This study presents a novel microwave-assisted low-temperature synthesis of 3C-SiC using lychee wood as a carbon source. Through systematic analyses using XRD, SEM, and XPS, the morphological evolution of SiC at different temperatures was studied and its growth mechanism under microwave heating was proposed. During the low-temperature stage (900 ℃), the microwave selective heating effect facilitated heterogeneous nucleation of SiC at the surface defect sites of the carbon source; at the medium-temperature stage (1000–1100 ℃), the microwave plasma effect reduced the activation energy, facilitating a gas–solid growth that produced rod-like crystals (with an aspect ratios of 10–20) preferentially oriented along the lychee wood fibre direction; at the high-temperature stage (1200 °C), the pyrolysis of the carbon template induced a structural collapse, resulting in a mixed morphology of short rods and particles. This method significantly reduced the energy consumption compared with traditional processes and dramatically shortened the reaction time to 10 min. This novel, green manufacturing strategy using biomass holds significant potential for the development of SiC applications in high-performance ceramics and advanced thermal management systems.
The development of sustainable, cost-effective silica sources is critical for green nanomedicine. In this scientific research, high-purity silica nanoparticles (SiO2 NPs) were synthesized via a solvent-assisted, top-down mechanochemical route using naturally abundant Indian beach sand. Two feedstock types—intertidal wet sand (BSC) and supratidal dry sand (FSC)—were processed via high-energy planetary ball milling for 4 and 8 h. Comprehensive characterization (FE-SEM, EDS, XRD, FTIR, DLS) revealed that FSC-8H exhibited superior physicochemical properties, with 94.21 wt.
This Paper explores the design of Heterojunction Tunnel Field-Effect Transistors (TFETs) using a SiO2 isolation layer between the drain and source regions in order to improve low-power applications. Optimal ON-state current and subthreshold swing (SS) are difficult to accomplish with conventional TFETs, which use quantum tunneling for switching. This calls for creative designs. We look at the distinct benefits of two TFET architectures: Ultra-Thin Body (UTB) and Nanowire (NW). To ascertain typical operating values for TFET-based inverters, the study examines Ids vs. Vgs features using device simulation modeling tools. A thorough knowledge of TFET performance for low-power applications is aided by this research, which determines ideal threshold voltage ranges, operable currents, the impacts of doping changes, channel length, and oxide and channel thickness. The proposed work highlights the significance of Tunnel Field-Effect Transistors (TFETs) in advancing energy-efficient electronics, focusing on Ultra-Thin Body (UTB) and Nanowire (NW) TFET architectures. The integration of a Silicon Dioxide (SiO₂) isolation layer enhances ON-state current, subthreshold swing, and overall power efficiency, contributing to sustainable computing. The UTB TFET demonstrated a threshold voltage of 0.709 V with a maximum current of 2.53 mA, while the NW TFET exhibited a threshold voltage of 2.095 V and a maximum current of 1.27 mA, both achieving a subthreshold swing of 62 mV/dec. The design, and simulations for optimized TFET architectures, ensuring precise modeling and fine-tuning of device parameters for low-power applications.
Addressing the challenge of extracting microcircuit defects in wafer images due to low-contrast features, this study presents a defect extraction method combining weighted fusion enhancement and adaptive contour segmentation. The method analyzes wafer microcircuit image patterns and grayscale distribution, deploying a target localization algorithm based on dark-region detection. Adaptive thresholding and region filtering enable precise defect localization. The low-contrast characteristics of wafer defects are addressed through an adaptive fusion enhancement algorithm, weighting local standard deviation enhancement and sigmoid nonlinear stretching enhancement to boost contrast while preserving defect details. Integrating multidimensional defect features, an adaptive active contour model is constructed, segmenting defects via iterative contour evolution. Enhanced defect images achieve edge preservation and sharpness indices of 0.74 and 0.33, respectively, with an average extraction precision of 0.9931. The method effectively mitigates low-contrast interference in non-destructive wafer defect identification.
This study examines an advanced thermal management strategy for solar photovoltaic (PV) panels utilizing eutectic phase change materials (PCMs) to alleviate heat-related efficiency losses. A comparative assessment was conducted between two panel configurations: Panel 1, incorporating a traditional wax-based phase change material, and Panel 2, employing a eutectic mixture of 60
Given the wide genetic variability of sugarcane (Saccharum spp.) and the importance of silicon (Si) for this crop, this study aimed to assess the response of 50 genotypes to Si supply under greenhouse conditions, screening for genotypes with contrasting efficiency and responsiveness to Si supply and high capacity for Si accumulation. Two greenhouse experiments were conducted under contrasting Si availability conditions: Low Si (no Si application) and Adequate Si (application equivalent to 600 kg ha−1 Si). The experimental design was a randomized complete block design with four replicates. At 120 days after planting, the following traits were evaluated: Plant Height (PH), Stalk Diameter (SD), Number of Stalks per Clump (NSC), Shoot Dry Matter (SDM), Shoot Si Concentration (SSiC), and Si Use Efficiency (SiUE). A significant genotype × Si availability interaction was observed for all traits, except SD. On average, Si supplementation increased NSC by 28.6
This work reports the novel fabrication and comprehensive characterization of lithium-doped magnesium titanate (Mg₀. ₈₅Li₀. ₁₅TiO₃) thin films synthesized via a sol–gel/spin-coating route and successfully integrated into Au/Mg₀.₈₅Li₀.₁₅TiO₃/p-Si heterojunction devices for transparent electronic applications. The originality of this study lies in establishing a direct correlation between lithium-induced defect engineering, optical band structure modification, and charge transport mechanisms within a single device-oriented investigation. X-ray diffraction (XRD) confirms a lower crystalline structure of calcines Mg₀.₈₅Li₀.₁₅TiO₃ thin film, whereas the formed film shows amorphous behavior and FTIR demonstrates a significant formation of Ti–O–Ti bonds, along with their connection to Li and Mg, while scanning electron microscopy revealed densely connected grains with average size 220 nm. XPS analysis confirmed the chemical purity and oxidation states of Ti4⁺, Mg2⁺, and Li⁺, revealing strong lattice oxygen bonding and partial surface crystallization within an amorphous matrix. Optical characterization demonstrated a direct allowed bandgap of 4.37 eV, determined through Tauc plot analysis, with multiple sub-bandgap absorption peaks at 284, 358, 450, 610, and 935 nm attributed to defect states and electronic transitions within the lithium-doped titanate system. The material exhibited high infrared transmittance (> 82
The main focus of the research is to develop and examine the silane treatedbiosilica extricated from paddy straw, PET core and pineapple fiber and are subjected to temperature and warm water aging. The study also investigates the creep, In-plane shear, thermal conductivity, ballistic impact and machinability behaviour of the varying composite specimens. Within the assessed specimens, the warm water aged composite C33 (with 25 vol.
Audio frequency nitrogen plasma, which includes generating plasma using nitrogen gas excited by audio frequency, has been used in this work. Three different metals (Al, Cu, and W) have been used as an electrode to investigate the alterations in the surface of soda-lime silicate glass (SLSG) samples. To study and compare the effect of these metal electrodes on the surface of the samples, different techniques have been utilized, like FTIR to evaluate the alteration in absorption bands, surface roughness behaviors by roughness tester, contact angles by contact angle goniometer, and the electronic transitions of the soda lime silicate glass using UV/Vis spectroscopy. FTIR spectra reveal that an alteration in the absorption band intensities was observed, indicating chemical modifications of the glass surface as a result of plasma exposure. This change may lead to the formation of new bonds or the alteration of existing ones. The presence of new absorption bands associated with surface species or structural alterations. The presence of new absorption bands associated with surface species or structural alterations. The variations in shifts and intensity variations of the Si–O-Si bands may be caused by the introduction of hydrated species, interactions with the silicate network, and changes in alkali ion concentrations. Then, the amendment of the surface structure and, hence, the kind and extent of the changes observed in the glass surface will depend on the kind of metal used for the electrode. Changes have been noted in surface roughness due to utilizing different metal electrodes as a result of the glass surface being etched, new chemical bonds being formed, and the oxidation process. This is attributable to the interaction between the plasma species and the glass, which can be impacted by the electrode material, determining the roughness parameter results. The contact angles of treated samples are reduced due to the formation of hydrophilic groups and surface hydroxylation, which depends on the type of electrode utilized. The kind of electrode material can create distinct surface qualities by altering the plasma chemistry and contact with the glass, which might vary the degree of surface alteration. UV–visible results displayed a shift in the absorbance edge toward the longer wavelength. This reflects a decrease in the band gap energy, which indicates an enhancement in the electrical conductivity of the samples. The surface amendments lead to changes in the material’s transparency, color degree, and linear refractive index through influencing the glass network and generating new absorption or scattering sites of light.
The strategic incorporation of carbon nanomaterials into polymer blends is a key route to develop advanced multifunctional materials for modern technologies. This work aims to fabricate a novel class of nanocomposite films based on a poly(vinyl chloride) (PVC)/polyethylene oxide (PEO) blend loaded with silicon carbide (SiC) nanoparticles and various carbon-based nanomaterials: multi-walled carbon nanotubes (MWCNTs), carbon nanoparticles (CNP), graphene oxide (GO), and reduced graphene oxide (rGO). The structural properties of the fillers and the blended polymers were investigated using X-ray diffraction (XRD) technique. The impact of these nanofillers on the morphology of the host polymer matrix was also studied. The incorporation of these fillers resulted in significant and tunable enhancements in the material's functional properties. Notably, the films exhibited excellent UV-blocking capability, with the PVC/PEO/SiC/GO composite showing the lowest optical transmittance, making them promising candidates for UV protection and solar cell absorber layers. The PVC/PEO/SiC/GO sample demonstrated the highest performance among the series, achieving a refractive index of 1.77 at 600 nm—a substantial increase compared to the pure blend—along with the greatest optical dielectric constant and enhanced nonlinear optical parameters. Furthermore, a dramatic reduction (quenching) of up to 75
Dopant-free asymmetric heterocontact (DASH) solar cells offer a promising approach to advancing silicon photovoltaic technology by overcoming the limitations of conventional architectures such as HIT, TOPCon, and POLO, which rely on heavy doping, complex fabrication, and toxic gases, creating challenges for scalability and cost-effectiveness. A novel DASH configuration is proposed, integrating transition metal oxides (TMOs), specifically Vanadium oxide (V2Ox), as the hole transport layer (HTL), and Lithium Fluoride (LiFx), an alkali metal-based material, as the electron transport layer (ETL). Using AFORS-HET software, the design is rigorously simulated and evaluated based on key performance parameters, including power conversion efficiency (PCE), open-circuit voltage (Voc), short-circuit current density (Jsc), fill factor (FF), and carrier selectivity. Following a series of optimizations, the proposed DASH cell achieves a PCE of 27.96
Accurate prediction of activity interaction coefficients is essential for optimizing the industrial silicon smelting and purification process. Nevertheless, several challenges persist, including the scarcity of experimental data, the complexity of multi-component interactions, prohibitive experimental costs and extended durations, and a substantial divergence between theoretical and experimental values. In this study, six machine learning models including Linear Regression (LR), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Network (MLP), Extreme Gradient Boosting (XGB), Tabnet, and Tab-Transformer (Tabnet-TF) were used to predict both theoretical and experimental activity interaction coefficients. The study found that XGB exhibited the best performance on the theoretical data, achieving determination coefficient (R2) > 0.98, root mean square error (RMSE) < 0.0082, mean absolute error (MAE) < 0.67, and mean absolute percentage error (MAPE) < 0.22. By integrating XGB with a transfer learning approach, pre-training on theoretical data and fine-tuning with sample weighting. The results demonstrate that the model significantly improved prediction accuracy on experimental data, attaining an R2 of 0.9960, compared to 0.6937 with the best standalone model. This work presents an efficient strategy for predicting thermodynamic parameters in silicon refining and validates the effectiveness of transfer learning in materials computation.
The extensive consumption of natural aggregates and the disposal of large volumes of industrial by-products present significant environmental challenges, necessitating sustainable alternatives for concrete construction. One such approach is the use of cold-bonded Fly Ash Aggregate (FAA) and Bottom Ash (BA) as partial replacements for natural aggregates, offering the potential to reduce environmental impact while producing lighter concrete. Cold bonding allows these by-products to be converted into artificial aggregates without energy-intensive thermal processes, supporting resource efficiency and waste utilisation. An experimental investigation was carried out to examine the compressive strength, flexural strength, deflection behaviour, workability, and density of concrete incorporating FAA and BA. The results show that replacing a portion of natural aggregates with FAA leads to a moderate reduction in strength, while still maintaining structural-grade performance. When BA is used together with FAA, changes in flexural behaviour become more pronounced, reflecting the influence of aggregate shape and stiffness. A noticeable reduction in concrete density was also observed, indicating clear potential for lightweight structural applications. At the same time, increased deformability in flexural members highlights the importance of considering serviceability requirements in design. Overall, the findings suggest that cold-bonded FAA–BA concrete can be used effectively in structural and non-structural applications where reduced self-weight and sustainability are important, provided appropriate design checks are applied. The study highlights the practical potential of industrial by-products as alternatives to natural aggregates, contributing to more resource-efficient and environmentally responsible construction practices.
The next generation of power electronic devices will necessitate mechanically robust substrates with excellent thermal conductivity. Silicon nitride will play an indispensable role because of its exceptional thermal and mechanical properties, which are crucial for operating effectively in the complex conditions commonly faced by power electronic devices. In this study, we used machine learning models (XGBoost, Random Forest, and Support Vector Regression) to get an insight into the impact of processing parameters on the thermal conductivity of reaction-bonded sintered silicon nitride. The best coefficient of determination achieved was 0.98 and 0.97 for training and testing data, respectively, using the XGBoost model. The model shows that sintering pressure, particle size, particle purity, and sintering time are significant, as reflected by their high feature importance scores. The present study paves the way for the successful integration of a machine learning approach in predicting the properties of silicon nitride and may be applied to other materials.
A novel method to surface engineering, hydrophobic nanocomposite coatings combine polymer matrices and nanoscale roughness to create water-repellent and self-cleaning surfaces for cutting-edge environmental and technological applications. In the present study, micro silicone emulsion developed by modifying polydimethylsiloxane (SE-PDMS) and silica nanoparticles (Si-NPs) was used to fabricate a hydrophobic coating on a glass substrate by using traditional painting brush. Si-NPs were synthesized from Padma River sand by following the sol–gel technology. Before incorporation in the polymer matrix, surface modification of Si-NPs was performed by stearic acid to alter the hydrophilic nature and reduce the agglomeration tendency. Si-NPs and coatings were characterized by X-ray diffraction (XRD), Fourier Transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM) analysis, Energy-Dispersive X-ray Spectroscopy (EDS), Ultraviolet–visible (UV–vis) spectroscopy, water contact angle (WCA) analysis, surface roughness analysis, and antifogging analysis. XRD analysis revealed the broad peak at 22.15°, confirming the presence of amorphous silica with an average crystallite size of 46 nm. With distinctive Si–O–Si, Si–C, and –CH₃ bands suggesting a persistent silicone-polyether layer, FTIR spectra verified the effective coating of SE-PDMS and surface-modified silica on glass. On the other hand, stearic-acid-modified silica added extra C–H and C = O peaks, improving hydrophobicity. SEM showed fine, nearly spherical, amorphous silica nanoparticles with some agglomeration (average size 58 nm), while SE-PDMS coatings formed uniform, compact films on glass. The presence of Si and O in silica and C, Si, and O in coated surfaces was confirmed by EDS, confirming successful nanoparticle synthesis and SE-PDMS deposition. By using the absorption spectra and Tauc plot, the band gap of silica nanoparticles was calculated to be 3.75 eV. By substituting hydrophobic methyl groups for hydrophilic hydroxyls, the 0.5
The mining industry produces vast amounts of Gold Ore Tailings (GOTs) in the form of soil or gravel, which can lead to major environmental problems such acid mine drainage, large voids left after excavation, soil erosion, water pollution, and air pollution. To alleviate environmental concerns, tailings can be used as a finer material in the production of concrete blocks, bricks, and tiles, among other things. Utilization of these tailings in the construction sector is possible by transporting it to the construction site and using it to make building materials. While extracting gold minerals from calaverite ore, the mining industry generates GOTs as a waste product. From the previous literatures, GOTs utilization in concrete makes better performance with respect to mechanical and durability properties. This paper reviews the utilization of GOTs for manufacture of building materials in the construction industry.
Bifacial dye-sensitized solar cells (BF-DSSCs) are an advanced type of opto-electro-mechanical systems, which utilize light-absorbing dyes on both sides of the cell to facilitate higher power conversion efficiency (PCE). Due to dual absorption, BF-DSSCs are greatly useful in a broad lighting environment as they can efficiently harness reflected and diffuse light. The way dyes adsorb and anchor to mesoporous titanium dioxide (TiO2) can improve performance. Recent developments aim to preserve high bifaciality factors (rear/front PCE ratio) of about 83
This study presents the fabrication and characterization of PS–PEG/SiO2–Co2O3 nanocomposites synthesized via a simple solution-casting method for pressure-sensing applications. Hybrid films containing 1.1–3.3 wt
In this paper, we examine the DC, analog/RF performance metrics of gate-stacked cylindrical gate-all-around (CGAA) single-material gate (SMG) Si and GaN nanowire field-effect transistors (NWFETs), focusing on the effect of temperature, doping concentration, and work function (ϕ) at sub-10 nm node. DC and AC/RF performance metrics are investigated, including drain induced barrier lowering (DIBL), subthreshold swing (SS), ON current (ION), OFF current (IOFF), transconductance (gm), transconductance generation factor (TGF), output conductance (gd), early voltage (VEA), parasitic capacitance (Cgg), cut-off frequency(fT). Power consumption was analysed and compared between the SMG Si and GaN CGAA NWFET devices. In both devices, an increase in ϕ suppresses short-channel effects (SCE) and improves leakage performance, with GaN showing a substantially lower IOFF reaching less than nA in the range of 10−15A and a higher VEA of 83.51 V. Si NWFET exhibits a higher gₘ of 62.45µS and fT to be in the THz range during temperature change, but they also have a worsened switching ratio (ION/IOFF) and more leakage, while GaN devices continue to exhibit superior thermal stability and analogue behaviour. Doping variation demonstrates that, even at high doping, GaN maintains superior ION/IOFF ratio in the range of 108 and output characteristics, whereas ION improves in both devices. GaN offers superior TGF and leakage control, while Si delivers higher gₘ and marginally better fT at low doping. Si NWFET continues to function well for high-speed digital logic under moderate thermal and doping circumstances, whereas GaN NWFETs are generally better suited for low-power and high-temperature applications.
This paper addresses the imperative need for low-power electronic devices, which is crucial in today's technology landscape where energy efficiency and portability are paramount. Traditional MOSFETs, while prevalent, face significant challenges when scaled down for low power applications, particularly due to high leakage currents and suboptimal performance at lower voltages. Tunnel Field-Effect Transistors (TFETs) emerge as a viable solution due to their ability to achieve steeper subthreshold slopes (SS), enabling lower operational voltages and reduced power consumption. However, TFETs inherently suffer from limitations such as low ON-current (ION) and ambipolarity, which hinder their widespread adoption. To mitigate these issues, this research explores advanced source engineering techniques to enhance TFET performance. The study begins with the double gate TFETs (DGTFET), followed by an investigation into broken gate TFETs (BG-TFET), focusing on optimizing parameters such as drain doping concentration, gate-drain underlap, and drain split configurations. Gate discontinuity in broken gate TFETs enhances the local electric field at the tunneling junction, boosting ON-state current. It also suppresses ambipolar leakage, improving overall device efficiency for low-power applications. A significant advancement is achieved by introducing a Germanium source in the broken gate TFET (Ge-BG-TFET), aimed at improving the ION. Simulations conducted using TCAD software demonstrate an ION of around 10–5 A/µm, and by employing the split drain technique the ambipolar current reduced to IA of around 10–11 A/µm. These results indicate a substantial improvement in device efficiency, effectively overcoming the low ION challenge of traditional TFETs while maintaining low power operation. Furthermore, the applicability of the proposed Germanium source broken gate TFET structure is validated through its realization in low power biosensing application.
