Underwater optical wireless communication (UOWC) enables high-throughput, low-latency links. However, performance is strongly constrained by wavelength-dependent absorption and scattering in seawater, especially for the non-line-of-sight (NLOS) geometries that arise due to misalignment and platform mobility. This work develops a framework to optimise the wavelength for NLOS UOWC across Jerlov water types – I, III and 9C in the 400–700 nm band. The model employs a Lambertian transmitter, direct and scattered components with Beer–Lambert attenuation, wavelength-dependent silicon photodetector responsivity, and a receiver noise stack (shot and thermal). The electrical SNR, BER (OOK) and channel capacity are investigated. Because the idealised OOK expression can return physically unrealistic raw magnitudes at high SNR, BER is reported against a practical floor of 10−12 throughout (values below this floor are reported as “<10−12” rather than as raw numbers), and results for the most turbid water type (Jerlov 9C) are explicitly flagged as indicative trends rather than quantitative predictions, consistent with the single-scattering approximation underlying the model. Model-based results indicate that the optimum wavelength shifts with turbidity: clear ocean favours the blue–green window (520–570 nm, peak near 564 nm for a silicon photodetector), whereas coastal and turbid waters shift the optimum towards the red band (650–700 nm). This red-shift arises from a combined electro-optical trade-off among absorption, scattering-induced angular spreading, and photodetector responsivity, not from optical attenuation alone. Furthermore, simulation results indicate that distance sensitivity undergoes a gradual change beyond 13 m, rendering NLOS links noise-limited at longer ranges. These model-derived findings offer wavelength-selection and link-budgeting guidance for UOWC system design.
Carbon dots (CDs) have emerged as promising luminescent nanothermometers for optical thermometry owing to their tunable optical properties, low toxicity, environmental compatibility, and structural versatility. However, the intrinsic complexity of their emission mechanisms and the multidimensional nature of synthesis–structure–property relationships continue to hinder the rational development of high-performance temperature-sensing platforms. This review examines the rapidly evolving intersection of CD thermometry and machine learning (ML), with emphasis on how data-driven methodologies are transforming both materials design and thermal-signal interpretation. The fundamental photophysical origins of temperature-dependent luminescence are first analyzed, focusing on the interplay between structural heterogeneity, excited-state dynamics, and thermometric information encoding. Subsequently, machine-learning frameworks relevant to optical sensor development are discussed from the perspectives of descriptor engineering, predictive modeling, uncertainty assessment, and adaptive design. By synthesizing recent advances in ML-assisted CD research, this review identifies emerging design principles governing optical-property prediction, luminescence optimization, and multidimensional temperature sensing. Particular attention is given to the transition from empirical optimization toward predictive and increasingly autonomous thermometric systems. The analysis further highlights unresolved challenges related to mechanistic ambiguity, model transferability, reproducibility, standardization, and real-world deployment. Finally, a future roadmap is proposed in which physically informed ML, integrated data infrastructures, and closed-loop discovery frameworks converge to enable the next generation of intelligent CD thermometric technologies.
This paper presents the design of a plasmonic memory based on Metal-Insulator-Metal (MIM) input and output waveguides and Ge2Sb2Te5 (GST) phase-change material. In this design, silver (Ag) is used as the metal, and air along with the phase-change material serve as the dielectrics. The results are reported using the Finite-Difference Time-Domain (FDTD) method. To achieve a memory structure with optimized dimensions and desired performance, a plasmonic filter has been designed. The dielectric function of the metal is characterized using the Drude theoretical model and the Johnson and Christy experimental model. The filter's transmission curve exhibits a Gaussian mode with a transmittance of 91% at the resonance wavelength of 1210 nm; the consistency between the results of the theoretical and experimental models is demonstrated in this design. Several factors were investigated and reported to assess optimal performance in the designed structure. To realize the proposed memory structure, GST material and a probe signal are utilized. This memory is programmable from above the chip and performs the reading operation of the stored state by applying a probe signal to the structure. At the resonance wavelength, this memory features a contrast of 95% and an extinction ratio of ER = 25 dB for detecting and differentiating between various states, Additionally, the Insertion Loss (IL) reading bit ‘1’ is 0.19 dB and bit ‘0’ is 25.2 dB, providingFootprint=0.095μm2 and 10.5 bits/μm2integration density. Temporal analysis of the logic state read operation is performed under excitation by a pulse with a square temporal profile distribution, and the system response is presented in terms of the normalized power |E|2 as a function of time (femtoseconds). This memory cell is designed for robust binary logic operations, offering high-contrast switching for non-volatile storage in photonic integrated circuits (PICs). Furthermore, the proposed architecture serves as a foundational platform for neuromorphic integration in optical artificial neural networks (OANNs), where the multi-level crystallization potential of the phase-change material can be leveraged for future analog synaptic weight modulation.
Chikungunya virus (CHIKV) remains a major mosquito-borne disease requiring rapid and label-free diagnostic technologies for early detection This study presents a terahertz (THz) metasurface biosensor comprising tungsten disulfide (WS₂) square resonators, MXene concentric rings, gold (Au) circular resonators, copper (Cu) structures, and a gate-tunable monolayer graphene layer on a SiO₂ substrate for numerical investigation of refractive-index sensing using values representative of healthy and CHIKV-related plasma and platelet samples. Finite element simulations in COMSOL Multiphysics were performed to optimize the graphene chemical potential, incident angle, circular resonator radius, and square resonator dimensions. The optimized sensor exhibits a resonance frequency of 0.355 THz with strong electric-field confinement, achieving a maximum numerically predicted refractive-index sensitivity of 907 GHz/RIU over the representative refractive-index range of 1.33–1.39 RIU. The device maintains stable spectral performance for incident angles from 0° to 80°. XGBoost regression models accurately predicted the transmission response for varying structural parameters, achieving R2 > 0.997 with low prediction errors. The present work represents a computational proof-of-concept demonstrating the feasibility of a THz metasurface for refractive-index sensing relevant to CHIKV detection. Experimental validation through device fabrication, biofunctionalization with CHIKV-specific recognition elements, and testing using clinical samples will be pursued in future studies to establish practical diagnostic performance.
Wireless communication systems, particularly free-space optical (FSO) links, are highly susceptible to adverse weather conditions such as fog, dust, rain, and snow. These conditions severely degrade link quality through signal attenuation, scattering, and reduced visibility. To address these issues, this study proposes a weather-aware adaptive communication framework that integrates deep learning-based perception with transmission control. Several state-of-the-art computer vision architectures, including Convolutional Neural Networks (CNNs), ConvNeXt, and Vision Transformers (ViT), are trained on a labeled outdoor weather dataset to enable robust multi-class classification under diverse visibility conditions. The classification outputs are then employed by an adaptive power control algorithm that dynamically adjusts the transmitted optical power based on the predicted weather class.Experimental results demonstrate that the DeiT-Base model achieves the best performance, attaining a Top-1 accuracy of 97.4%, a balanced accuracy of 97.5%, a Macro-F1 score of 0.976, and a macro AUROC of 0.999. Furthermore, simulation results indicate that the proposed adaptive framework improves communication reliability while enhancing SNR and energy efficiency compared with a fixed-power transmission strategy These findings demonstrate the potential of integrating deep learning-driven environmental awareness with adaptive transmission strategies for reliable and energy-efficient weather-resilient FSO systems.
In this study, a compact and programmable N × N photonic integrated circuit based on silicon bimodal waveguides decorated with periodic air-cavity perturbations is introduced. The proposed structure enables highly efficient routing and interference-based optical switching via thermo-optic phase modulation, and also performs neuromorphic computations with high accuracy. In each 2 × 2-unit cell, the simultaneous propagation of fundamental (TE0) and first-order (TE1) modes is supported, where the embedded air-cavities enhance the inter-mode coupling and slow-light behavior. At an operating wavelength of 1621 nm, the unit cell exhibits an extinction ratio of 26.18 dB and an insertion loss of 1.63 dB, which is achieved through thermos-optic modulation that induces a refractive-index change of Δn=0.0137 and creates a phase difference of π between the modes. Five such unit cells are cascaded to realize a fully programmable 4 × 4 photonic circuit controlled by five discrete control signals. By employing the proposed structure and training on the data generated from this photonic circuit, a feedforward mapping neural network based on a fully connected architecture is obtained. This neural network calculates the (4 × 4) transfer matrix from the (1 × 5) weights given to each unit cell of the neural network. The neural network achieves an accuracy of more than 98%. These results indicate that air-cavity-based bimodal waveguides offer a scalable, low-power, and high-performance platform for programmable optical computing and neuromorphic photonic systems.
This work presents a numerical study of non-concentric SiO2@Ag core-shell nanoparticles as refractive-index sensors, using the MNPBEM toolbox. We systematically vary the outer nanoparticle diameter, Ag-shell thickness, and normalized silica-core eccentricity to build performance maps of extinction, resonance wavelength, sensitivity, linewidth, and figure of merit (FOM). The nanoparticle is modeled as a single isolated particle suspended in a homogeneous nondispersive surrounding medium. The refractive index of the medium was varied uniformly from 1.0 to 2.0. The controlled core offset excites mixed bright-dark plasmon modes, yielding narrow Fano-like resonances that improve sensing performance. Large-core (thin-shell) nanoparticles with large position offsets show strong hybridization and strongly increase the local field, characterized by high sensitivities of 250–420 nm/RIU, with very narrow linewidths, providing high sensing performance (FOM > 80 RIU−1). The maximum FOM is reached within a design window, rather than at the largest eccentricity or the thinnest shell. This observation underlines the importance of balancing field exposure, radiative damping, mode hybridization, and extinction amplitude simultaneously. We provide quantitative, particle-level design guidance for suspended- or colloidal-based localized surface plasmon resonance sensing platforms.
The wavefront tilt parameter a plays a critical role in the formulation of the short-exposure optical transfer function for imaging through atmospheric turbulence. In this work, we present a systematic and comprehensive numerical investigation of a using split-step wave optics simulations under two commonly used tilt-removal definitions, namely Z-tilt and G-tilt. The dependence of a on turbulence strength and aperture size is quantified over a wide range of conditions spanning weak to strong turbulence regimes. The results show that, under Z-tilt removal, a exhibits weak dependence on turbulence strength and is primarily determined by aperture size, consistent with classical short-exposure theory. In contrast, under G-tilt removal, a depends strongly on both aperture size and turbulence strength, exhibiting distinct piecewise behavior, particularly in the small-aperture regime. Empirical expressions are developed for both cases to enable efficient estimation of a in practical applications. These findings provide a unified interpretation of discrepancies reported in previous studies and highlight the importance of tilt definition in modeling turbulence-degraded imaging systems.
In this study, we investigate the thermal effects on the photonic band gaps of a two-dimensional SiO₂-Si hexagonal photonic crystal composed of a silicon dioxide (SiO₂) substrate with an array of silicon-filled holes. The photonic band structures of the transverse electric (TE) mode are calculated using the plane wave expansion method, and the thermal effects on the photonic band gaps are analyzed. Two temperature-dependent refractive-index models, namely the Ghosh model and the Sellmeier equation, are used to calculate the refractive indices of silicon dioxide. In addition, a temperature-dependent refractive-index function for silicon reported by H. H. Li is adopted at the target telecommunication wavelength of 1.55 μm, without considering the absorption coefficient. Using these experimentally based material parameters, well-converged numerical results are obtained. Eight photonic band gaps are initially identified below the normalized frequency ωa/2πc=1.1 over the temperature range from 200 K to 600 K. However, the sixth gap remains close to zero width and is excluded from the quantitative discussion because it is extremely narrow and may be affected by numerical convergence. Therefore, the remaining seven reliable photonic band gaps are analyzed in detail. The results show that lower-order band gaps exhibit relatively weak temperature dependence, whereas some higher-order band gaps show more pronounced thermal variation. These numerical results provide useful theoretical guidance for the development and optimization of future silicon photonic devices, such as waveguides and optical filters.
This paper investigates transmission optimization in photonic crystal T-junction waveguides using defect engineering. Six modified junction structures are analyzed using the finite-difference time-domain (FDTD) method. The optical transmission characteristics are evaluated through normalized transmission spectra, and several quantitative performance metrics including insertion loss, transmission enhancement factor, effective transmission bandwidth, and average transmission are introduced for systematic comparison. The results show that the optimized configuration significantly improves the transmission efficiency of the junction. In particular, the peak transmission increases from approximately 0.15 in the reference structure to about 0.42 in the optimized design, corresponding to a reduction of insertion loss from 8.24 dB to 3.77 dB. In addition, the optimized structure exhibits a broader transmission bandwidth and higher average transmission across the operating frequency range. Field distribution analysis reveals that the improved performance originates from enhanced mode matching and reduced scattering at the junction interface. These results demonstrate that defect engineering provides an effective approach for improving power transmission in photonic crystal waveguide junctions and may be useful for integrated photonic circuits.
The early and accurate detection of cancer cells still represents a significant challenge due to the invasive nature, high costs, and time-consuming procedures of traditional diagnosis methods. In this study, the numerical design and optimization of an ultra-compact D-shaped optical fiber SPR biosensor for label-free detection of cancer cells have been presented. The suggested sensor consists of the side-polished D-shaped optical fiber with a thin layer of gold plasmonic coating to improve the evanescent field-metal–dielectric interface interaction. The simulation of the suggested biosensor was done using the Finite Element Method (FEM) in COMSOL Multiphysics 6.0 based on Drude dispersion model for gold and Sellmeier dispersion model for silica optical fibers. Parametric optimization was carried out to achieve the maximum plasmonic coupling by changing groove dimensions and gold plasmonic layer thickness. Wavelength sensitivity was found according to the phase matching condition of the core guided mode and SPP mode which corresponds to the specific confinement loss peak. The resulting optimized sensor had a maximum wavelength sensitivity of 3571 nm/RIU and a figure of merit greater than 8.4. Numerical analysis shows successful classification of various cancer cell types, such as HeLa, MDA-MB-231, Jurkat, and PC-12 cells, within the refractive index range of 1.33 to 1.42 RIU. The suggested sensor has great application prospects due to its simple geometry and sensing characteristics.
Considering that microRNAs (miRNAs) play a vital role in regulating genes and are essential for detecting illnesses and ensuring treatment efficacy, accurate medical, biological, and veterinary diagnostics critically necessitate miRNA identification, which also aligns with the third United Nations Sustainable Development Goal (SDG) for good health and well-being. However, conventional techniques, including reverse transcription quantitative polymerase chain reaction (RT-qPCR), microarrays, and next-generation sequencing (NGS), are costly, complex, and do not provide real-time detection capacities, rendering the urgent demand for advanced biosensing technologies, such as functionalised optical fibre (OF) sensors, capable of miniaturisation, real-time object monitoring, and recognition of unlabelled objects. These innovative biosensors would also enable the precise tracking of miRNA in models of living creatures for diagnosing diseases, eco-monitoring, and veterinary medicine applications. Molecular biosensing is a primary advancement within the biosensing field, offering disease detection efficiency and selectivity enhancements. Although OF biosensors demonstrate substantial potential, they exhibit instability, functionality, and clinical application issues. Knowledge about their performance, limitations, and scaling potentials is also scarce due to limited reports that critically evaluated the usability of fibre biosensors in miRNA identification and comparative studies on the sensors against existing and new miRNA detection methods. Persistent repeatability, protocol compliance, and artificial intelligence (AI) integration-related concerns have also inhibited the practical applications of OF biosensors. Functionalised OF biosensors should only be employed in clinical settings after addressing their stability, reusability, and regulatory limitations. This study aimed to fill these knowledge gaps by determining the utility of functionalised OF sensors for miRNA detection. Particularly, the objectives of this study were to establish the operational mechanisms, compare the efficacy to other existing methods, explore other fabrication methods, address scalability and clinical usage issues of the biosensors, and identify future research directions. The primary findings were then linked to practical applications via a comparative analysis of numerous OF biosensors, and solutions to existing hindrances were suggested. This study would contribute to the body of knowledge on biosensing technology, particularly in enabling the rapid, accurate, and affordable detection of miRNAs. This study also addressed the existing gaps within this field and contributed to future possibilities for long-term healthcare and innovative diagnostics advancements.
A plasmon-enhanced U-bent optical fiber sensor was developed for ammonia detection at ambient temperature, utilising an Ag/ZIF-8 hybrid sensing layer. The sensor was fabricated by attaching silver nanoparticles to a functionalized optical fiber surface, followed by in situ growth of a porous ZIF-8 layer. ZIF-8 served as the gas recognition layer owing to its microporous structure and capacity to adsorb ammonia, while silver nanoparticles amplified the signal via localized surface plasmon resonance near the excitation wavelength of 433 nm. The sensing layer was characterized using FESEM/EDS, XRD, Raman spectroscopy, UV–Vis spectroscopy, and argon adsorption–desorption analysis to confirm its morphology, elemental composition, crystalline structure, optical properties, and porosity. Initial testing with ammonia, acetone, and isopropyl alcohol indicated that the ZIF-8-coated probe responded most strongly to ammonia. Subsequently, single-wavelength ammonia sensing was conducted at 433 nm using both ZIF-8- and Ag/ZIF-8-coated probes. The Ag/ZIF-8 sensor exhibited a significantly greater response, with approximately a 600-count change compared to ~70 counts for the ZIF-8-only probe. It demonstrated approximately 16.9% response at 100 ppm ammonia, with response and recovery times of 15 s and 21 s, respectively. The sensor maintained satisfactory short-term stability, displayed selective response to ammonia over organic vapours tested, and functioned effectively under varying humidity conditions. These results substantiate that the integration of silver nanoparticles with ZIF-8 enhances the efficacy of optical fiber-based ammonia sensors operating at room temperature.
We present hybrid integration of substrate-free, miniaturized spectral thin-film interference filters into a modular optical micro-platform, together with photodiodes, polymer waveguides and coupled to single-mode fiber. All components have edge lengths of few hundreds of micrometers and are assembled on a 6 mm × 16 mm fused silica platform by an automatable precision optics assembly system. The spectral filter is only 16.7 μm thick and it is actively tuned by rotation to meet the desired spectral performance during its assembly. The demonstrated platform functions as a balanced wavelength detector by integration of a long-pass filter with simultaneous on-chip measurement of its transmission and reflection by bare die photodiodes. Tunability of the long-pass filter edge by more than 16 nm in the wavelength range of 968 nm to 984 nm during the assembly was demonstrated. No collimation or other lenses are required on the platform, which is enabled by the implemented technology of substrate-free thin-film filters. We provide a simple numerical approach to analyze the spectral effects due to angle of incidence distribution from beam divergence when combining a thin-film filter with single-mode fiber without collimating lenses. Our spectral measurements of the integrated long-pass filters' transmission and reflection, performed directly on the optical micro-platform, show good agreement with our numerical modeling.
Vital signs such as heart rate (HR) and respiratory rate (RR) serve as a baseline indicator for monitoring cardiac and respiratory activities. Wearable sensor systems that offer real-time, unobtrusive solutions for cardiorespiratory monitoring are becoming important in personalized healthcare settings. In this context, Fiber Bragg gratings (FBGs) have gained attention due to their high sensitivity and favorable metrological properties. In particular, FBGs are encapsulated in suitable materials to expand their application scope in clinical settings. In this work, we propose a Fiber Bragg grating (FBG)-based finger plethysmography sensor for the simultaneous measurement of the cardiopulmonary parameters. The sensor is encapsulated inside a biocompatible silicone matrix due to its favorable features assisting wearability. A circular geometry is chosen for the sensor design to ensure conformability at the fingertip, allowing adaptability across individuals with varying anatomical features. At first, the influence of encapsulation and sensor design on the sensor's metrological performance is experimentally evaluated. Further, an adhesive tape is used to secure the sensor to the fingertip, and experimental trials are conducted to determine optimal measurement conditions. Subsequently, experiment trials are carried out on healthy volunteers to assess the efficacy and suitability of the sensor for the monitoring of HR and RR through finger plethysmography. The findings demonstrate the usefulness of the proposed optical sensor as a wearable system for continuous monitoring of vital signs through arterial pulse waveform analysis.
This work presents the design and numerical analysis of a compact two-dimensional photonic temperature sensor based on a cross-defect photonic crystal ring resonator (CD-PCRR). The sensor is formed in a square lattice of silicon rods in air and operates within the transverse-electric (TE) photonic bandgap. A symmetry-engineered cross-shaped defect is introduced into the ring resonator to enhance optical-field confinement and increase the interaction of the resonant mode with the thermo-optically active silicon region. The resonator is side-coupled to bus and drop waveguides, providing a distinct channel-drop response and high spectral selectivity. Full-wave finite-difference time-domain (FDTD) simulations reveal strong field localization inside the defect cavity and a narrow resonance suitable for wavelength-based temperature interrogation. Temperature sensing is achieved through the thermo-optic response of silicon, which shifts the resonant wavelength as the refractive index changes. Over the temperature range of 0–68 °C, the resonance shifts monotonically from 1638.1 to 1663.3 nm. Linear regression of the simulated data yields a sensitivity of 370.6 pm/°C with excellent linearity (R2 = 1.00). The optimized resonator also achieves a quality factor of up to 573, indicating strong optical confinement and good spectral selectivity. With a footprint of approximately 356 μm2, a simple resonator geometry, and a silicon-based material platform, the proposed CD-PCRR offers a promising approach to compact, high-resolution temperature sensing. The results support its potential for integrated thermal monitoring and other refractive-index-based photonic sensing applications.
The chalcone derivative, 4-[(E)-3-(4-methylphenyl)-3-oxoprop-1-en-1-yl] benzonitrile (MPOB), was synthesized and its spectroscopic and nonlinear optical (NLO) properties were investigated using experimental and density functional theory (DFT) methods. The crystalline nature was confirmed by powder X-ray diffraction analysis. Vibrational and UV–Vis spectral studies showed good agreement with theoretical results. The electronic properties, including frontier molecular orbitals and charge transfer interactions, were analyzed using TD-DFT and NBO approaches. The calculated hyperpolarizability and Kurtz–Perry measurements reveal significant NLO activity. Molecular electrostatic potential and topological analysis highlight the presence of reactive sites and noncovalent interactions. Thermal stability and laser damage threshold further support the applicability of the material. Overall, the results indicate that MPOB is a promising candidate for nonlinear optical applications.
The transition of vanadium oxide from an insulating phase to a metallic phase at a temperature of approximately 340 K is very interesting for a wide variety of photonic and optoelectronic applications. In this work, we study a gold/vanadium oxide bilayer, in which a pulsed laser source excites the metal. Using a three-temperature model of gold electrons, the gold lattice and the vanadium oxide lattice, we estimate the heating of the oxide that would induce its phase transition. The optical properties of the bilayer are studied using the transfer matrix method, highlighting the different light transmission with and without laser excitation. In this work, VO2 excitation is mediated indirectly by Au heating and quantitatively modeled. The study introduces a unified framework combining a three-temperature model with transfer matrix calculations to describe indirect ultrafast thermal activation of VO2 via gold.
Digital Image Correlation (DIC) serves as a non-contact optical metrology technique for quantifying object deformation, with widespread deployment in industrial inspection and intelligent manufacturing. Conventional DIC approaches commonly exploit the spatial–temporal continuity of object deformation to boost measurement precision and robustness. While existing deep learning-based DIC methods have effectively leveraged spatial continuity, delivering marked improvements in accuracy, efficiency, and anti-interference capability, they remain limited by underutilization of temporal continuity. Specifically, current DIC networks only take a reference image and a deformed image as input, disregarding historical deformation sequences. To overcome this constraint, this work presents Temporal-DICnet, a sequential-image-input network that incorporates temporal information for multi-frame deformation field estimation. The proposed framework comprises three core modules: a feature encoder that extracts image features and constructs 4D correlation cost volumes between the reference and each deformed frame; a motion encoder that enables inter-frame motion feature propagation and fusion; and a deformation prediction module with convolutional residual blocks used to refine the output deformation field. Experimental results on synthetic datasets reveal that Temporal-DICnet reduces the mean absolute error (MAE) by more than 50% relative to the ALDIC method. Physical uniaxial tensile tests further validate the accuracy and generalization of the proposed network in real-world deformation measurement scenarios.
Lightweight alkaline-earth metal dihydrides, XH₂ (X = Be, Mg, Ca, Sr, and Ba), were systematically investigated using density functional theory (DFT) calculations implemented in Materials Studio and WIEN2k to examine their structural, mechanical, electronic, optical, and thermodynamic properties. This work provides a unified comparative framework across the complete XH₂ series to establish composition-dependent structure–property relationships relevant to optoelectronic and hydrogen-related materials. The optimized lattice parameters increase progressively from BeH₂ to BaH₂, whereas gravimetric hydrogen-storage capacity decreases from 18.2 wt% to 2.6 wt%. Although lighter hydrides possess higher hydrogen content, their stronger thermodynamic stability suggests restricted practical hydrogen-release behavior. All investigated compounds satisfy Born stability criteria and exhibit mechanically stable behavior dominated by ionic metal–hydrogen interactions. Electronic band-structure and density-of-states analyses reveal semiconducting characteristics with progressively reduced band gaps toward heavier cations and increased electronic-state density near the Fermi level. Optical properties, including dielectric response, refractive index, absorption coefficient, reflectivity, optical conductivity, and energy-loss spectra, display pronounced composition-dependent behavior governed by interband electronic transitions. Strong optical absorption and tunable dielectric characteristics, particularly in the ultraviolet and high-energy regions, highlight the comparative optoelectronic relevance of these hydrides. Thermodynamic analysis indicates decreasing lattice vibrational activity with increasing atomic mass. While GGA-PBE calculations do not explicitly address kinetic or diffusion effects, the present results establish a consistent comparative platform for understanding bonding, electronic structure, and optical response across alkaline-earth hydrides and support their screening for UV optoelectronic and hydrogen-related material applications.
