Search Results (235)

One of the strongest electromagnetic engineering approaches for enhancing antenna performance is the use of photonic crystal (PhC) substrates. This technique can be efficiently applied to antenna design and offers notable advantages, such as gain improvement, increased bandwidth, and frequency-dependent beam scanning. In this paper, a bow-tie dipole antenna has been developed for terahertz operation over the 0.39–1.3 THz band, presenting a novel structure capable of producing strong ultra-wideband (UWB) field enhancement within its feed gap. The feed gap between the two metallic arms has a slot width of 1.24 λ0 (λ0 is the wavelength in free space at a center range of 0.8 THz), which facilitates the generation of an enhanced electric field. The PhC substrate enables surface-wave control through dispersion engineering, thereby enhancing the radiation efficiency of the antenna. The proposed antenna exhibits a radiation efficiency of approximately 73–93% over the entire UWB frequency band. Furthermore, the PhC substrate antenna achieves a maximum gain of 21 dB, exceeding that of a homogeneous-substrate THz bow-tie antenna by at least 3.3 dB. The results indicate that the antenna achieves |S₁₁| < −10 dB impedance matching over the bandwidth of 105.9%, ranging from 0.4 to 1.3 THz. The proposed bow-tie dipole antenna integrated with a PhC substrate demonstrates a wide beam-scanning capability from −54° to +74° across the 0.39–1.16 THz band, while maintaining a compact footprint of 14.9 λ₀ × 22.4 λ₀. This combination of wide scanning, broad bandwidth, and ultra-low profile represents a notable advancement in the development of compact THz radiating structures. Full article

In this work, a comprehensive first-principles investigation of the electronic, magnetic, and optical properties of pristine and Fe-, Co-, and Ni-doped MoS₂ monolayers is presented within the framework of density functional theory. Substitutional transition-metal doping at the Mo site is shown to induce spin-polarized impurity states within the pristine band gap, leading to significant modifications of the electronic structure, including metallic, semimetallic, or half-metallic behavior depending on the dopant species. The calculated spin-resolved band structures and projected density of states reveal a strong hybridization between the dopant $3d$ orbitals and the Mo-$4d$/S-$3p$ states, giving rise to sizable magnetic moments and dopant-dependent exchange splitting. When spin–orbit coupling is included, the combined effect of exchange interactions and relativistic effects leads to an effective valley splitting at the K and $K^{\prime }$ points, whose magnitude and sign depend sensitively on the chemical nature of the dopant. Optical properties are analyzed within a linear-response framework, showing pronounced dopant-induced modifications of the optical spectra. While the pristine monolayer exhibits well-defined excitonic features, transition-metal substitution introduces low-energy optical transitions associated with impurity-related states. Consequently, the exciton binding energies estimated from the difference between the electronic and optical gaps are interpreted as effective measures of dopant-induced perturbations to optical transitions, rather than as quantitative many-body excitonic binding energies in the strict sense. These results provide microscopic insight into the interplay between magnetism, spin–orbit coupling, and optical response in doped MoS₂ monolayers, highlighting the potential of transition-metal substitution as a route to engineer spin- and valley-dependent phenomena in two-dimensional materials. Full article

The 100 GHz-class ultrafast photonic integrated circuit (PIC) positions itself as a promising technology in the post-Moore era, when the bandwidth limit of metallic interconnections constrains current electronic integrated circuits. Nevertheless, the lack of an effective on-chip, CMOS-compatible laser source challenges the ongoing development of PIC. Germanium straintronics facilitate bandgap transformation from indirect to direct, thereby enabling effective band-to-band radiative recombination. Some parameters, such as nanowire diameters or crystalline orientation and strain direction, have a profound effect on the bandgap transformation of Ge nanowires. In this review, we will discuss changes in the fundamental physical properties of Ge nanowires under strain, including mechanical, electronic, optical, and thermal properties. Subsequently, we summarize common methods for strain engineering, as well as novel approaches that have emerged in recent years. Some notable application cases reported in the last few decades will be discussed in detail. This review may fill knowledge gaps and provide a solid background for forthcoming investigations of on-chip strained Ge lasers. Full article

Zinc titanate (ZnTiO₃) is a chemically stable and non-toxic oxide perovskite whose photovoltaic potential remains largely unexplored due to its wide indirect bandgap. This study evaluates whether oxygen-vacancy (F-center) engineering can tailor its electronic structure and improve its suitability as a photovoltaic absorber. Density Functional Theory (DFT) calculations using VASP (PAW − GGA/PBE + U) were performed to evaluate structural stability, electronic properties, and electron affinity, while optical absorption was modeled through a combined Tauc–Gaussian approach. Device performance was assessed via SCAPS-1D simulations in an FTO/ZnO/ZnTiO₃/Spiro-OMeTAD architecture. Oxygen vacancies induce bandgap narrowing from ~2.96 eV to ~1.47 eV and generate Ti-3d-dominated donor-like and deep intragap states. The calculated electron affinity is ~3.77 eV. Simulated single-layer devices reach Voc ≈ 1.11 V, Jsc ≈ 8.27 mA·cm⁻², FF ≈ 83%, and a maximum efficiency of ~7.65%, primarily limited by moderate absorption strength and defect-assisted recombination. Multilayer configurations indicate that geometric optimization can significantly enhance projected efficiency, approaching 19.25% under idealized conditions. Although vacancy engineering extends visible-light absorption, the intrinsic indirect band-gap character constrains the ultimate photovoltaic performance of ZnTiO₃. Full article

α-Fe_2−4xZ_3xO₃ (Z = Ce, Ru) nanoparticles were synthesized via a conventional solid-state reaction route. X-Ray diffraction analysis confirmed that all compositions crystallize in the single-phase hexagonal hematite (α-Fe₂O₃) structure, with no detectable secondary phases. Cerium substitution resulted in a pronounced reduction in crystallite size accompanied by a progressive narrowing of the optical band gap, which decreased to approximately 1.73 eV at higher Ce contents. The optical properties were further investigated through absorption coefficient, optical transmittance, and complex refractive index analyses, revealing that cerium-doped hematite exhibits enhanced light-harvesting capability, highlighting its strong potential for optoelectronic and solar-energy conversion applications. Magnetic hysteresis measurements on α-Fe_2−4xRu_3xO₃ samples showed a systematic increase in both coercive field (H_c) and remanent magnetization (M_r) with increasing Ru concentration. This magnetic hardening behavior is attributed to strengthened magnetocrystalline and shape anisotropy induced by Ru incorporation into the hematite lattice. Mössbauer spectroscopy confirmed the presence of Fe³⁺ and Ru⁴⁺ species, providing valuable insight into the oxidation states and local magnetic environments within the corundum-type structure. Full article

ZnO and ZnO:5%B nanoparticles produced by sol–gel synthesis exhibit a single-phase wurtzite structure. X-ray diffraction (XRD) investigation reveals crystallite sizes in the range of $32.37–39.63$ nm and microstrain values on the order of $(1.98–8.03)\times {10}^{-4}$, despite the Uniform Stress Deformation Model (USDM) indicating the presence of considerable tensile stress. Significant band-tail states are introduced via boron doping, resulting in Urbach energies ranging from $110$ to $193$ meV and a narrowed optical band gap of $3.216$ eV. With a refractive index range of $2.05–2.71$, the material exhibits tunable optical characteristics. Violet and blue emissions originating predominantly from zinc interstitials (Znᵢ) and zinc vacancies (V_Zn) dominate the photoluminescence spectra, while oxygen interstitial-related contributions remain relatively weak. A high spin density is confirmed by electron spin resonance measurements, which reveal a strong defect-related signal at $g\approx 2.294$. The formation of Znᵢ/V_Zn defect centers due to charge compensation and ionic size mismatch induced by B³⁺ substitution for Zn²⁺ significantly modifies the band-edge states and optical constants. These defect-engineered properties render the material promising for applications in ultraviolet (UV) photodetectors, transparent conducting oxides, and electron transport layers in organic photovoltaic devices. Full article

Bismuth-based semiconductors have emerged as a promising class of visible-light-responsive photo(electro)catalysts for environmental remediation owing to their tunable electronic structures, moderate band gaps, and relatively low toxicity. The stereochemically active Bi³⁺ 6s² lone pair and strong Bi–O orbital hybridization tailor valence-band states, enabling enhanced utilization of the solar spectrum and favorable charge-carrier dynamics. In addition, layered, perovskite-like, and aurivillius-type crystal frameworks generate internal electric fields that are advantageous for photoelectrochemical (PEC) operation. This review critically examines advances from 2015 to 2025 in the design, synthesis, modification, and environmental applications of bismuth-based photo(electro)catalysts, with particular emphasis on PEC systems for pollutant degradation. Major material families, including bismuth oxides, oxyhalides, oxychalcogenides, chalcogenides, perovskite-like oxides, and complex metal oxides, are discussed in relation to their structure–property–performance relationships. Key synthesis strategies, such as solid-state, sol–gel, hydro/solvothermal, microwave-assisted, spray pyrolysis, and electrodeposition methods, are compared with respect to morphology control, defect chemistry, and electrode integration. Performance-enhancing approaches, including elemental doping, oxygen-vacancy engineering, and the rational design of type-II, p–n, Z-scheme, and S-scheme heterojunctions, are critically assessed. Practical considerations related to stability, scalability, and techno-economic constraints are highlighted. Finally, current challenges and future directions toward durable and application-ready bismuth-based PEC technologies are outlined. Full article

Machine learning (ML) methods have been widely explored for predicting material properties. However, due to the rapid development of ML techniques and the diversity of available models, performance comparisons between traditional and graph-based machine learning models remain limited. Therefore, we evaluate 11 conventional ML models alongside the graph neural network-based Crystal Graph Convolutional Neural Network (CGCNN) for predicting three key properties—formation energy (E_f), band gap (E_g), and energy above hull (E_h)—across a dataset comprising single perovskites, double perovskites, and their combined structures. The results demonstrate that for single perovskites, CGCNN exhibits gains of over 20% in the root mean square error (RMSE) relative to the second-best model (Gradient Boosting Regression), achieving values of 0.205 eV/atom (E_f), 0.718 eV (E_g), and 0.167 eV/atom (E_h). Prediction accuracy for double perovskites is significantly enhanced by training CGCNN on a combined dataset, particularly for E_h, where the coefficient of determination (R²) improves approximately 68.1-fold compared to models trained exclusively on double-perovskite data. Feature importance analysis via one-shot, permutation-based, and recursive feature elimination (RFE) methods reveals that optimal model performance requires retention of at least the top 20 critical features. Furthermore, feature utilization patterns of CGCNN across different prediction tasks are visualized. This work provides actionable guidelines for model selection and feature engineering in perovskite property prediction, establishing a benchmark for future ML-driven materials discovery. Full article

In this study, the synergistic effects of Fe doping and oxygen vacancies on the structural, electronic, and optical properties of Bi₄O₅Br₂, as well as their influence on the photocatalytic CO₂ reduction mechanism, were systematically explored through first-principles calculations. The results reveal that Fe-doped, oxygen-defective, and Fe–Vo co-modified Bi₄O₅Br₂ systems exhibit excellent thermodynamic and dynamic stability. Oxygen vacancies introduce defect states near the Fermi level, narrowing the band gap and enhancing charge localization and CO₂ adsorption, while Fe doping induces strong spin polarization and introduces Fe 3d impurity levels that effectively couple with O 2p orbitals, promoting charge transfer and visible-light absorption. The coexistence of Fe dopants and oxygen vacancies produces a significant synergistic effect, forming a continuous energy-level bridge that enhances charge separation and broadens the light absorption range. Gibbs free energy analyses further demonstrate that the Fe–Vo–BOB system exhibits the lowest energy barriers and the most favorable thermodynamics for CO₂-to-CO conversion. This study provides deep insight into the defect–dopant synergy in Bi₄O₅Br₂ and offers valuable theoretical guidance for engineering highly efficient visible-light-driven photocatalysts in solar energy conversion and environmental remediation. Full article

Phononic band gap crystals are characterized by periodic scatterers embedded within a matrix, which enable precise modulation of acoustic or elastic waves. Conventional optimization prioritizes bandwidth maximization, yet practical engineering often requires band gaps at specified frequencies. This requirement creates a significant design challenge. To this end, we develop a topology optimization strategy capable of maximizing elastic wave band gaps around prescribed target frequencies. The approach utilizes Material-Field Series Expansion (MFSE) for unit cell representation and a gradient-free Kriging-based algorithm to tackle the complex optimization problems. This strategy is systematically applied to optimize the band gaps of out-of-plane, in-plane, and complete wave modes, and is further extended to more complex scenarios involving dual-target frequencies. A variety of numerical results demonstrate the method’s effectiveness in engineering phononic crystals for bespoke frequency specifications. Full article

Classical earth pressure theories often struggle to account for the complex coupling effects of wall displacement and spatial non-uniformity under non-limit states. This study presents an interpretable machine learning framework designed to extract universal mechanical laws from heterogeneous experimental datasets. Using a multi-source database of rigid retaining walls with sandy backfill, a three-stage feature refinement strategy is proposed that incorporates Recursive Feature Elimination, Collinearity Analysis, and Interpretability Comparison to identify a parsimonious set of five fundamental physical parameters. A SHapley Additive exPlanations-Categorical Boosting (CatBoost-SHAP) framework is established to predict the active earth pressure coefficient (K) and interpret the underlying mechanisms across various movement modes (RB, RT, and T). Results demonstrate that the model effectively captures the progressive evolution of shear bands and the soil arching effect. Specifically, a critical displacement threshold of Δ/H ≈ 0.006 is identified, marking the transition from mode-dominated stress non-uniformity to magnitude-driven limit states. Leave-One-Dataset-Out Cross-Validation (LODOCV) confirms the model’s ability to maintain physical consistency over purely statistical fitting despite significant inter-literature heterogeneity. Finally, a Graphical User Interface (GUI) is developed to facilitate rapid, displacement-based design in engineering practice. This research bridges the gap between empirical laboratory observations and generalized mechanical logic, providing a data-driven foundation for refined geotechnical design. Full article

Organic optoelectronic devices receive appreciable attention due to their low cost, ecology, mechanical flexibility, band-gap engineering, brightness, and solution process ability over a broad area. In this study, we designed and studied organic light-emitting diodes (OLEDs) consisting of an assembly of natural dyes, extracted from noble fir leaves (evergreen) and blue hydrangea flowers mixed with poly-methyl methacrylate (PMMA) as light emitters. We experimentally demonstrate the effective conversion of blue light emitted by an inorganic laser/photodiode into longer-wavelength red and green tunable photoluminescence due to the excitation of natural dye–PMMA nanostructures. UV-visible absorption and photoluminescence spectroscopy, ellipsometry, and Fourier transform infrared methods, together with optical microscopy, were performed for confirming and characterizing the properties of light-emitting diodes based on natural dyes. We highlighted the optical and physical properties of two different natural dyes and demonstrated how such characteristics can be exploited to make efficient LED devices. A strong pure red emission with a narrow full-width at half maximum (FWHM) of 23 nm in the noble fir dye–PMMA layer and a green emission with a FWHM of 45 nm in blue hydrangea dye–PMMA layer were observed. It was revealed that adding monolayer MoS₂ to the nanostructures can significantly enhance the photoluminescence of the natural dye due to a strong correlation between the emission bands of the inorganic–organic emitters and back mirror reflection of the excitation blue light from the monolayer. Based on the investigation of two natural dyes, we demonstrated viable pathways for scalable manufacturing of efficient hybrid OLEDs consisting of assembly of natural-dye polymers through low-cost, purely ecological, and convenient processes. Full article

Ga₂O₃ and In₂O₃ are vital semiconductors with current and future electronic device applications. Here, we study the alloying of In₂O₃ and Ga₂O₃ (IGO) and the associated changes in structure, morphology, band gap, and electrical transport properties. Undoped films of IGO were deposited on sapphire substrates with varying indium (In) percentage from zero to 100% by metal-organic chemical vapor deposition (MOCVD). Some films were annealed in H₂ to induce electrical conductivity. The measurements showed the optical band gap decreased by adding In; this was confirmed by density functional (DFT) calculations, which revealed that the nature of the valence band maximum and conduction band minimum strongly relate to the chemistry and that the band gap drops by adding In. The as-grown films were highly resistive except for pure In₂O₃, which possesses p-type conductivity, likely arising from In vacancy-related acceptor states. N-type conductivity was induced in all films after H-anneal. DFT calculations revealed that the presence of In decreases the electron effective mass, which is consistent with the electrical transport measurements that showed higher electron mobility for higher In percentage. The work revealed the successful band gap engineering of IGO and the modification of its band structure while maintaining high-quality films by MOCVD. Full article

Widespread antibiotic residues in aquatic environments pose escalating threats to ecological stability and human health, highlighting the urgent demand for effective remediation strategies. In recent years, photocatalytic technology based on advanced nanomaterials has emerged as a sustainable and efficient strategy for antibiotic degradation, enabling the effective utilization of solar energy for environmental remediation. This review provides an in-depth discussion of six representative categories of photocatalytic nanomaterials that have demonstrated remarkable performance in antibiotic degradation, including metal oxide-based systems with defect engineering and hollow architectures, bismuth-based semiconductors with narrow band gaps and heterojunction designs, silver-based plasmonic composites with enhanced light harvesting, metal–organic frameworks (MOFs) featuring tunable porosity and hybrid interfaces, carbon-based materials such as g-C₃N₄ and biochar that facilitate charge transfer and adsorption, and emerging MXene–semiconductor hybrids exhibiting exceptional conductivity and interfacial activity. The photocatalytic performance of these nanomaterials is compared in terms of degradation efficiency, recyclability, and visible-light response to evaluate their suitability for antibiotic degradation. Beyond parent compound removal, we emphasize transformation products, mineralization, and post-treatment toxicity evolution as critical metrics for assessing true detoxification and environmental risk. In addition, the incorporation of artificial intelligence into photocatalyst design, mechanistic modeling, and process optimization is highlighted as a promising direction for accelerating material innovation and advancing toward scalable, safe, and sustainable photocatalytic applications. Full article

Herein, a high-performance Ta₂O₅/AgNPs composite Surface-Enhanced Raman Scattering (SERS) substrate is engineered for highly sensitive detection of microplastics. Through morphology modulation and band-gap engineering, the semiconductor Ta₂O₅ is structured into spheres and composited with silver nanoparticles (AgNPs), facilitating efficient charge transfer and localized surface plasmon resonance (LSPR). This architecture integrates electromagnetic (EM) and chemical (CM) enhancement mechanisms, achieving an ultra-low detection limit of 10⁻¹³ M for rhodamine 6G (R6G) with excellent linearity. Furthermore, the three-dimensional “pseudo-Neuston” network structure exhibits superior capture capability for microplastics (PS, PET, PMMA). To address spectral interference in simulated complex environments, a multi-scale deep-learning model combining wavelet transform, Convolutional Neural Networks (CNN), and Transformers is proposed. This model achieves a classification accuracy of 98.7% under high-noise conditions, significantly outperforming traditional machine learning methods. This work presents a robust strategy for environmental monitoring, offering a novel solution for precise risk assessment of microplastic pollution. Full article