Chromium-doped iron(III) oxide (Cr-Fe2O3) thin films were deposited via spray pyrolysis at 420 degrees C with chromium (Cr) contents of 1, 5, and 10 wt%. X-ray diffraction confirmed single-phase rhombohedral iron(III) oxide (Fe2O3), while FE-SEM revealed grain coarsening with Cr incorporation, verified as Cr3+ by EDX and XPS. In addition, the optical transmission analysis yielded film thicknesses of similar to 535-600 nm and direct band gaps of 2.21 eV (1%, 5%) and 2.18 eV (10%), with Urbach energies indicating minimal disorder near 5 wt% Cr. Moreover, the electrochemical impedance spectroscopy (EIS) fitted with Rs-(R2 parallel to CPE1)-(R3 parallel to CPE2) showed an improved high-frequency transport with doping (Rs approximate to 40 -> 11 Omegacm(2); R2 approximate to 1.9 x 10(3) -> 1.2 x 10(2) Omegacm(2)), but anincreased low-frequency interfacial resistance of (R3 approximate to 7.3 x 10(4) -> 3.6 x 10(6) Omegacm(2)). Then, the Mott-Schottky analysis confirmed the n-type behavior with a donor density (N-D) approximate to 3.2-3.8 x 10(18) cm(-3) and a conduction-band minimum (CBM) positively shifting from 1% to 5% Cr, partially relaxing at 10%. Overall, the moderate Cr incorporation enhances the bulk transport yet increases interfacial blocking behavior, revealing a trade-off that points to the intermediate doping as the optimal compromise.

A novel NiO/(TiO2-Na2Ti3O7) p-n heterojunction nanocomposite was developed as a dual-functional photocatalyst for solar-driven dye degradation and photoelectrochemical hydrogen production. Structural and morphological characterizations confirmed successful heterojunction formation, where XRD revealed highly crystalline NiO, TiO2, and Na2Ti3O7 phases, SEM showed intimate interfacial contact between rod-like titanate structures and NiO nanoparticles, and EDX verified elemental purity. The fabricated composites exhibited band gap energies in the range of 1.99 - 2.37 eV, indicating enhanced visible-light absorption. Among the tested compositions, the NiO/(TiO2-Na2Ti3O7) nanocomposite with a 1 : 0.5 wt ratio displayed the highest performance. This optimized sample achieved complete methylene blue degradation within 60 min under natural sunlight and produced a photocurrent density of - 34 mA cm-2 at - 0.34 V vs. RHE under simulated solar illumination. The nanocomposite also exhibited good stability and reusability, highlighting its potential as a costeffective photocatalyst for wastewater remediation and sustainable hydrogen generation.

This study presents the design, electromagnetic modeling, and numerical evaluation of a multi-layer terahertz (THz) metasurface biosensor for label-free peptide detection. The sensor uses a stacked resonator architecture with a central MXene (Ti₃C₂Tₓ) circular disk, BaTiO₃ rectangular resonators, a WS₂ annular ring, a phosphorene square ring, and a graphene backplane on a SiO₂ substrate. Aptamer functionalization enables selective recognition of target peptides. Full-wave simulations in COMSOL Multiphysics show peak transmission stability exceeding 98.6

This study proposes quasi-periodic phononic crystal architectures with Terfenol-D layers for highly sensitive magnetic field sensing. Three sequences, Thue-Morse, double-period and Fibonacci, are examined to realize tunable acoustic band structures that enhance defect-mode interaction under external magnetic fields. A transfer matrix method analyses bandgap formation and defect-mode localization. Sensing is governed by a localized defect mode within the bandgap and magneto-acoustic coupling in Terfenol-D, where magnetic fields alter elastic properties and acoustic impedance, causing measurable spectral shifts. Thickness optimization identifies the best design. The Thue-Morse sequence exhibits the strongest localization and interaction. Maximum sensitivity reaches 4.54 & times; 10 & sup3; Hz/Oe, with quality factor 580.2, signal-to-noise ratio 45.4, figure of merit 0.908 Oe(-1), and detection limit 4.28 & times; 10 & sup2; Oe. Compact high-performance sensors for real-time monitoring in industrial biomedical environmental systems are enabled, offering stable, precise low-power operation with small footprints and robust repeatable measurements for future integrated portable smart networked sensing platforms worldwide today and beyond.

In this research, we present a multilayer metasurface sensor design integrating graphene, MXene, black phosphorus, and gold for the ultrasensitive detection of brain tumor biomarkers in liquid biopsy samples. The hierarchical structure consists of a MXene-coated rectangular resonator, a black phosphorus-coated square resonator, a gold-coated circular ring, and a graphene-based circular substrate. This architecture was systematically optimized through comprehensive numerical simulations using COMSOL Multiphysics 6.3, integrated with machine learning frameworks. The proposed sensor demonstrates an outstanding sensitivity of 2308 GHz/RIU across a physiologically relevant refractive index range (1.3333–1.4833), significantly outperforming current state-of-the-art devices. Performance analysis identifies an optimal sensing regime at RI = 1.3425, achieving a figure of merit of 20.79 RIU−1 and a detection limit as low as 0.079 RIU. Detailed investigations of the transmission spectra under varying graphene chemical potentials (0.1–0.9 eV), incident angles (0°–80°), and geometric modifications of the resonators reveal highly tunable sensing behavior. Furthermore, Random Forest Regression models achieve predictive accuracies of 85%–100%, enabling reliable estimation of sensor performance across diverse operating conditions. Collectively, these results establish a solid foundation for employing advanced 2D material–based metasurfaces in minimally invasive and early-stage brain tumor diagnostics, thereby advancing the capabilities of next-generation liquid biopsy technologies.

Rapid detection of bacterial contamination in aquatic systems requires field-deployable sensing platforms with reproducible analytical performance. This study presents a multilayer plasmonic biosensor proposed for the simulation-based detection of waterborne bacteria. The sensor operates via localized electromagnetic field enhancement and resonance frequency shifts induced by bacterial analyte adsorption. Finite-element simulations in COMSOL Multiphysics were performed across graphene chemical potentials of 0.1–0.9 eV and incident angles of 0–80°. The sensor achieved an average refractive index sensitivity of 811 GHz/RIU, computed from linear regression of resonance frequency against refractive index across the full detection range (R2 > 0.70, n = 1.33–1.3921 RIU). A secondary local maximum sensitivity of 976 GHz/RIU was additionally observed near the upper end of the detection range and is reported for completeness. The minimum detection limit is 0.071 RIU. Under analyte detection conditions within the refractive index range n = 1.33–1.3921 RIU, with resonance frequencies spanning 0.167–0.175 THz and a constant full width at half maximum of 0.054 THz, quality factors ranged from 3.093 to 3.241. Polynomial regression models predicted resonance shifts with coefficients of determination up to 0.94, enabling real-time calibration and performance monitoring. The proposed fabrication protocol employs chemical vapour deposition for graphene synthesis, electron-beam lithography for pattern definition, and thin-film deposition for multilayer stack formation using exclusively graphene, copper, aluminium, BaTiO3, and SiO2, conforming to standard microfabrication procedures adaptable for environmental monitoring and water quality assessment. All results are derived exclusively from electromagnetic simulations and numerical analysis. No experimental fabrication or measurements were taken.

This work presents a terahertz (THz) glucose biosensor based on a graphene–gold hybrid metasurface that combines strong plasmonic confinement with electrical tunability. The sensor was numerically analysed using the finite element method, and its performance was evaluated over a refractive index range of 1.335–1.347 RIU, corresponding to glucose-induced variations in blood and interstitial fluid. The optimized structure achieved a maximum sensitivity of 1000 GHz·RIU⁻1, with a constant full width at half maximum of 0.068 THz, yielding a figure of merit and detection accuracy of 14.706 RIU⁻1. The resonance frequency exhibited a linear dependence on both glucose concentration (R2 = 1.00) and refractive index (R2 = 0.85). Bayesian ridge regression was employed to model the relationship between resonance characteristics and sensing parameters, achieving high predictive accuracy with quantified uncertainty. The results demonstrate the potential of graphene–gold metasurfaces for high-performance, non-invasive THz glucose sensing.

This work presents a theoretically optimized multilayer surface plasmon resonance (SPR) biosensor for quantitative hemoglobin detection using the Kretschmann configuration. The sensor integrates a BK-7 prism, silver plasmonic layer, graphene enhancement layer, zirconium nitride (ZrN) protective layer, and aqueous sensing medium. This architecture synergistically combines enhanced electromagnetic confinement with chemical stability, addressing silver's oxidation vulnerability while maintaining superior plasmonic performance. Electromagnetic analysis via transfer matrix method and finite-element simulations demonstrates exceptional sensitivity metrics: maximum angular sensitivity of 500 degrees/RIU, figure of merit of 92.25 RIU-1, and detection limit of 0.006 RIU across clinically relevant hemoglobin concentrations (10-40 g/L). Localized electric field enhancement (similar to 10(6) V/m) at the sensing interface confirms optimal light-matter interaction amplification. Machine learning models predict sensor responses to graphene thickness and refractive index variations with R-2 > 0.99, enabling rapid optimization. This design advances SPR biosensor technology for sensitive, label-free biochemical detection applications.

Persistent divalent metal cations such as Cu2+ and Mg2+ accumulate in freshwater systems and pose measurable risks to water quality and human exposure. Established analytical techniques, including inductively coupled plasma mass spectrometry and atomic absorption spectroscopy, require centralized laboratories, extensive sample preparation, and batch processing, which limits their use for continuous in situ monitoring at low concentrations. This study presents a terahertz frequency MXene-based metasurface sensor that integrates graphene, silver, strontium titanate, and copper layers to increase electromagnetic field confinement and coupling between the sensing surface and dissolved ions. The sensing mechanism relies on conductivity changes in MXene nanosheets induced by ion adsorption, modulation of the graphene surface response through electrostatic gating, and refractive index sensitivity in the terahertz band. Finite element simulations show that the sensor achieves a spectral sensitivity of 151.1 GHz/RIU for Cu2+ and 227.8 GHz/RIU for Mg2+. The corresponding figures of merit are 1145.5 and 1759.5 RIU−1, with theoretical detection limits of 4.7 × 10−4 RIU and 2.7 × 10−4 RIU. Resonance frequency shifts exhibit linear relationships with refractive index changes, with R2 values above 0.993, and with ion concentration, with R2 values above 0.895. Random Forest regression was employed as a surrogate modeling tool to interpolate sensor responses obtained from physics-based simulations. The dataset was divided using an 80:20 train–test split, and model performance was evaluated on unseen test data. High coefficients of determination (R2 > 0.998) reflect the smooth, deterministic nature of the simulated electromagnetic response rather than experimental variability.

The demand for high-performance sensing technologies is significantly related to the need for accurate temperature monitoring across diverse industrial sectors. Due to their exceptional sensitivity to temperature changes, optical temperature sensing devices have received considerable attention. Meanwhile, we have theoretically introduced in the present study a high-sensitivity terahertz (THz) temperature sensor. The mainstay of this sensor is based on the design of a hybrid architecture that integrates a graphene monolayer with a one-dimensional photonic crystal (1D-PC) to excite sharp Tamm/Fano resonances. By embedding a liquid crystal (LC) layer, the designed structure achieves dynamic tunability and enhanced sensitivity. Here, the transfer matrix method (TMM) represents the cornerstone towards the investigation of the optical properties of the designed sensor. In this regard, the numerical findings investigate the emergence of a coupled Tamm/Fano resonance at 1.1799 THz. Additionally, the emerged resonance provides a good sensitivity towards the temperature variations. However, to achieve the highest possible performance, all parameters that directly affect the proposed sensor have been optimized. Following the optimization process, the sensor achieved high temperature sensitivity of 0.002411 THz/oC (270.5 nm/oC) and detection limit of 0.728 °C through temperature changes from 15 °C to 50 °C. Therefore, we believe that the deigned temperature sensor could be of a promising interest through many applications including microelectronics, chip thermal management, microfluidics and biochemical tests.

We propose a reflectance-based terahertz (THz) metasurface biosensor that integrates tunable graphene components for highly sensitive, label-free peptide detection in biomedical applications. Through COMSOL Multiphysics simulations employing the finite element method, we demonstrate outstanding sensor performance, achieving a peak sensitivity of 0.279 THz/RIU, a figure of merit of 15.5 RIU-1, a quality factor above 50, and a detection limit of 0.048 RIU across the 0.1-0.45 THz frequency range. The sensor exhibits excellent angular stability, with reflectance increasing from 66.251% to 91.305% for incidence angles between 0 degrees and 80 degrees. By tuning graphene's chemical potential (0.1-0.9 eV), dynamic spectral control is achieved, enhancing reflectance from 14.947% to 70.919% - a performance surpassing that of conventional absorptance-based sensors. Parametric optimization reveals key geometric dependencies, identifying optimal resonator dimensions for maximum performance. Furthermore, machine learning - assisted optimization using Gradient Boosting Regression attains prediction accuracies above 90% for both refractive index variations and angular responses.

This work presents the computational design, numerical optimization, and machine learning-assisted performance evaluation of a multi-resonator terahertz (THz) metasurface biosensor intended for potential non-invasive glucose monitoring applications. Numerical simulations using COMSOL Multiphysics reveal that the sensor achieves a high sensitivity of 1000 GHz/RIU across a refractive index range of 1.335–1.347 RIU, with a quality factor exceeding 14 and figure of merit reaching 20 RIU−1. The design's versatility is demonstrated through parametric studies analysing the effects of graphene chemical potential, incident angle, and resonator dimensions on transmission spectra. For glucose detection applications, the sensor exhibits systematic frequency shifts from 0.719 THz to 0.714 THz corresponding to varying glucose concentrations, with a total tuning range of 30 GHz. One-dimensional convolutional neural networks further optimize detection accuracy, achieving up to 93

Infectious diseases and metabolic disorders remain major global health challenges, especially in areas with limited diagnostic infrastructure. Conventional diagnostic methods are often costly, complex, and restricted to detecting a single analyte. This study presents a multilayer surface plasmon resonance (SPR) biosensor composed of zinc oxide (ZnO), graphene, silver (Ag), and MXene layers for simultaneous, label-free detection of leptospirosis, malaria, and glucose imbalance. The combined material system improves plasmonic field confinement, increases surface electron density, and strengthens biomolecular interaction sensitivity. The sensor uses a Kretschmann configuration with p-polarized light excitation and is analyzed through the transfer matrix method. Systematic optimization of layer thickness and refractive index variation improves optical performance across several biomarkers. Numerical simulations show that the sensor achieves 1240 degrees/RIU sensitivity with a 1.5 degrees resonance shift for polyuria detection, 2 degrees angular tuning for oliguria, a 2.5 degrees resonance shift for glucose detection, and a 10 degrees shift with 271.43 degrees/RIU sensitivity for malaria biomarkers. Quality factors range from 19.76 to 24.03, and detection limits vary between 0.006 and 0.134 RIU across analytes. Regression-based machine learning analysis verifies the model's predictive accuracy, with R-2 values above 0.995 for refractive index variation.

Accurate amino acid detection is essential for biomedical diagnostics, clinical monitoring, and biochemical research; however, conventional analytical techniques are often constrained by complex sample preparation, high operational costs, and limited real-time capability. Terahertz (THz) metasurface sensors provide a promising label-free and non-ionizing alternative, yet their performance is frequently hindered by weak light–matter interaction and design trade-offs between sensitivity and fabrication feasibility. In this work, a hybrid THz metasurface sensor is proposed, integrating graphene, gold (Au), silver (Ag), copper (Cu), and tungsten disulfide (WS₂) within a hierarchical multi-resonator configuration comprising square, circular ring, and L-shaped resonators fabricated on a SiO₂ substrate. The proposed architecture exploits synergistic plasmonic–dielectric coupling and strong near-field confinement to enhance sensitivity to refractive index perturbations induced by amino acid analytes, while maintaining a geometrically simplified structure to ensure manufacturability. Numerical simulations demonstrate excellent sensing performance, achieving a maximum sensitivity of 1000 GHz/RIU, a peak figure of merit (FOM) of 50 RIU⁻1, and a strong linear relationship (R2 = 0.96243) between resonance frequency shift and analyte refractive index. Furthermore, machine learning (ML) models are employed to predict and optimize sensor behavior, yielding near-perfect accuracy (R2 > 0.9995) for variations in graphene chemical potential (0.1–0.9 eV) and circular resonator dimensions (5.5–7.5 µm). The proposed integration of hybrid materials, multi-resonator metasurface design, and ML-driven optimization effectively addresses key challenges in THz biosensing, enabling rapid, sensitive, and scalable amino acid detection for both point-of-care diagnostics and advanced biochemical research.

This paper presents a gas sensor based on an integrated metasurface design incorporating borophene, germanene, phosphorene, and graphene. The sensor leverages the unique properties of these two-dimensional materials arranged in a precisely engineered geometric configuration of circular and square ring resonators. Comprehensive simulation and optimization studies demonstrate that the sensor achieves a maximum sensitivity of 700 GHz/RIU, a figure of merit of 9.459 RIU-1, and a detection accuracy of 13.514. Meanwhile, the extensive optimization procedure regarding the sensor's performance was analyzed across varying parameters, including graphene chemical potential (0.1-0.9 eV), angle of incidence (0-80 degrees), and resonator dimensions. In addition, a machine learning approach using locally weighted linear regression was implemented to optimize the sensor response, achieving prediction accuracies up to 100% R-2. The proposed design offers a promising platform for real-time, highly sensitive toxic gas detection with potential applications in industrial safety and environmental monitoring.

Accurate temperature monitoring plays a critical role in many industrial applications, thereby fueling the demand for high-performance sensing technologies. Among the available approaches, optical sensing techniques have gained a significant attention due to their exceptional sensitivity to temperature variations. In this work, we propose a high-sensitivity temperature sensor based on Tamm plasmon (TP) resonance, realized within a one-dimensional photonic crystal (1D-PC) incorporating a thermally responsive liquid crystal (LC) layer. The proposed configuration follows the structure [prism/Ag/LC/(Ge/MgF2)N/air], where the integration of the Ag layer with the 1D-PC (Ge/MgF2)N forms a resonant cavity embedding the LC, that functions as the temperature-sensitive medium. The optical characteristics and key performance metrics including reflectance spectra, quality factor, sensitivity, and detection limit are systematically analyzed using the transfer matrix method (TMM). By optimizing critical design parameters such as layer thicknesses, incidence angle, and the number of photonic crystal bilayers (N), the sensor demonstrates an enhanced performance. The optimized structure achieves a temperature sensitivity of 5.3453 nm/degrees C over the temperature range of 15-50 degrees C, along with a low detection limit of 0.0224 degrees C. These results underscore the strong potential of the proposed sensor for high-precision optical temperature sensing in advanced photonic and optoelectronic applications.

This study presents a terahertz-based surface plasmon resonance (SPR) sensor developed for colorectal cancer detection. The device employs a distinctive multi-resonator design that integrates gold, silver, and graphene. Structurally, the sensor comprises an elliptical ring resonator coated with silver, surrounded by a gold-coated circular ring on a silicon dioxide substrate, while a graphene layer is incorporated to enhance sensing performance. Performance analysis was conducted using COMSOL Multiphysics simulations under varying conditions, including graphene chemical potential, incident angles, and resonator dimensions. The proposed sensor demonstrated a maximum sensitivity of 1100 GHz/RIU across a refractive index range of 1.329-1.348 RIU, achieving an optimal figure of merit of 17.460 RIU-1 at 0.719 THz. Additionally, a Random Forest Regression model was used to optimize sensor parameters, achieving up to 100 % accuracy in predicting sensor responses. The device also demonstrated potential as a 2-bit binary encoder, highlighting its versatility for both biosensing and data encoding applications.

A simple surface plasmon resonance (SPR) biosensor employing a hierarchical Ag-WS2-graphene heterostructure is presented for ultrasensitive quantification of Isoquercitrin, a therapeutically relevant flavonoid compound. The biosensor exhibits exceptional refractive index sensitivity spanning 158.333 degrees/RIU to 320 degrees/RIU across a refractive index range of 1.333-1.385, corresponding to distinct Isoquercitrin concentration gradients. Integration of machine learning algorithms, specifically employing a Gradient Boosting Regressor (GBR) framework, substantially augments predictive performance, yielding coefficient of determination (R2) values ranging from 0.89 to 1.00 for both structural parameter optimization and biomarker quantification tasks. The optimized sensing platform demonstrates superior analytical figures of merit, including a maximum figure of merit of 168.421 RIU-1, quality factor of 43.684, and detection limit of 3.0 & times; 10-3 RIU. Comparative benchmarking against stateof-the-art plasmonic biosensors establishes that the proposed Ag-WS2-graphene configuration attains unprecedented sensitivity (320 degrees/RIU), representing a significant advancement in label-free optical biosensing for flavonoid detection.

A graphene-integrated refractory metasurface absorber is proposed for broadband solar thermal energy conversion. Near-unity broadband absorptance across 300-2500 nm is achieved through three concurrent mechanisms: free-space impedance matching, ground-plane-mediated transmission suppression, and multimodal electromagnetic energy dissipation distributed across spectrally coupled plasmonic and dielectric resonant channels. Spectral tunability without structural reconfiguration is demonstrated through electrostatic modulation of the graphene Fermi level, which enables reversible control of the optical sheet conductivity. Alternative material configurations incorporating caesium, gallium arsenide, copper, and strontium are evaluated through full-wave COMSOL Multiphysics simulations under periodic boundary conditions, with assessment of spectral bandwidth, resonance behaviour, and thermal robustness at elevated temperatures. A dielectric substrate thickness of approximately 4.1 μm satisfies the quarter-wavelength Fabry-Perot cavity resonance condition, suppressing mid-infrared radiative emission and reducing parasitic thermal losses. A random forest regression surrogate model trained on 1,200 finite-element simulation samples, with five geometric and material input parameters and 500 estimators, maps the design space with R2 > 0.90 across most parameter configurations. Accuracy decreases to R2 approximately 0.63 near normal incidence, where overlapping resonances increase spectral complexity. The optimised configuration achieves a peak absorptance of 99.99% and a broadband solar-weighted average of 98.6%.

This study presents a gas sensor employing a multi-resonator architecture composed of gold, MXene, copper, and graphene layers deposited on a silicon dioxide substrate. The device incorporates concentric circular and square ring resonator geometries designed to operate across the 0.1-1 THz frequency range. Finite element analysis using COMSOL Multiphysics demonstrates significant tunability through modulation of graphene chemical potential (0.1-0.9 eV) and incident angle variation (0 degrees-80 degrees). The sensor exhibits a refractive index sensitivity of 300 GHzRIU-1, a quality factor of 13.143, and a figure of merit of 4.762 RIU-1. Integration of machine learning algorithms, specifically polynomial regression models, facilitates enhanced performance prediction with coefficient of determination (R2) values approaching unity. The proposed sensor architecture is compatible with standard photolithographic fabrication processes, demonstrates resilience to environmental perturbations, and presents a viable solution for portable gas detection systems. Compared to previously reported designs, this multi-material configuration offers enhanced practicality and detection precision for industrial sensing applications.