample location makes the sensor more sensitive to changes in the sample. A comprehensive comparison of the sensitivity performance of the proposed sensor with other recent microwave sensors is presented in Table 3 . This comparison ranks advanced glucose sensors based on their sensitivity to relevant parameters. Sensitivity is defined as the change in frequency (Δf R ) per unit change in glucose concentration (1 mg/dL) for a given volume and specific test setup. As a result, the sensitivity obtained in previous works based on different microwave sensing mechanisms was significantly lower than the minimum resolution adopted by the proposed sensor of this research. Additionally, most of the previously proposed sensors have not been investigated under the more complex and realistic conditions considered in this study. Therefore, the sensitivity obtained for the more realistic conditions investigated in this study is another distinguishing feature of this research. The sensitivity of the proposed sensor surpasses that of other techniques, which rely on tracking slight changes in the S11 and S21 resonance magnitudes, requiring high-precision measuring instruments. The improved design of the CSRR elements in this work enhances the interaction between the sample and the sensor in the sensor region, allowing the resonant frequency response of the sensor to be defined mainly by the passage of the sample under test (SUT). The sensitivity achieved in this study, at 2.3 MHz/[mg/dL] under standard conditions and 1.78 MHz/[mg/dL] under more practical circumstances, surpasses the highest reported values to date, as determined by our investigations. Moreover, the sample volume utilized in this research aligns with realistic conditions and the volume of blood in the wrist that engages with the sensor. The sensor's performance under these conditions demonstrates enhanced reliability compared to conventional counterparts, suggesting its suitability for real-world applications. The proposed sensor can be effectively used to detect the normal blood glucose range as well as cases of hypoglycemia and hyperglycemia. Table 3 Comparison of different glucose sensors. Ref Sensing Technique Operation Frequency (GHz) Test solution Concentration (mg/dL) Sensing parameter S (MHz/[mg/dL]) 52 split ring resonator 4.18 Aqueous solution 0-5000 f R (S 21 ) 0.0026 53 Band-stop filter based on SIW cavity 5-5.5 The fingertip 100–500 f R (S 21 ) 0.24 54 three-loop microstrip patch antenna 3 Aqueous solution 50–500 f R (S 11 ) 0.25 55 meta-structured antenna 4 Aqueous solution 50–250 f R (S 11 ) 0.352 56 Two-port Rectangular Dielectric Resonator (RDR) 2.47 The fingertip 90–403 f R (S 21 ) 0.39 57 Omega-coupled split-ring resonator 1.41 The fingertip 0-200 f R (S 11 ) 0.56 58 closed-loop split ring resonator 2–5 On the forearm 80–155 f R (S 11 ) 0.82 59 patch antenna 5.7 Aqueous solution - f R (S 11 ) 0.089 60 Open-loop resonator with electric coupling 2.35 Aqueous solution 89–456 f R (S 21 ) 0.95 61 Substrate Integrated Waveguide (SIW) 5.74 Aqueous solution 10–200 f R (S 11 ) 1.218 62 Complementary split-ring resonator 2.45 Aqueous solution 40–140 f R (S 21 ) 1.25 63 Fingertip placed on a planar resonator 1.5–2.3 The fingertip 98–188 f R (S 11 ) 1.34 64 square-shaped spiral ring resonator 1–2 Aqueous solution 0.01–0.05 f R (S 11 ) 1.99 65 Dual-band bandpass filter 2.45–5.2 Aqueous solution 0-400 f R (S 11 ) 2.026 This work Octagonal-shaped complementary split ring resonator 4.3–5.1 The Wrist 80–340 f R (S 21 ) 1.78 This work Octagonal-shaped complementary split ring resonator 4.4–5.5 Blood sample 50–500 f R (S 21 ) 2.3 Conclusion This paper presents the design, simulation, and experimental validation of a novel microwave sensor for non-invasive blood glucose monitoring. By employing an innovative octagonal CSRR configuration with an engineered 180-degree phase difference between unit cells, we achieved a highly concentrated and intense electric field, maximizing its interaction with the sample under test. This advanced design principle yielded unprecedented sensitivity, which was rigorously verified through both realistic simulations and in vitro experiments using real human blood samples. The sensor demonstrated an exceptional experimental sensitivity of 2.3 MHz/(mg/dL) under laboratory conditions and a simulated sensitivity of 1.78 MHz/(mg/dL) in a more practical wrist model, values that surpass those of previously reported microwave-based sensors. The strong linear correlation observed between resonance frequency shifts and glucose concentrations, ranging from 50 to 500 mg/dL, confirms the sensor's potential for reliable and accurate glucose tracking across the full clinical range. Looking to the future, the remarkable sensitivity achieved by this sensor generates a wealth of high-resolution data that necessitates sophisticated analysis. While this research establishes a robust proof of concept, the next critical step involves addressing the processing of these specific and complex results. We suggest that future work should focus on leveraging the power of machine learning and artificial intelligence. An AI-driven approach could analyze and compensate for numerous individual and environmental parameters, such as skin thickness, body temperature, motion artifacts, sweat composition, and unique tissue dielectric properties. This would unlock powerful operational applications, including integration into closed-loop systems for automated insulin injection, predictive alerts for hypoglycemia and hyperglycemia, and real-time dietary feedback based on glucose response. Such an intelligent system would represent a significant leap towards fully automated and personalized diabetes management, transforming patient care. Declarations Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Availability: The data supporting the conclusions of this research can be obtained from M.T. and A.J., but there are limitations to accessing this data. The data used in this study were obtained under a license and are not publicly accessible. However, the authors can provide the data upon a reasonable request, provided permission is obtained from M.T. Ethical Considerations: All experimental protocols were approved by the Research Ethics Committee of Iran University of Science and Technology and were conducted in accordance with relevant guidelines and regulations. Informed consent was obtained from all subjects. 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A Novel Textile-Embedded Wearable Microwave Sensor for Non-Invasive Sweat Glucose Monitoring. IEEE Transactions on Instrumentation and Measurement (2025). Lee, I., Probst, D., Klonoff, D. & Sode, K. Continuous glucose monitoring systems-Current status and future perspectives of the flagship technologies in biosensor research. Biosensors and Bioelectronics 181 , 113054 (2021). Hamed, M. M., Mohammed, N. A. & Badawi, K. A. A Compact 2-D photonic crystal biomedical sensor for enhanced glucose concentration detection in urine. Scientific Reports 15 , 4905 (2025). Baghelani, M., Abbasi, Z., Daneshmand, M. & Light, P. E. Non-invasive continuous-time glucose monitoring system using a chipless printable sensor based on split ring microwave resonators. Scientific Reports 10 , 12980 (2020). Yang, P.-H. et al. A Miniaturized MgO Multisensor Device Based on A Flexible Printed Circuit Board for Glucose and pH Detection. IEEE Transactions on NanoBioscience (2025). Jang, C., Lee, H.-J. & Yook, J.-G. Radio-frequency biosensors for real-time and continuous glucose detection. Sensors 21 , 1843 (2021). Zaarour, Y. et al. Microwave Antenna Sensing for Glucose Monitoring in a Vein Model Mimicking Human Physiology. Biosensors 15 , 282 (2025). Sattari, M. A. & Hayati, M. Detection of fasting blood sugar using a microwave sensor and convolutional neural network. Scientific Reports 15 , 22937 (2025). Malena, L. et al. Feasibility of glucose concentration estimation in whole blood samples using non-invasive metamaterial microwave sensor. IEEE Sensors Journal (2025). Patel, S., Singh, A., Mitra, D. & Koley, C. Metamaterial-enabled microwave sensor for non-invasive continuous glucose monitoring. Results in Engineering , 105782 (2025). Nitas, M., Kafesaki, M. & Arslanagić, S. Investigation of the Electromagnetic Behavior of Complementary Split-Ring Resonators: Toward a Novel CSRR Design. IEEE Transactions on Microwave Theory and Techniques (2024). Martinic, M. et al. Highly Sensitive Impedance-Matched Microwave Dielectric Sensor for Glucose Concentration Measurements. IEEE Sensors Journal (2025). Abdolrazzaghi, M., Katchinskiy, N., Elezzabi, A. Y., Light, P. E. & Daneshmand, M. Non-invasive glucose sensing in aqueous solutions using an active split-ring resonator. IEEE Sensors Journal 21 , 18742–18755 (2021). Turgul, V. & Kale, I. Permittivity extraction of glucose solutions through artificial neural networks and non-invasive microwave glucose sensing. Sensors and Actuators A: Physical 277 , 65–72 (2018). Hofmann, M., Fischer, G., Weigel, R. & Kissinger, D. Microwave-based non-invasive concentration measurements for biomedical applications. IEEE Transactions on Microwave Theory and Techniques 61 , 2195–2204 (2013). Baena, J. D. et al. Equivalent-circuit models for split-ring resonators and complementary split-ring resonators coupled to planar transmission lines. IEEE transactions on microwave theory and techniques 53 , 1451–1461 (2005). Ebrahimi, A., Withayachumnankul, W., Al-Sarawi, S. F. & Abbott, D. Dual-mode behavior of the complementary electric-LC resonators loaded on transmission line: Analysis and applications. Journal of Applied Physics 116 (2014). Yilmaz, T., Foster, R. & Hao, Y. Radio-frequency and microwave techniques for non-invasive measurement of blood glucose levels. Diagnostics 9 , 6 (2019). Govind, G. & Akhtar, M. J. Metamaterial-inspired microwave microfluidic sensor for glucose monitoring in aqueous solutions. IEEE Sensors Journal 19 , 11900–11907 (2019). Kiani, S., Rezaei, P., Karami, M. & Sadeghzadeh, R. A. Band‐stop filter sensor based on SIW cavity for the non‐invasive measuring of blood glucose. IET Wireless Sensor Systems 9 , 1–5 (2019). Zhang, M. et al. Microfluidic microwave biosensor based on biomimetic materials for the quantitative detection of glucose. Scientific Reports 12 , 15961 (2022). Jeong, J.-M., Bien, F. & Lee, J.-G. Design of Orientation-Independent Non-Invasive Glucose Sensor Based on Meta-Structured Antenna. Electronics 14 , 2295 (2025). Marzouk, H. M., Abd El-Hameed, A. S., Allam, A., Pokharel, R. K. & Abdel-Rahman, A. B. Comprehensive system for non-invasive glucose monitoring utilizing a rectangular dielectric resonator microwave sensor. IEEE Transactions on Instrumentation and Measurement (2025). Harnsoongnoen, S., Srisai, S., Kongkeaw, P. & Buranrat, B. Detection and classification of glucose solution concentration and blood sugar levels at the fingertip using a novel planar microwave sensor and deep learning techniques. Sensors and Actuators B: Chemical 430 , 137322 (2025). Kandwal, A. et al. Highly sensitive closed loop enclosed split ring biosensor with high field confinement for aqueous and blood-glucose measurements. Scientific reports 10 , 4081 (2020). Singh, T., Mishra, P. K., Pal, A. & Tripathi, V. S. A planar microwave sensor for non-invasive detection of glucose concentration using regression analysis. International Journal of Microwave and Wireless Technologies 15 , 1343–1353 (2023). Mohammadi, P., Mohammadi, A. & Kara, A. Dual Frequency Microwave Resonator for Non-invasive detection of Aqueous Glucose. IEEE Sensors Journal (2023). Hamid Allah, A., Ayissi Eyebe, G. & Domingue, F. Improved fully 3D-printed SIW-based sensor for non-invasive glucose measurement. Sensors 25 , 2382 (2025). Omer, A. E. et al. Low-cost portable microwave sensor for non-invasive monitoring of blood glucose level: Novel design utilizing a four-cell CSRR hexagonal configuration. Scientific Reports 10 , 15200 (2020). Cebedio, M. C. et al. Analysis and design of a microwave coplanar sensor for non-invasive blood glucose measurements. IEEE Sensors Journal 20 , 10572–10581 (2020). Kim, N.-Y. et al. Rapid, sensitive and reusable detection of glucose by a robust radiofrequency integrated passive device biosensor chip. Scientific reports 5 , 7807 (2015). Farouk, M., El-Hameed, A. S. A., Eldamak, A. R. & Elsheakh, D. N. Non-invasive blood glucose monitoring using a dual band microwave sensor with machine learning. Scientific Reports 15 , 16271 (2025). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 13 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviews received at journal 23 Sep, 2025 Reviews received at journal 13 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers agreed at journal 09 Sep, 2025 Reviewers invited by journal 09 Sep, 2025 Editor assigned by journal 09 Sep, 2025 Editor invited by journal 09 Sep, 2025 Submission checks completed at journal 05 Sep, 2025 First submitted to journal 05 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7494729","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":514618673,"identity":"2c155c76-8f36-467c-9e32-7d72193874a7","order_by":0,"name":"Alireza Jamili","email":"","orcid":"","institution":"Iran University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Alireza","middleName":"","lastName":"Jamili","suffix":""},{"id":514618674,"identity":"ff573d12-3137-49f1-a7a9-52ae8fc72488","order_by":1,"name":"Majid 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10:38:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7494729/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7494729/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-41378-6","type":"published","date":"2026-03-03T15:58:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91648635,"identity":"07d89ba0-f9f9-4caf-b3a8-8236731b1491","added_by":"auto","created_at":"2025-09-18 16:25:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":242945,"visible":true,"origin":"","legend":"\u003cp\u003eThe proposed CSRR sensor (\u003cstrong\u003ea\u003c/strong\u003e) The assembled microwave sensor, including the standalone prototype, aluminum enclosure, SMA connectors, and a glass container filled with blood (\u003cstrong\u003eb\u003c/strong\u003e) The electric field distribution on the CSRR surface at 5.2 GHz, with a very high concentration in the location of unit cells (\u003cstrong\u003ec\u003c/strong\u003e) The electric field lines at the resonant frequency, viewed from the side in a plane perpendicular to the feed lines (\u003cstrong\u003ed\u003c/strong\u003e) The CSRR sensor configuration, shows the copper ground plate, copper feed line, octagonal unit cell topology, and geometric parameters.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7494729/v1/4c39fba554391e61573dea30.png"},{"id":91647422,"identity":"b805bad9-1a02-433f-8e8d-2f637f454ced","added_by":"auto","created_at":"2025-09-18 16:09:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38135,"visible":true,"origin":"","legend":"\u003cp\u003eEquivalent electrical model of the CSRR sensor under loading conditions\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7494729/v1/e046cbe12d265f749b705319.png"},{"id":91647780,"identity":"af70bc6a-d806-4fff-91db-d42557d23209","added_by":"auto","created_at":"2025-09-18 16:17:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eSimulated Transmission Frequency Response \u003cstrong\u003e(b)\u003c/strong\u003eSimulated Transmission Frequency Response Loaded with different glucose concentrations from 50 mg/dL to 140 mg/dL \u003cstrong\u003e(c)\u003c/strong\u003e Loaded with different glucose concentrations from 200 mg/dL to 500 mg/dL.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7494729/v1/d4d3b1b10dbfd72da006b06e.png"},{"id":91647425,"identity":"78ed793c-f7ce-414a-8768-421c509f4b41","added_by":"auto","created_at":"2025-09-18 16:09:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":93462,"visible":true,"origin":"","legend":"\u003cp\u003eLinear correlation models for the resultant resonant frequencies of (\u003cstrong\u003ea\u003c/strong\u003e) concentrations from 50 mg/dL to 140 (\u003cstrong\u003eb\u003c/strong\u003e) concentrations from 200 mg/dL to 500 mg/dL.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7494729/v1/639c6bdd991449588c0bd1c0.png"},{"id":91648636,"identity":"d07a60b1-d75c-47e3-90b7-edf93cd4adae","added_by":"auto","created_at":"2025-09-18 16:25:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":189099,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e)The structural representation of the simulated model designed to investigate more authentic conditions by incorporating layers of skin, fat, and blood (\u003cstrong\u003eb\u003c/strong\u003e) Electric field lines visualized from a Side view.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7494729/v1/faac0b5e9e4c6455c399d319.png"},{"id":91647429,"identity":"b372ac8a-82ce-4bd0-9b56-f5059f42cc9a","added_by":"auto","created_at":"2025-09-18 16:09:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":158976,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003ea\u003c/strong\u003e)Simulated Transmission Frequency Response (\u003cstrong\u003eb\u003c/strong\u003e)Simulated Transmission Frequency Response Loaded with different glucose concentrations from 50 mg/dL to 140 mg/dL (\u003cstrong\u003ec\u003c/strong\u003e) Loaded with different glucose concentrations from 200 mg/dL to 500 mg/dL\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7494729/v1/950ae58bc1bdf6e0e05cb40e.png"},{"id":91647431,"identity":"9169afda-0fed-42b5-a12e-d83f85378a11","added_by":"auto","created_at":"2025-09-18 16:09:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":25805,"visible":true,"origin":"","legend":"\u003cp\u003eLinear correlation models for the resultant resonant frequencies of (\u003cstrong\u003ea\u003c/strong\u003e) concentrations from 50 mg/dL to 140 mg/dL (\u003cstrong\u003eb\u003c/strong\u003e) concentrations from 200 mg/dL to 500 mg/dL\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7494729/v1/3a69cce4b9b015d5f3d0a812.png"},{"id":91649554,"identity":"7c03dcb5-e732-446b-ac86-add954b18f29","added_by":"auto","created_at":"2025-09-18 16:41:40","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":156265,"visible":true,"origin":"","legend":"\u003cp\u003eGlucose sensing experiments (\u003cstrong\u003ea\u003c/strong\u003e) VNA experimental setup, (\u003cstrong\u003eb\u003c/strong\u003e) sensor loaded with a sample of blood\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7494729/v1/d23373d48e31daf1ad390782.png"},{"id":91647437,"identity":"7932ae38-41b8-40cd-a1bf-5734da598f2b","added_by":"auto","created_at":"2025-09-18 16:09:40","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":101015,"visible":true,"origin":"","legend":"\u003cp\u003eTransmission frequency response of the sensor, both unloaded and loaded with glucose concentrations ranging from 80 mg/dL to 340 mg/dL.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7494729/v1/8ec6cb3fe0bb13099852dd7f.png"},{"id":91648950,"identity":"97a651fe-cd7d-4200-9b5a-5549df31c4c2","added_by":"auto","created_at":"2025-09-18 16:33:40","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":43269,"visible":true,"origin":"","legend":"\u003cp\u003eLinear correlation models for the resultant resonant frequencies of (\u003cstrong\u003ea\u003c/strong\u003e) 23 aged (\u003cstrong\u003eb\u003c/strong\u003e) 27 aged (\u003cstrong\u003ec\u003c/strong\u003e) 32 aged.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7494729/v1/5c048254ef73349e68ccdbd0.png"},{"id":104250804,"identity":"67143874-3271-4cd3-ba47-b9b6e0dbe0f5","added_by":"auto","created_at":"2026-03-09 16:08:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2093412,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7494729/v1/07f3fe71-1533-4edc-bcfe-9a17bc07d5f9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Innovative Design of a Microwave Sensor for Non-Invasive Monitoring of Blood Glucose Level with High Sensitivity Using Electromagnetic Properties","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes is a chronic disorder in which the body cannot correctly produce or use insulin to regulate blood glucose levels\u003csup\u003e1\u003c/sup\u003e. Maintaining blood glucose within the normal range (70-130 mg/dL before meals and under 180 mg/dL after meals) is crucial, as failing to do so can lead to hyperglycemia or hypoglycemia, resulting in severe complications, including cardiovascular disease, nerve damage, and death\u003csup\u003e2\u003c/sup\u003e.\u0026nbsp;Consequently, frequent glucose monitoring is vital for management. Traditional finger-prick testing is often painful and inconvenient. An alternative, Continuous Glucose Monitoring (CGM), uses a sensor inserted under the skin to measure glucose in the interstitial fluid. However, CGM systems are often limited by high costs, discomfort, low stability, and inaccuracies arising from the difference between interstitial fluid and blood glucose levels\u003csup\u003e3-6\u003c/sup\u003e.\u0026nbsp;These challenges create a strong demand for non-invasive monitoring techniques to improve patient comfort and adherence. The urgency is emphasized by data from the International Diabetes Federation (IDF), which shows that 589 million adults (1 in 9) have diabetes as of 2025, with over 40% being undiagnosed. The IDF projects this number will rise to 853 million by 2050. This growing prevalence, combined with the flaws of current methods, underscores the critical need for accurate, non-invasive blood glucose sensors\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNon-Invasive Blood Glucose Monitoring: Recent Advances and Challenges\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant research has been conducted to develop innovative non-invasive (NI) glucose detection systems, aiming to provide more comfortable and continuous monitoring. Most of these systems are based on a variety of optical, spectroscopic, and electromagnetic methods, including near-infrared (NIR) technologies like IoT-enabled R-NIR sensors\u003csup\u003e8\u003c/sup\u003e, mbNIR sensors paired with neural networks\u003csup\u003e9\u003c/sup\u003e, photoplethysmography (PPG)\u003csup\u003e10\u003c/sup\u003e, custom diode arrays\u003csup\u003e11\u003c/sup\u003e, and commercial NIRS systems\u003csup\u003e12\u003c/sup\u003e. Other approaches utilize mid-infrared (MIR) spectroscopy through passive imaging\u003csup\u003e13\u003c/sup\u003e, tuneable quantum cascade lasers (QCLs)\u003csup\u003e14,15\u003c/sup\u003e, and IR sensors with fuzzy logic\u003csup\u003e16\u003c/sup\u003e. Foundational work has also been conducted to create high-power MIR frequency combs for spectroscopy\u003csup\u003e17\u003c/sup\u003e. Further techniques include photoacoustic multispectral systems\u003csup\u003e18\u003c/sup\u003e and microscopic photoacoustic spectroscopy\u003csup\u003e19\u003c/sup\u003e, Raman spectroscopy with individualized calibration models\u003csup\u003e20\u003c/sup\u003e, polarization-based sensing with machine learning\u003csup\u003e21\u003c/sup\u003e,\u0026nbsp;and low-power polarization-switching\u0026nbsp;\u003csup\u003e22\u003c/sup\u003e.\u0026nbsp;Additionally, AI-driven terahertz (THz) metasurface biosensors\u003csup\u003e23\u003c/sup\u003e,\u0026nbsp;and multisensor systems measuring impedance and temperature\u003csup\u003e24\u003c/sup\u003e. Additionally, readily accessible biological fluids, such as tears\u003csup\u003e25\u003c/sup\u003e, saliva\u003csup\u003e26-29\u003c/sup\u003e, sweat\u003csup\u003e30-34\u003c/sup\u003e, urine\u003csup\u003e35,36\u003c/sup\u003e, and respiratory moisture, have been employed in numerous enzyme-based electrochemical techniques to establish a correlation between their glucose content and glycemic levels. However, these methods often exhibit a delayed correlation with actual blood glucose variations and are susceptible to metabolic fluctuations.\u003c/p\u003e\n\u003cp\u003eRadio frequency (RF)/microwave techniques are a promising avenue for non-invasive glucose monitoring. By measuring how glucose affects the dielectric properties of blood, these methods circumvent issues that plague optical sensors, such as interference from skin pigmentation or temperature fluctuations. RF waves also penetrate tissue more deeply than light, allowing for more reliable and real-time measurements. Among these technologies, resonant-based sensors are highly effective. They operate by detecting shifts in resonance characteristics (like frequency, amplitude, or quality factor) caused by changes in a sample\u0026apos;s permittivity. A significant advantage of this approach is its potential for simple, low-cost physical structures that can be miniaturized for wearable devices\u003csup\u003e37,38\u003c/sup\u003e. However, the primary challenge remains achieving sufficient sensitivity to detect the minimal variations in blood glucose found in the human body\u003csup\u003e39\u003c/sup\u003e. For a sensor to be viable, it must be validated under realistic conditions. Testing in simple water-based solutions is insufficient, as it doesn\u0026apos;t replicate the complex, \u0026quot;lossy\u0026quot; nature of biological fluids. Therefore, performance must be evaluated with tissue-mimicking phantoms\u003csup\u003e40\u003c/sup\u003e and, most critically, with actual human blood samples to ensure dependable results\u003csup\u003e41,42\u003c/sup\u003e. Practical factors, such as operating within the physiological glucose range and considering the impact of sample volume and sensor placement are also crucial for demonstrating real-world performance\u003csup\u003e43\u003c/sup\u003e. Overcoming these challenges necessitates sophisticated engineering based on the principles of electromagnetic wave interaction with biological tissues\u003csup\u003e44\u003c/sup\u003e. A cornerstone of this approach is the strategic use of specialized resonant structures, notably Split-Ring Resonators (SRR) and Complementary Split-Ring Resonators (CSRR). These meticulously engineered structures exhibit a sharp resonant response and can confine a highly intense electric field within a tiny region. \u0026nbsp;By placing the sample in this \u0026quot;hotspot,\u0026quot; the wave-sample interaction is maximized, producing a larger and more easily detectable resonance shift even for tiny changes in glucose, thereby dramatically boosting sensitivity. Successful applications of this principle are evident in high-sensitivity chipless sensors for wearables and advanced CSRR structures, which achieve exceptional performance through impedance matching\u003csup\u003e45\u003c/sup\u003e. The next generation of these sensors integrates this core principle with other technologies. This includes using active electronic components to compensate for signal loss in blood\u003csup\u003e46\u003c/sup\u003e and applying advanced data processing, such as Convolutional Neural Networks (CNNs), to interpret complex sensor data accurately\u003csup\u003e41\u003c/sup\u003e. The ultimate goal is to develop a highly sensitive and reliable sensor platform that is structurally simple, cost-effective, and suitable for integration into wearable devices.\u003c/p\u003e\n\u003cp\u003eWe present a novel microwave sensor designed for the non-invasive measurement of blood glucose levels. As depicted in Fig. 1a, the sensor comprises a microstrip structure with two unit cells in a specific octagonal pattern, which are formed by etching complementary slit ring resonators (CSRRs) onto a copper ground plate. The cells are excited by two feeding lines engineered to create a 180-degree phase difference between them. This innovative approach, which combines octagonal geometry with a 180-degree phase difference, significantly enhances the sensitivity of traditional CSRR structures by concentrating the resonant electric field in the target area. The corresponding electric field distribution at the resonance frequency of 5.5 GHz (Fig.1b) confirms a highly concentrated and intense field within the CSRR elements. This focused interaction enhances sensitivity, enabling the detection of subtle changes in dielectric properties associated with different glucose concentrations. The sensor\u0026apos;s ability to detect these changes is demonstrated by tracking changes in its transmission frequency characteristics.\u0026nbsp;The proposed sensor exhibits excellent glucose sensitivity and a frequency resolution of approximately 2.3 MHz/(mg/dL) under laboratory conditions, which involve testing a blood-mimicking solution in a glass container as shown in Fig.1a. To assess its practical viability, the sensor\u0026apos;s performance was also investigated under more realistic conditions that account for the complexities of tissue structure and the effect of a sensor enclosure. Evaluating performance under such conditions provides greater confidence for its potential clinical use and commercialization. The high-resolution frequency shifts provided by the sensor increase its robustness against noise and measurement uncertainties.\u003c/p\u003e\n\u003cp\u003eFurthermore, its design ensures compatibility with wide dynamic range readout circuits, which facilitates simpler, more convenient, and accurate readings.\u0026nbsp;The designed sensor is capable of detecting glucose concentrations across the physiological range of 50 to 500 mg/dL with high resolution, a level of performance sufficient for accurate real-time glucose monitoring. The integrated octagonal cell sensor offers several advantages for assessing glycemic levels, including portability, non-invasiveness, low cost, compact dimensions, and ease of fabrication. This research represents a significant milestone toward developing a sensor for intermittent glucose monitoring, specifically designed for strategic placement on the wrist. The current design can be readily adapted for wearable applications by fabricating the CSRR sensing elements on a flexible substrate and integrating the device with a suitable electronic reader.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Parameter values\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eValue(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eValue(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eValue(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eL\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e7.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eL\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eL\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003ed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eL\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eL\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eThis section provides a detailed description of the proposed microwave glucose sensor, covering its design, key parameters, numerical simulations, and experimental validation. The sensor is designed to operate at approximately 5.5 GHz within the ISM band, a frequency chosen to ensure that electromagnetic waves penetrate sufficiently to reach the tissue. This choice optimizes penetration depth while minimizing tissue losses, thereby enhancing the sensor\u0026apos;s sensitivity to subtle changes in blood\u0026apos;s dielectric properties associated with varying glucose concentrations\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. These variations in the dielectric constant and loss tangent were studied from 1 to 7 GHz using the first-order Debye relaxation model, which shows a slight increase in dielectric constant and a corresponding decrease in loss tangent as glucose concentration rises\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. As depicted in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec, the electric field lines at the resonant frequency of 5.5 GHz form a closed loop between two unit cells. This configuration, where the lines originate from one unit cell, traverse the glass and blood, and subsequently return to another unit cell, creates a highly concentrated electric field and a strong coupling between the unit cells. Consequently, the microstrip structure\u0026apos;s frequency response reaches its maximum potential effectiveness. The sensor\u0026apos;s physical architecture features two identical octagonal Complementary Split-Ring Resonators (CSRRs). As illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea, these resonators are etched onto the 35 \u0026micro;m thick copper ground plane of a Rogers 4003 PCB, which has a dielectric permittivity (ɛ\u003csub\u003er\u0026prime;\u003c/sub\u003e ) of 3.55 and a loss tangent (tan\u0026delta;) of 0.0027. For practical application and test repeatability, the sensor is housed within an aluminum enclosure. Excitation of the CSRRs is achieved through efficient coupling to a microstrip transmission line (MTL), which was optimized with a width of 1.1 mm, a thickness to 0.035 mm, and an impedance of 50 Ω for optimal power transmission. Two distinct topologies of this design were implemented and optimized: the first for blood samples contained in glass vessels (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea) and the second for a simplified wrist model (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). In this configuration, the two octagonal cells are positioned vertically along the MTL axis, separated by a distance of D\u0026thinsp;=\u0026thinsp;5.2 mm. Each CSRR unit cell comprises two concentric octagonal rings with precisely defined geometric parameters: an outer ring diagonal length (a) of 5 mm, an inner ring length (b) of 4.12 mm, a coupling-split (d) of 0.2 mm, a dielectric split (c) of 0.2 mm, and a metal split-gap (g) of 0.2 mm, with the splits for each ring on opposite diagonal sides. A comprehensive schematic of the sensor configuration, detailing all geometric parameters, is shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb, with specific values provided in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. To significantly boost sensor sensitivity, a novel approach incorporating a 180\u0026deg; phase shift between the two unit cells was introduced. This phase difference creates the closed-loop electric field configuration, effectively trapping electromagnetic energy within the sensor\u0026apos;s immediate vicinity. As a result, the dielectric properties of loaded samples, such as glucose-laden blood tissue, can be measured with high accuracy. The design and geometric parameters of the planar transmission line and the etched unit cells were therefore meticulously optimized to achieve a sharp transmission resonance around f₀ = 5.5 GHz when the sensor is loaded with a sample.\u003c/p\u003e\n\u003ch3\u003eGlucose detection mechanism\u003c/h3\u003e\n\u003cp\u003eTo simulate the frequency behavior of the integrated CSRR sensor, a lumped-element model was developed, as illustrated in Fig.\u0026nbsp;2\u003csup\u003e49,50\u003c/sup\u003e. In this model, each of the two identical octagonal cell resonators is represented by a parallel RLC resonant circuit (L\u003csub\u003eR1\u003c/sub\u003e, C\u003csub\u003eR1\u003c/sub\u003e, R\u003csub\u003eR1\u003c/sub\u003e and L\u003csub\u003eR2\u003c/sub\u003e, C\u003csub\u003eR2\u003c/sub\u003e, R\u003csub\u003eR2\u003c/sub\u003e). The inductance (L\u003csub\u003eR\u003c/sub\u003e) arises from the structure\u0026apos;s dielectric rings, the capacitance (C\u003csub\u003eR\u003c/sub\u003e) is formed by the metal splits and spacers, and the resistance (R\u003csub\u003eR\u003c/sub\u003e) accounts for conductive and dielectric losses. A coupling inductance L\u003csub\u003ec1\u003c/sub\u003e models the microstrip transmission line (MTL) that excites the resonators. In contrast, the coupling between the MTL and the CSRR structure is represented by a shunt capacitor, C\u003csub\u003ec1\u003c/sub\u003e, in parallel with a resistor, R\u003csub\u003ec1\u003c/sub\u003e, to account for substrate and conduction losses. A key aspect of the model is the method for creating the 180\u0026deg; phase shift: while the first cell is excited via L\u003csub\u003ec1\u003c/sub\u003e, the second cell is coupled to the feed line through an additional, distinct inductor (L\u003csub\u003ec2\u003c/sub\u003e), which effectively models the two different excitation paths. Finally, when a sample is loaded onto the sensor, its dielectric properties are incorporated by adding a parallel RC circuit (C\u003csub\u003eM\u003c/sub\u003e, R\u003csub\u003eM\u003c/sub\u003e) coupled to each resonator. The capacitance (C\u003csub\u003eM1\u003c/sub\u003e, C\u003csub\u003eM2\u003c/sub\u003e) is directly related to the sample\u0026apos;s relative permittivity, and the resistance (R\u003csub\u003eM1\u003c/sub\u003e, R\u003csub\u003eM2\u003c/sub\u003e) corresponds to its loss characteristics. Due to the symmetrical design, the values of these sample-related components are identical for both cells.\u003c/p\u003e\n\u003cp\u003eChanges in the dielectric permittivity of the blood samples affect the electric field distribution, which can be observed in the resonance frequency f\u003csub\u003eR\u003c/sub\u003e through changes in the effective capacitor C\u003csub\u003ee\u003c/sub\u003e CSRR (C\u003csub\u003ee\u003c/sub\u003e =C\u003csub\u003em\u003c/sub\u003e||C\u003csub\u003eR\u003c/sub\u003e ). Therefore, changes in resonance frequency can be used to determine the glucose concentration of the sample. The resistor R\u003csub\u003ee\u003c/sub\u003e (R\u003csub\u003ee\u003c/sub\u003e =R\u003csub\u003em\u003c/sub\u003e||R\u003csub\u003eR\u003c/sub\u003e ), which represents the combined resistance of R\u003csub\u003ec\u003c/sub\u003e and the CSRR-part R\u003csub\u003eR\u003c/sub\u003e, is mainly affected by the loss characteristics of the blood sample. Changes in tan\u0026delta; are reflected as changes in the amplitude of the resonance profile. These changes in resonance properties are a signature of the dielectric properties of the blood sample, which can be related to the glucose level through analysis of the modified resonance behavior. The arrangement of the lumped elements stores oscillating electric and magnetic energy in the inductance and capacitance. These are caused by the induced charges and currents within the patterned dielectric loops or slots when the CSRRs are excited. When the electric and magnetic energies are balanced, the microwave sensor resonates at a specific frequency, as shown in Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). This resonance is directly seen as the lowest point in the transmission coefficient S\u003csub\u003e21\u003c/sub\u003e.\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{f}_{R}\\left({\\epsilon\\:}_{r}^{{\\prime\\:}}\\right)=\\frac{1}{2\\pi\\:\\sqrt{{L}_{R}\\left({C}_{e}\\left({\\epsilon\\:}_{r}^{{\\prime\\:}}\\right)+{C}_{c}\\right)}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eTo evaluate the performance and resonance frequencies of the proposed sensor under loaded conditions, numerical simulations were conducted using CST Microwave Studio. The simulation modeled a cylindrical glass container, designed to hold 0.5 mL of blood samples on the CSRR surface, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea. The container had an outer diameter of 11 mm, an inner diameter of 9 mm, a wall thickness of 1 mm, and a height of 25 mm. The unipolar Debye model (first order), presented by Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), was used to create numerical models for the dielectric properties of dispersed glucose-blood samples at different concentrations. This model was developed in \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e based on spectroscopic measurements of 50, 250, 1000, and 2000 mg/dL aqueous solutions collected using a commercial coaxial probe kit connected to a VNA. This model is the most reasonable approximation for the behavior of blood glucose.\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:{\\epsilon\\:}_{r}\\left({w}_{9}\\xi\\:\\right)={\\epsilon\\:}_{\\infty\\:}\\left(\\xi\\:\\right)+\\left(\\frac{{\\epsilon\\:}_{stat}\\left(\\xi\\:\\right)-{\\epsilon\\:}_{\\infty\\:}\\left(\\xi\\:\\right)}{1+jw\\tau\\:\\left(\\xi\\:\\right)}\\right)+\\frac{{\\sigma\\:}_{s}}{jw{\\epsilon\\:}_{0}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eEquation (\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) defines the complex permittivity of the blood solution of glucose concentration \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\xi\\:\\)\u003c/span\u003e\u003c/span\u003e (in mg/dL) at the angular frequency \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:w\\)\u003c/span\u003e\u003c/span\u003e. The parameters \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{stat}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{\\infty\\:}\\)\u003c/span\u003e\u003c/span\u003e, and \u0026tau; are concentration-dependent Debye coefficients\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{s}\\)\u003c/span\u003e\u003c/span\u003e is static conductivity, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the permittivity of free space. Blood samples with glucose concentrations ranging from S\u003csub\u003e1\u003c/sub\u003e-S\u003csub\u003e14\u003c/sub\u003e (50\u0026ndash;500 mg/dL) were simulated above the sensing area within the glass container. This concentration range encompasses a broad spectrum of diabetic conditions, including hypoglycemia (\u0026lt;\u0026thinsp;70 mg/dL) and hyperglycemia (\u0026gt;\u0026thinsp;130 mg/dL). As can be observed in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, five distinct resonances were identified in the transmission scattering parameters, centered approximately at f\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.05 GHz, f\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.4 GHz, f\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.15 GHz, f\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.5 GHz, and f\u003csub\u003e5\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;6.7 GHz. Resonance frequency shifts were simulated for selected concentrations (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), and the resulting transmission frequency response changes are depicted in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb and c. The most pronounced resonance frequency changes were observed at the fourth resonance (approximately 5.5 GHz), prompting a more detailed analysis of this frequency range. In addition, the resonances exhibited damping characteristics, characterized by a significant decrease in the amplitude of the resonance peaks. This attenuation is attributed to the absorptive properties of the blood sample. To highlight the linear correlation between glucose concentration and the frequencies of the second to fifth resonances, Fig. 4a and 4b present linear regression models for each blood glucose range (the first resonance shows almost no change). These results demonstrate a strong linear relationship, suggesting that the sensor can be calibrated for individual patients to accurately measure blood glucose levels continuously.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eParameter values\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValue (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003csub\u003e11\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003csub\u003e12\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003csub\u003e13\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003csub\u003e14\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValue (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTo simulate a more realistic scenario, a second analysis was performed using a simplified wrist model placed in the sensing area, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea. This model consisted of a\u0026thinsp;=\u0026thinsp;1 mm thick skin layer, a\u0026thinsp;=\u0026thinsp;1 mm thick fat layer, and a\u0026thinsp;=\u0026thinsp;1 mm radius cylinder filled with blood. The resulting electric field lines at the resonant frequency of 5.2 GHz (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb) show a diminished interaction with the blood compared to the previous lab-based scenario. This reduction in intensity is an expected consequence of the high-permittivity skin (ɛ\u003csub\u003er\u003c/sub\u003e=40) and fat (ɛ\u003csub\u003er\u003c/sub\u003e=10) layers, which absorb and disperse the field. Nevertheless, a significant coupling between the two unit cells persists, ensuring that the electric field intensity interacting with the blood is maximized under these more challenging, realistic conditions. The sensitivity of many prior sensors has been characterized in vitro using phantoms, where performance relies on the stark dielectric contrast between the blood sample and the surrounding air. This high contrast naturally concentrates the electric field within the sample, yielding a strong response. However, this mechanism is less effective for realistic in-vivo applications. The proximity of skin and fat tissue, which have high dielectric constants, disperses the electric field and diminishes its intensity in the target blood vessels, rendering the simple contrast-based approach unreliable. An effective in-vivo sensor must therefore achieve field confinement through deliberate design, creating the focus of the electric field that is robust to such environmental loading effects. Our work introduces a novel approach that accomplishes this by leveraging a 180-degree phase differential between two unit cells. This configuration induces strong electromagnetic coupling, which becomes the primary mechanism for focusing the electric field into the target region. Consequently, the sensor\u0026apos;s high sensitivity is an engineered feature arising from this internal coupling, rather than a passive reliance on the dielectric properties of its surroundings. This methodology represents a significant and necessary departure from previous designs.\u003c/p\u003e\n\u003cp\u003eTo investigate the influence of skin thickness on sensor sensitivity, simulations were conducted using a wrist model with varying skin thicknesses of 0.5 mm, 1 mm, and 1.5 mm. The corresponding transmission responses were analyzed. While skin thickness varies due to factors like body position, gender, skin type, age, race, and geographic location, the chosen thicknesses represent a practical range for the human wrist. As depicted in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea, three distinct resonances were observed in the scattering responses, centered at specific frequencies: f\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.1 GHz, f\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.2 GHz, and f\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;6.3 GHz. As illustrated for the 1 mm case in Figs. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec, shifts in glucose concentration correlate directly with shifts in the resonant frequency. Notably, as depicted in Figs. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ec, the second resonance in each response provided valuable information about the glucose concentration of the simulated samples beneath the skin and fat layers. However, the analysis confirmed that signal attenuation is directly proportional to skin thickness. This phenomenon weakens the electric field\u0026apos;s interaction with the glucose sample, leading to a decline in sensor sensitivity. The average sensitivity was measured to be 1.85 MHz/(mg/dL) for 0.5 mm skin, 1.78 MHz/(mg/dL) for 1 mm skin, and 1.6 MHz/(mg/dL) for 1.5 mm skin.\u003c/p\u003e\n\u003cp\u003eCrucially, while this decline in sensitivity is relatively small, it highlights a significant source of inter-individual variability. Such variations, if unaddressed, could lead to inaccurate measurements between different users. Therefore, to ensure clinical accuracy in practical applications, a subject-specific calibration protocol is essential.\u003c/p\u003e\n\u003cp\u003eFigures\u0026nbsp;7a and 7b present linear regression models for each blood glucose range.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIn-vitro experiments.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the proposed glucose sensor, rigorous laboratory testing was conducted using a Vector Network Analyzer (VNA). As illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, two identical prototypes were fabricated to assess manufacturing tolerances and record sensor transmission responses for various glucose samples. Real blood samples from three individuals aged 24, 27, and 32 were used to mimic the behavior of blood at various glucose concentrations within the clinically relevant range for type 2 diabetes (80\u0026ndash;320 mg/dL). The blood samples were divided into multiple identical containers, each containing 0.5 ml of blood, and different concentrations of blood glucose were obtained by adding varying amounts of dextrose powder. Repeating these tests with three distinct blood samples from individuals of different ages and blood characteristics helped to ensure the reproducibility of the measured scattering data.\u003c/p\u003e\n\u003cp\u003eWhile many previous studies have employed glucose-aqueous solutions for initial experiments in non-invasive glucose detection using RF sensors, this study utilized more realistic sample conditions by using actual blood samples. The choice of real blood samples was motivated by the understanding that different blood concentrations, blood groups, and other individual blood characteristics can influence measurement results. Two of the individuals had blood group O+, while one had blood group B+.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e presents the measured transmission coefficients (S\u003csub\u003e21\u003c/sub\u003e) for the fabricated sensors in both unloaded and loaded states within the 1\u0026ndash;6 GHz frequency range. The measured resonant frequencies for both prototypes closely matched the values predicted by simulations. The two sensors exhibited nearly identical performance, with only minor variations in resonance depth and frequency, which are attributed to normal manufacturing tolerances.\u003c/p\u003e\n\u003cp\u003eFor loaded measurements, cylindrical glass containers were used to hold the prepared blood samples on the sensor\u0026apos;s surface, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eb. Placing the empty container on the sensor introduced a baseline frequency shift of a few megahertz from the unloaded resonance frequency. In each experiment, a micropipette was used to dispense a precise 0.5 mL volume of each sample to minimize errors arising from volume uncertainty, and the corresponding shifts in the transmission resonance were recorded. The actual glucose concentration of each prepared sample was independently verified using a commercial blood glucose level (BGL) monitoring device.\u003c/p\u003e\n\u003cp\u003eThe experimental transmission response of the sensor was measured across a range of glucose concentrations (80\u0026ndash;340 mg/dL), as shown in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e. These results revealed two key phenomena: a consistent downward shift in resonant frequency with increasing glucose concentration, and detectable changes in resonance amplitude due to slight variations in the sample\u0026apos;s loss tangent.\u003c/p\u003e\n\u003cp\u003eAs predicted by simulations, the fourth resonance (f\u003csub\u003e4\u003c/sub\u003e) exhibited the highest sensitivity to these changes. A linear regression model of the frequency response, illustrated in Fig.\u0026nbsp;10, was used to quantify this performance. The analysis yielded an experimental sensitivity of approximately 2.3 MHz/(mg/dL) at f\u003csub\u003e4\u003c/sub\u003e. This result confirms the value predicted in our simulations and validates the sensor\u0026apos;s high-frequency resolution for detecting small changes in dielectric properties.\u003c/p\u003e\n\u003cp\u003eTo ensure accuracy, all glucose measurements were repeated three times, and the average values were reported. This averaging process helped eliminate possible random noise from the power supply (i.e., VNA) or other unrelated sources, thereby improving the signal-to-noise ratio (SNR). Considering that the sensor frequency reading for 10 mg/dL is approximately 23 MHz (with an average sensitivity of 2.3 MHz/(mg/dL)), the proposed measurement platform can reliably identify glucose concentrations as low as 1 mg/dL. Since the scattering response of the sensor is strongly dependent on the electromagnetic properties of the sample, which are temperature-dependent, all prepared glucose samples were kept in a temperature-controlled room of 25\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C. Therefore, small changes in ambient temperature have a negligible effect on the resonance measurement of the CSRR sensor. This is because most of the electromagnetic energy is concentrated in the octagonal cell area and the near field region, which interacts with the sample tissue. Additionally, the high concentration of the electric field at the sample location makes the sensor more sensitive to changes in the sample.\u003c/p\u003e\n\u003cp\u003eA comprehensive comparison of the sensitivity performance of the proposed sensor with other recent microwave sensors is presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. This comparison ranks advanced glucose sensors based on their sensitivity to relevant parameters. Sensitivity is defined as the change in frequency (\u0026Delta;f\u003csub\u003eR\u003c/sub\u003e) per unit change in glucose concentration (1 mg/dL) for a given volume and specific test setup. As a result, the sensitivity obtained in previous works based on different microwave sensing mechanisms was significantly lower than the minimum resolution adopted by the proposed sensor of this research. Additionally, most of the previously proposed sensors have not been investigated under the more complex and realistic conditions considered in this study. Therefore, the sensitivity obtained for the more realistic conditions investigated in this study is another distinguishing feature of this research. The sensitivity of the proposed sensor surpasses that of other techniques, which rely on tracking slight changes in the S11 and S21 resonance magnitudes, requiring high-precision measuring instruments. The improved design of the CSRR elements in this work enhances the interaction between the sample and the sensor in the sensor region, allowing the resonant frequency response of the sensor to be defined mainly by the passage of the sample under test (SUT).\u003c/p\u003e\n\u003cp\u003eThe sensitivity achieved in this study, at 2.3 MHz/[mg/dL] under standard conditions and 1.78 MHz/[mg/dL] under more practical circumstances, surpasses the highest reported values to date, as determined by our investigations. Moreover, the sample volume utilized in this research aligns with realistic conditions and the volume of blood in the wrist that engages with the sensor. The sensor\u0026apos;s performance under these conditions demonstrates enhanced reliability compared to conventional counterparts, suggesting its suitability for real-world applications.\u003c/p\u003e\n\u003cp\u003eThe proposed sensor can be effectively used to detect the normal blood glucose range as well as cases of hypoglycemia and hyperglycemia.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of different glucose sensors.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensing Technique\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOperation\u003c/p\u003e\n \u003cp\u003eFrequency (GHz)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest solution\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConcentration (mg/dL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensing\u003c/p\u003e\n \u003cp\u003eparameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS (MHz/[mg/dL])\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esplit ring resonator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAqueous solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-5000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e21\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBand-stop filter based on SIW cavity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5-5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe fingertip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u0026ndash;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e21\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ethree-loop microstrip patch antenna\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAqueous solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e11\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emeta-structured antenna\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAqueous solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e11\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwo-port Rectangular Dielectric Resonator (RDR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe fingertip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90\u0026ndash;403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e21\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOmega-coupled split-ring resonator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe fingertip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e11\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eclosed-loop split ring resonator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOn the forearm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u0026ndash;155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e11\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epatch antenna\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAqueous solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e11\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOpen-loop resonator with electric coupling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAqueous solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89\u0026ndash;456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e21\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubstrate Integrated Waveguide (SIW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAqueous solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e11\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComplementary split-ring resonator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAqueous solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e21\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFingertip placed on a planar resonator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u0026ndash;2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe fingertip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u0026ndash;188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e11\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esquare-shaped spiral ring resonator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAqueous solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u0026ndash;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e11\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDual-band bandpass filter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45\u0026ndash;5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAqueous solution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e11\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThis work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOctagonal-shaped complementary split ring resonator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3\u0026ndash;5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe Wrist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u0026ndash;340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e21\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThis work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOctagonal-shaped complementary split ring resonator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.4\u0026ndash;5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood sample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ef\u003csub\u003eR\u003c/sub\u003e (S\u003csub\u003e21\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis paper presents the design, simulation, and experimental validation of a novel microwave sensor for non-invasive blood glucose monitoring. By employing an innovative octagonal CSRR configuration with an engineered 180-degree phase difference between unit cells, we achieved a highly concentrated and intense electric field, maximizing its interaction with the sample under test. This advanced design principle yielded unprecedented sensitivity, which was rigorously verified through both realistic simulations and in vitro experiments using real human blood samples. The sensor demonstrated an exceptional experimental sensitivity of 2.3 MHz/(mg/dL) under laboratory conditions and a simulated sensitivity of 1.78 MHz/(mg/dL) in a more practical wrist model, values that surpass those of previously reported microwave-based sensors. The strong linear correlation observed between resonance frequency shifts and glucose concentrations, ranging from 50 to 500 mg/dL, confirms the sensor's potential for reliable and accurate glucose tracking across the full clinical range.\u003c/p\u003e\u003cp\u003eLooking to the future, the remarkable sensitivity achieved by this sensor generates a wealth of high-resolution data that necessitates sophisticated analysis. While this research establishes a robust proof of concept, the next critical step involves addressing the processing of these specific and complex results. We suggest that future work should focus on leveraging the power of machine learning and artificial intelligence. An AI-driven approach could analyze and compensate for numerous individual and environmental parameters, such as skin thickness, body temperature, motion artifacts, sweat composition, and unique tissue dielectric properties. This would unlock powerful operational applications, including integration into closed-loop systems for automated insulin injection, predictive alerts for hypoglycemia and hyperglycemia, and real-time dietary feedback based on glucose response. Such an intelligent system would represent a significant leap towards fully automated and personalized diabetes management, transforming patient care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the conclusions of this research can be obtained from M.T. and A.J., but there are limitations to accessing this data. The data used in this study were obtained under a license and are not publicly accessible. However, the authors can provide the data upon a reasonable request, provided permission is obtained from M.T.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental protocols were approved by the Research Ethics Committee of Iran University of Science and Technology and were conducted in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects. All participants were volunteers and were fully informed about the purpose of the study and the nature of the procedures before providing their consent.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLaugesen, C., Ranjan, A. G., Schmidt, S. \u0026amp; N\u0026oslash;rgaard, K. 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The design features an octagonal array of complementary slotted ring resonators (CSRRs) on a dielectric substrate, operating safely in the industrial, scientific, and medical (ISM) frequency band. Its key innovation, an engineered 180\u003csup\u003e∘\u003c/sup\u003e phase difference between adjacent unit cells, generates a highly concentrated electromagnetic (EM) field at the sample interface. This focused interaction significantly enhances measurement sensitivity and overall detection capability. The sensor accurately detects glucose concentrations across the 50\u0026ndash;500 mg/dL clinical range, demonstrating a remarkable sensitivity of 2.3 MHz/(mg/dL) in laboratory settings and 1.78 MHz/(mg/dL) in realistic scenarios, surpassing existing microwave sensors. This superior performance is attributed to the CSRR architecture, which maximizes the sample's EM field interaction, enabling the precise quantification of subtle dielectric changes corresponding to varying glucose levels. Laboratory verification using a vector network analyzer (VNA) confirmed significant frequency shifts with glucose samples from 80 to 340 mg/dL. Beyond its high sensitivity, the sensor\u0026rsquo;s compact size, simple fabrication, affordability, and non-ionizing operation establish it as a promising candidate for developing practical, real-time, non-invasive glucose monitoring systems to advance diabetes management.\u003c/p\u003e","manuscriptTitle":"Innovative Design of a Microwave Sensor for Non-Invasive Monitoring of Blood Glucose Level with High Sensitivity Using Electromagnetic Properties","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-18 16:09:35","doi":"10.21203/rs.3.rs-7494729/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-13T17:12:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T18:38:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T17:51:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T14:55:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252898135882229140559837594470643215055","date":"2025-09-12T04:07:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52697358945514409989074384210408210874","date":"2025-09-10T07:44:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159924972537591671458434502305470500794","date":"2025-09-10T03:37:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-10T03:36:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-10T03:33:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-09T20:36:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T11:12:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-05T11:08:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c9fb4ff1-b616-4859-9b2e-d6a12d94f64d","owner":[],"postedDate":"September 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54670482,"name":"Physical sciences/Engineering"},{"id":54670483,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2026-03-09T16:05:10+00:00","versionOfRecord":{"articleIdentity":"rs-7494729","link":"https://doi.org/10.1038/s41598-026-41378-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-03 15:58:35","publishedOnDateReadable":"March 3rd, 2026"},"versionCreatedAt":"2025-09-18 16:09:35","video":"","vorDoi":"10.1038/s41598-026-41378-6","vorDoiUrl":"https://doi.org/10.1038/s41598-026-41378-6","workflowStages":[]},"version":"v1","identity":"rs-7494729","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7494729","identity":"rs-7494729","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}