Journal of Computational Electronics最新文献

In recent decades, there has been significant interest in finding negative electrode materials that offer excellent reversibility, low open circuit voltages, high specific capacity, and rapid charge–discharge rates. This inclusive innovation study examines the suitability of a T-BN monolayer as negative electrode material for Na-ion batteries (SIBs) applying first-principles computations. The impressive flexibility of T-BN enables it to exhibit strong reversibility when faced with volume expansion resulting from full adsorption. Furthermore, when Na is adsorbed onto the T-BN monolayer, the layer exhibits metallic characteristics, highlighting an exceptional electrical conductivity. Moreover, the T-BN monolayer exhibits a combination of features including a suitable average open-circuit voltage (OCV, 0.26 V), remarkable specific capacity (839.51 mA h g⁻¹), low diffusion energy barrier (37 eV), and minimal change in lattice constants (2.78%). With these outstanding characteristics in mind, we anticipate that T-BN has promise to serve as a favorable negative electrode material for SIBs.

We present a compact in-fiber polarization beam splitter (PBS) implemented in a gold-coated dual-core photonic crystal fiber (DC-PCF), using finite element method (FEM). The octagonally arranged DC-PCF achieves enhanced birefringence through optimized structural design. Gold layers integrated within the two large air holes induce surface plasmon resonance (SPR), which modulates the optical response at the edges of the operational band and enhances polarization splitting efficiency. Numerical analysis shows that when the lattice gap Λ = 1.6 μm, diameters d₁ = 1.2 μm, d₂ = 0.5 μm, d₃ = 2.1 μm, d₄ = 0.7 μm, and a gold layer thickness of t = 50 nm, this PBS achieves a coupling length ratio (CLR) of 0.5 at 1.55 μm. It exhibits a shortest splitting length of 220 μm and a maximum extinction ratio (ER) of -133 dB over an operating bandwidth of 140 nm. The fabrication process and experimental setup are analyzed. It is worth anticipating that this polarizer will emerge as a crucial signal processing component in photonic integrated systems, driving the continuous advancement of communication systems and information technology.

In this work, an interpretable hybrid modeling framework is developed that integrates physics-based TCAD simulations, compact Verilog-A implementation, and graph neural network (GNN) analysis to investigate nanoscale Junctionless Gate-All-Around (JLGAA) FETs for ultra-low-power current mirror circuits. The proposed multi-scale methodology couples Silvaco TCAD data with a calibrated Verilog-A compact model, enabling accurate and efficient circuit-level evaluation in the Cadence Spectre environment. The compact model reproduces TCAD characteristics with less than 2.5% deviation, while achieving a > 1000 × reduction in simulation time compared with full 3-D TCAD analysis. An interpretable GNN-based deep learning framework predicts circuit performance metrics, including current accuracy, output resistance, and power consumption, with a mean absolute error below 4% across 900 simulation cases. SHapley Additive exPlanations (SHAP) reveal that channel doping and gate length dominate current matching and energy efficiency. The proposed hybrid TCAD–Verilog-A–GNN methodology provides a transparent, accurate, and computationally efficient pathway for the design and optimization of next-generation ultra-low-power nanoelectronic circuits.

This study aims to enhance the performance of dithienosilole-based non-fullerene acceptors (NFAs) for organic solar cells (OSCs) through strategic end-capped modifications. Eleven novel NFAs (D1–D11) were theoretically designed by substituting the cyano groups of the reference molecule (BCNDTS, R) with various electron-withdrawing units. Their optoelectronic and photophysical properties were systematically investigated using density functional theory (DFT) and time-dependent DFT (TD-DFT) at the PBE1PBE/6-31G(d) level. Among all the scrutinized structures, D10 showed the most remarkable outcomes, such as the lowest band gap (E_g = 1.96 eV), the greatest electron affinity (E_A = 3.35 eV), the highest λ_max (773 nm in gaseous and 836 nm in dichloromethane), and the lowest excitation energy (E_x = 1.60 eV in gaseous and 1.48 eV in dichloromethane). D9 also exhibited considerable enhancements in different aspects, such as a planar structure, the highest light harvesting efficiency (LHE = 0.990), and a narrow bandgap (E_g = 2.04 eV). The electron donor molecule PTB7TH was used to calculate the open-circuit voltage (Voc) of the model molecule and D1–D11. It was found that end-capped tailoring has a significant effect on the open-circuit voltage. In order to assess the charge transfer ability of the designed acceptors, we presented the HOMO and LUMO orbitals of the complex D9-PTB7Th. It was found out that all the tailored compounds, particularly D10 and D9, could be advantageous in the manufacturing of advanced NFAs for next-generation photovoltaic technologies especially D10 and D9.

Graphical abstract

Graphene nanostructures (GNSs) exhibit unique electronic and magnetic properties dependent on their shape and size. This research investigates the face index, a topological descriptor derived from chemical graph theory, as a means to characterize the structural topology of three distinct regular convex polygons: triangles, hexagonal, and rhombus-shaped GNSs, which are the most simple high-symmetry convex structures that can be ideally cut out of a graphene layer. We calculate the face index for these three geometries using graph theoretical methods. The results offer quantitative insights into the structure of these nanomaterials and provide a valuable contribution toward understanding structure–property relationships in graphene-based systems.

The electronic structure and optical properties of the photodetector based on the vertical TeSe₂/GaSe van der Waals heterostructure (vdWH) have been investigated by way of the first-principles calculations. The obtained results indicate that the dynamically stable TeSe₂/GaSe vdWH exhibits an indirect band gap of 0.63 eV and a type-I band alignment. A semiconductor-to-metal transition can be realized when the compressive strain exceeds − 4.4%, and the band alignment of the system should transform from type-I to type-II at the regime of 1.1% to 3.7% in-plane biaxial tensile strain. Compared to the TeSe₂ and GaSe monolayers, the TeSe₂/GaSe vdWH photodetector exhibits maximum absorption coefficient of ~ 25% in the visible range. The maximum photocurrent of the proposed TeSe₂/GaSe vdWH photodetector reaches 0.97 ({text{a}}_{0}^{2}/text{photon}) at the photon energy of 1.8 eV while the extinction ratio achieves a maximum value of 33.2 at the photon energy of 2.0 eV. The proposed photodetector from the vertical TeSe₂/GaSe vdWH may shed some light on the realization of high-performance photodetector applications.

Malaria remains a major global health challenge, with millions of cases and significant mortality each year, particularly in tropical and subtropical regions. Conventional diagnostic techniques, such as microscopy and rapid diagnostic tests (RDTs), suffer from limitations including reagent instability and false negatives. This necessitates rapid and reliable diagnostic alternatives for effective management and control. Surface Plasmon Resonance (SPR) based biosensors are now widely recognised for detecting biomolecular interactions in real time, without the need for labelling. In this work, we present the design and numerical analysis of a high-performance Kretschmann-configured surface plasmon resonance-based sensor. The proposed structure incorporates silver (Ag) as a metal layer, silicon (Si) as a dielectric layer and ultrathin TiO₂ as an additional dielectric layer to enhance optical response for medical diagnostics. The sensor is evaluated for the detection of malaria-infected red blood cells across different intraerythrocytic developmental stages. Performance metrics were analysed using the finite element method (FEM) in COMSOL Multiphysics and the transfer matrix method (TMM) in MATLAB environment. Optimization of thickness and layer configuration yielded sensitivity of 425 deg/RIU, 337.03 deg/RIU, and 302.70 deg/RIU for ring, trophozoite and schizont phases, respectively. Corresponding figure of merit (FoM) was calculated as 124.70 RIU⁻¹, 109.03 RIU⁻¹ and 101.95 RIU⁻¹ with a maximum detection accuracy of 0.336 deg⁻¹ for the schizont phase. Furthermore, a tolerance study was done to assess the robustness against minor fabrication errors. These results demonstrate the potential of the suggested SPR sensor, which utilises enhanced plasmonic responses to detect minimal refractive index variations, as a platform for early malaria diagnosis.

Parameter extraction for the triple-diode photovoltaic (PV) model presents a complex, highly nonlinear optimization problem. It necessitates an optimization algorithm that has strong exploration and exploitation abilities to avoid getting stuck in local optima and to accurately determine the model’s parameters. Although various optimization methods have been used in the literature to address this problem, most of them produce poor results, show instability across different PV modules, have slow convergence, and/or require high computational costs. These limitations motivate us to introduce a new robust parameter identification technique called IGO, which can achieve more accurate results with fewer function evaluations. It is based on integrating the recently published growth optimizer (GO) with two new optimization strategies—the convergence improvement strategy and the ranking-based update strategy. The latter strategy steadily enhances the exploration operator throughout the optimization to prevent premature convergence to local optima. Simultaneously, it gradually boosts the exploitation operator in the late phases to accelerate convergence to the global optimum. The former strategy focuses on enhancing the exploitation operator during the optimization process to maximize convergence speed while strengthening the exploratory operator in late stages to mitigate the risk of settling in local optima. Integrating both strategies in the proposed IGO aims to balance exploration and exploitation throughout different phases of iteration, thereby preventing stagnation in local optima and encouraging rapid convergence toward the global optimum. The proposed IGO is tested on six popular PV modules and compared with several recently published optimizers using various statistical measures in addition to convergence speed to demonstrate its effectiveness and significance. The experimental results demonstrate that IGO outperforms all other methods in both parameter quality and convergence speed, confirming it as a reliable alternative for extracting the unknown triple-diode model parameters.

This study presents a real-time biosensor for hemoglobin concentration detection based on optical Tamm resonance in a one-dimensional photonic crystal platform. The sensor architecture, comprising a silver layer, a blood sample layer, and a periodic Si/SiO₂ multilayer on a BK7 glass substrate, supports sharp resonance features due to Tamm state excitation at the metal–dielectric interface. Notably, the biosensor differentiates blood groups (A, B, and O) through distinct resonance wavelength shifts arising from their characteristic refractive index differences, enabling group-specific hemoglobin detection. The resonance dip initially appears at 751.1 nm and exhibits a pronounced redshift with increasing hemoglobin concentration, reaching up to 814.2 nm for 200 g/L in blood group O. Using the Transfer Matrix Method, the optical response is evaluated under both normal and oblique incidence for TE and TM polarizations. The biosensor demonstrates exceptional performance under TE-polarized oblique incidence, achieving a maximum sensitivity of 0.599 nm/(g/L), a figure of merit of 0.716 1/(g/L), and a detection limit as low as 0.069 g/L. The prism-free, planar configuration ensures a compact and cost-effective setup ideal for point-of-care diagnostics. Additionally, the device maintains high spectral resolution and stable operation across physiological temperature ranges, making it a robust platform for real-time clinical blood analysis.

The present work comprehensively investigates the design, optimization, and predictive modeling of a high-performance, lead-free RbGeI₃-based perovskite solar cell employing WS₂ as the ETL and CuI as the HTL. Numerical simulations performed using SCAPS-1D were validated against theoretical efficiency limits and electrostatic consistency checks, confirming the model’s physical reliability. The device achieved an optimized η of 24.15%, with V_oc = 1.1184 V, J_sc = 25.999 mA·cm⁻², and FF = 83.09%, demonstrating excellent charge extraction and minimal recombination losses. Systematic parametric analyses revealed the critical influence of absorber doping, transport layer defect density, resistive losses (R_s/R_sh), absorber thickness (t-Abs), defect density (Nt), temperature, and solar irradiance on overall performance. Optimal operation was achieved for Nt = 10¹⁴ cm⁻³, where light absorption and carrier transport are well balanced. Furthermore, machine learning (ML) algorithms, including XGBoost, random forest, and gradient boosting, were employed to predict photovoltaic outputs with near-perfect accuracy (R² ≈ 1.0). The XGBoost model successfully identified absorber defect density, series resistance, and illumination intensity as the most dominant performance-determining features. The results demonstrate that the synergistic combination of WS₂/CuI transport layers and ML-guided optimization establishes a promising framework for stable, efficient, and eco-friendly RbGeI₃-based PSCs, paving the way for next-generation lead-free photovoltaic technologies.