Porous SnS nanostructures have attracted significant attention owing to their excellent properties, including a large specific surface area, enhanced light trapping, and a tunable energy gap, which make them promising materials for high-performance photodetectors and other optoelectronic devices. In this manuscript, porous SnS-PVA nanostructures were prepared by pulsed laser ablation in liquid (PLAL). The microstructural, optical, and photodetection properties of the fabricated devices were systematically investigated. The SnS porous nanostructures were formed by laser ablation of the tin (Sn) target within a thiourea [SC(NH₂)₂]-PVA aqueous solution, using a fixed number of 500 laser pulses. The optical energy gap was determined to be 1.71 eV. X-ray diffraction (XRD) analysis confirmed that all synthesized SnS nanoparticles (NPs) demonstrated a polycrystalline nature with an orthorhombic crystal structure. Raman spectroscopy revealed characteristic vibrational modes centered at approximately 515 and 621 cm− 1. FE-SEM investigations revealed the formation of interconnected porous network nanostructure with surface porosity of 56.09 0.426 ± 0.00598 ( A W^-1 ), a specific detectivity (D*) of) 1.14± 0.01598) × 10¹¹ Jones, and an external quantum efficiency (EQE) of 117± 1.366
This study presents a machine learning model on U-channel-T-shaped gate with an Insb/SiGe heterojunction pocket doped vertical TFET. Higher ON-current and little OFF-state leakage are achieved by the dual-region source pocket’s efficient reduction of the tunneling barrier width. The electrical performance of three different V-TFET architectures is assessed in this work, with an emphasis on metrics including capacitance, surface potential, field distribution, and current-voltage behaviour. Because of their great computational complexity, Technology Computer-Aided Design (TCAD) tools are intrinsically constrained in their ability to handle large-scale optimization tasks. To address these challenges, this work proposes a Physics-Informed Neural Operator (PINO)-based surrogate modeling framework for efficient threshold voltage (Vth) modification and retention characteristic prediction. In comparison to traditional TCAD simulations, the suggested PINO model provides a computational speedup of more than four orders of magnitude while maintaining great physical fidelity and predictive accuracy by directly integrating basic physical concepts into the learning architecture.
The oxidation behavior of Sn–0.7Cu–xMn lead-free solder alloys was systematically investigated to elucidate the interplay among synthesis, microstructural evolution, thermodynamic driving forces, oxidation kinetics, and phase transformations during thermal exposure. Microstructural analysis by SEM (Scanning electron microscopy) revealed a β-Sn matrix reinforced with η-Cu₆Sn₅ intermetallic compounds. Mn addition promotes intermetallic refinement and acts as an oxygen scavenger, thereby modifying diffusion pathways and oxidation kinetics. Thermodynamic calculations showed that oxide formation in the Sn–Cu–Mn system is inevitable within the processing temperature range, with SnO₂ exhibiting the most negative ΔG° and thus dominating oxidation. Oxidation remains thermodynamically favorable even at very low oxygen partial pressures. Oxidation kinetics during heating (RT–350 °C) and isothermal holding at 350 °C through thermogravimetric analysis (TGA) followed diffusion-controlled behavior consistent with parabolic oxidation kinetics. X-ray diffraction (XRD) analysis identified SnO₂ as the primary oxide phase, along with minor Cu₂O and MnOₓ formed via selective oxidation. The results demonstrate that controlled Mn additions modify oxide scale chemistry and diffusion pathways, thereby improving the oxidation resistance of Sn–Cu based lead-free solders through the formation of a more stable and protective oxide layer.
In this study, the synthesis (solid-state reaction) and characterization of the BiVMnO6 (BVMO) ceramic are investigated. The preliminary X-Pert High-Score structural analysis reveals formation of a major phase of monoclinic crystal structure with a tolerance factor of 1.01, average crystallite size of 36 nm, a compressive micro-lattice strain of 0.000921, and dislocation density of 7.72 × 1014 m⁻2. Scanning electron microscopy (SEM) micrograph analysis reveals well-developed grains with a non-uniform distribution across the entire surface of the sample. The ImageJ software was used to calculate the average grain size of 1.15 µm, and the agglomeration ratio (DSEM/DWH) is found to be 32, which may be a good reason to show better electrical properties. The dielectric properties as a function of temperature and frequency show Maxwell–Wagner type dielectric dispersion. The negative temperature coefficient of resistance (NTCR) behaviour is confirmed by impedance analysis. The ac conductivity study suggests a thermally activated hopping mechanism in the material. Moreover, the Nyquist and Cole–Cole plot analyses confirm the semiconducting nature of the material, indicating its potential suitability for energy storage device applications. The resistance–temperature measurements confirm the negative temperature coefficient (NTC) thermistor behaviour of the material, indicating its potential suitability for temperature sensing applications.
In this study, z[BaTiO3]/1-z[Zn0.8Co0.2Fe₂O₄] (Z = 0.10, 0.15, 0.20) composites have been synthesized using both sol-gel auto-combustion and solid-state reaction methods. Firstly, Ferrite (Zn0.8Co0.2Fe2O4) is synthesized using the sol-gel auto-combustion method, in which Zn, Co, and Fe metal nitrates react with citric acid to form a precursor powder. This powder is then calcined at high temperature to produce the ferrite material. After synthesizing the ferrite, it was prepared by mixing barium titanate (BaTiO3) with the ferrite using the solid-state reaction method, incorporating 5, 10, 15, and 20 wt
