The increasing market demand for sweet corn has made it a widely cultivated crop among local farmers. One method of enhancing sweet corn productivity is combining potassium sulfate nanofertilizers from banana peel waste and nanosilica from rice husk waste. Nanosilica was synthesized using the sol-gel method; nanopotassium sulfate was synthesized by precipitating potassium using ammonium sulfate. Characterization results from FTIR (Fourier Transform Infrared Spectroscopy), XRD (X-Ray Diffraction), FESEM-EDX (Field Emission Scanning Electron Microscopy with Energy Dispersive X-ray), and TEM (Transmission Electron Microscopy) showed that the potassium-silica nanofluid fertilizer had a particle size of 8.6 nm. A single application of nanosilica enhanced the vegetative growth of sweet corn. Applying nanopotassium sulfate significantly increased plant height, especially in the S3W1 treatment 28 DAS (days after sowing). The combination of application time and concentration of potassium-silica nanofluid fertilizer had a significant effect on plant height. Best results were obtained with a concentration of 5 mL applied 35 DAS.
This study applied Fe3O4/SiO2/PPA magnetic nanocatalyst as a solid acid catalyst to produce bis(indolyl)methane derivatives (BIMs) under solvent-free conditions and at 50 degrees C. FTIR, XRD, EDX, FESEM, HRTEM, and BET were used for structure confirmation. The catalyst was environmentally friendly, heterogeneous, and recyclable without loss of catalytic activity. Also, this catalyst had high efficiency for the synthesis of bis(indolyl) methanes and the reaction products were obtained quickly. The synthesis of bis(indolyl)methanes from the reaction between indole derivatives and aldehydes has been carried out in short reaction times (5-30 min) with good to excellent yields (65-96%) using 50 mg of magnetic nanocatalyst at solvent-free conditions.
This work examined the oriented external electric field (OEEF) along x, y, and z-axes in the interaction between vanadocene dichloride (VDC) anticancer drug and C@Al12 cluster. OEEF effect on the electronic energy and interaction energy values was studied. These results indicated weaker adsorption in VDC. ..C@Al12 complex in the presence of OEEF along the z-axis than x, and y-axes. Variations of polarity and frontier orbital energy values of the molecule with strength of OEEF were reported. Greater polarity was found with higher OEEF strength. The most contribution in the SOMO and LUMO of the VDC. ..C@Al12 complex were belonged to C@Al12 and VDC moieties, respectively. Also, dependencies of reactivity parameters on OEEF strength were provided. Cl. ..Al interactions were illustrated with electron localization function (ELF) and charge displacement curves (CDC) results.
A simple wet-chemical method has been deployed to synthesize copper oxide (CuO) nanostructures. FESEM images revealed the formation of uniform-sized, spindle-like nanostructures. The TEM image revealed that the nanospindles are composed of several small crystallites. The distinct diffraction peaks in the XRD pattern confirm well-crystalline nanostructures and the average crystallite size was 10 nm. FTIR spectra revealed the formation of Cu-O bonds. The band gap of the nanocrystals was calculated from the absorption spectra and was estimated to be 3.28 eV. Quantum confinement effect is therefore found to be very significant in these nanocrystals, manifested by the band gap enhancement. The synthesized CuO nanospindles exhibit strong photoluminescence peaked at 407 nm owing to band-to-band transition. This emission peak is due to the transition associated with low energy peaks at 461 nm due to shallow levels created below the bottom of the conduction band. The nanostructure showed significant antimicrobial activity against S. Aureus (Gram-positive) and K. Pneumoniae (Gram-negative) bacteria as studied by agar well diffusion technique. The zone of inhibition depends on the concentration of nanoparticles as revealed from the study. Therefore, the synthesized CuO is of immense potential for optoelectronics and biomedical applications.
This study presents a comprehensive investigation into the structural, chemical, and electronic properties of zinc oxide (ZnO) and tannic acid-functionalized ZnO (ZnO-TA) nanostructures, synthesized via a green hydrothermal route under controlled pH conditions. FESEM revealed that pristine ZnO crystallized into welldefined nanorods, whereas incorporation of TA at acidic pH values induced a morphological transition towards interconnected networks, concomitant with elevated carbon incorporation. FTIR confirmed the presence of Zn-O vibrational modes alongside characteristic peaks corresponding to carbonyl, ester, and hydroxyl functionalities, evidencing successful surface modification by TA. The four-point probe I-V measurements demonstrated linear ohmic behaviour and a significant enhancement in lateral conductivity, reaching up to 10-8 S/cm, surpassing previously reported benchmarks for similar systems. Hall effect measurements consistently indicated n-type conductivity across all samples. Notably, carrier mobility peaked at pH 5, attributed to effective passivation of surface trap states by TA's functional groups. In contrast, excessive TA loading at pH 3 resulted in increased carrier concentrations but diminished mobility, likely due to nanoparticle aggregation and enhanced grain boundary scattering. These findings demonstrate the pivotal influence of pH and TA concentration in tailoring the morphology, surface chemistry and charge transport characteristics of ZnO nanostructures.
of This study investigates the antimicrobial effects of nisin extracted from Lactococcus lactis and the enhancement of its antimicrobial effect by incorporation into chitosan/alginate nanoparticles. Native strains of Lactococcus lactis were isolated from milk samples and the presence of the nisin gene was confirmed using polymerase chain reaction (PCR). Nisin concentration was determined using high-performance liquid chromatography (HPLC). The results showed that out of ten isolated strains, four strains (L1, L3, L8 and L9) showed genetic similarity to Lactococcus lactis with percentages of 82.45%, 79.87%, 93.33% and 79.87%, respectively. The optimal growth conditions for the most effective strain were determined at 30 degrees C and pH 7 with an optical density of 5.51 at 600 nm. Nisin extract showed a significant inhibitory zone of 19 mm against Bacillus cereus, while nisin-containing nanoparticles showed an enhanced inhibitory effect of 20 mm. Furthermore, the results showed that the combination of nisin-containing nanoparticles significantly improved the antimicrobial activity compared to nisin alone. This study showed that nisin incorporated into nanoparticles effectively retained its antimicrobial properties, indicating its potential as a valuable model for biopreservation of food. These findings emphasize the importance of using natural antimicrobials in food preservation.
Research into anti-cancer compounds from natural sources, such as Ganoderma lucidum and chitosan, and the development of their delivery systems, is a highly promising field. This study focused on developing nanocarriers of Ganoderma lucidum polysaccharides-chitosan and evaluating their effects on the expression of the Bax and Bcl-2 genes in the PC3 cell line. The extraction of polysaccharides from Ganoderma lucidum involved heating the material in water at 70 degrees C, followed by treatment with 96% ethanol and subsequent protein removal to purify the polysaccharides. These polysaccharides were successfully incorporated into chitosan nanoparticles via ion-exchange copolymerization. Scanning electron microscopy (SEM) and Fouriertransform infrared spectroscopy (FTIR) were utilized to examine the morphology and chemical properties of the synthesized nanoparticles. The effects of the chitosan-polysaccharide nanoparticles and free polysaccharides on gene expression were assessed using real-time PCR after treating PC3 cells with various concentrations. The results showed that the nanoparticles and polysaccharides increased Bax gene expression by 4.6-fold and 5.56-fold, respectively, while decreasing Bcl-2 expression by 0.64-fold and 0.81-fold, respectively. These observed changes indicate that apoptosis was induced in PC3 cells. Nanoparticles composed of polysaccharides and chitosan show promise for future prostate cancer therapies; however, additional in vitro and in vivo studies are necessary.
Recent advancements in nanoemulsion technology have profoundly influenced the food industry, providing innovative approaches to enhance food product quality, stability, and functionality. Nanoemulsions (20-200 nm) exhibit distinct physicochemical characteristics, such as a large surface area, controlled release of bioactive compounds, high loading capacity, improved bioavailability, and biphasic behavior. This review explores the latest developments in nanoemulsion types, their components, and their applications across various food sectors, focusing on the properties they confer to food products and packaging systems. Particular attention is given to their ability to improve the bioavailability of poorly soluble nutrients, stabilize food formulations, and integrate natural preservatives to extend shelf life. The article also examines emerging trends, including the use of eco-friendly surfactants, encapsulation of plant-based bioactives, antioxidants, vitamins, and fatty acids, and their potential to enhance functional and fortified food products. Our review demonstrates that nanoemulsions significantly improve nutrient delivery, extending the shelf life of food products, and enhancing sensory characteristics. Furthermore, they offer promising avenues for sustainable packaging solutions. However, challenges related to long-term stability, regulatory approval, and consumer perception remain critical areas for future research. Furthermore, challenges like scalability, regulatory hurdles, and consumer acceptance are addressed. Based on this review, a comprehensive update on the current state of nanoemulsions in food science is essential, with a focus on establishing and refining specific standards to align with industry needs for healthier, safer, and more sustainable food systems.
The primary objective of this study is to evaluate the feasibility of using hazelnut shells as a biosorbent for the cleanup of oil and petroleum product spills in water bodies. The effects bioadsorbent, nanoparticle concentrations, oil concentration, temperature (10-400 degrees C), and pH on oil adsorption were investigated to determine the optimal conditions for maximum purification efficiency. The highest oil removal efficiency achieved with the biosorbent was 61.25% at a pH of 7.5. To enhance the sorption capacity of the biosorbents were synthesized by incorporating Fe3O4 super paramagnetic nanoparticles at concentrations of 1%, 3%, 5%, and 10%. Characterization of both the biosorbents was conducted using SEM, TGA, and FTIR analyses to assess the interaction between Fe3O4 nanoparticles and the plant-derived surface of the biosorbent. For an adsorbent such as hazelnut shell + Fe3O4 bio-nanoadsorbent, the pHzpc indicates the point at which the surface of the bio-nanoadsorbent changes from negatively charged to positively charged as the pH of the solution increases or decreases. The zero-charge pH point of the bio-nanoasorbent was determined to be pH = 8 by Boehm titration. The optimal adsorption conditions for the bio-nanosorbent, composed of hazelnut shell and 10% Fe3O4 nanoparticles, were found to be 92.5% oil removal within 12 minutes at a pH of 7.5.
This study developed a comprehensive framework for assessing and managing health risks of manufactured nanomaterials (MNM) in workplaces. The approach combined control banding with multi-criteria decision-making, informed by expert input and a literature review on MNM hazards. A three-layered risk assessment was applied: hazard banding using the Globally Harmonized System (GHS) for five toxic endpoints, exposure banding based on MNM physical state, manufacturing processes, and working conditions (yielding four bands), and integration via control banding to classify risk into five levels. These layers complement each other to enhance accuracy. Control measures were identified and evaluated using a General Technique for Evaluating Control Measures (GTECM), scoring effectiveness, cost, and feasibility, with acceptable inter-rater reliability. The framework was implemented as an online tool to support practical application in Iranian nanotechnology workplaces. Field testing in selected facilities demonstrated its usability and consistency. By systematically integrating hazard, exposure, and control data, the framework supports informed decision-making for occupational health and safety managers. It aims to strengthen risk management practices and promote safer handling of MNMs, contributing to improved workplace health in Iran's growing nanotechnology sector.
Despite the rapid growth of pharmaceutical industries to meet rising demand, untreated or poorly treated wastewater continues to contaminate water bodies, posing serious risks to ecosystems and human health. Conventional treatment methods often fall short in effectively addressing these complex and persistent pollutants. In this context, nanomaterials have emerged as promising candidates for advanced water purification. This study presents a novel approach by employing TiO2 and nitrogen-doped TiO2 (N-TiO2) nanoparticles, synthesized via a simple and scalable sol-gel method, for the treatment of actual pharmaceutical industrial effluent under natural sunlight, a cost-effective and sustainable condition rarely explored in previous works. The synthesized photocatalysts were thoroughly characterized using UV, XRD, SEM, FTIR, and EDX analyses. Unlike most previous studies focused on model pollutants, this work directly addresses the degradation of real pharmaceutical wastewater by combining adsorption and photocatalysis. Higher adsorption efficiencies were observed for N-doped TiO2 nanoparticles (43.77%) compared to TiO2 (21.34%) after 2 hours. Photocatalytic treatment further enhanced the degradation to 57% (N-TiO2) and 22% (TiO2) after 3 hours under natural sunlight. The study demonstrates the potential of N-TiO2 as an efficient, sunlight-driven photocatalyst for practical wastewater treatment applications, highlighting its novelty and real-world applicability.
This report reveals the geometry, vibrational properties, and molecular orbitals with HOMO-LUMO energies of HCl clusters (HCl)n=1-2 under the confinement of carbon fullerenes CN=42-52 at B3LYP level with basis set 6-311++g(d,p). Due to the effect of the H-bond network, the variation in bond length is observed relative to the variation of diameter of carbon fullerenes from C42 to C52. Unexpectedly, the high intermolecular stretching mode (3403.96 cm-1) is observed for a shorter bond length (H-Cl= 1.234 A & ring;) under C46. The confined HCl under C52 results in high intensity (57.75 units) for stretching mode 2859.36 cm-1 with high polarity prediction but on the other hand confined HCl dimer under C44 shows the highest stretching mode in the frequency range 3000 cm-1 with compressed bond length (H-Cl = 1.254 A & ring;) which acts as an unsaturated system. For HCl, under the confinement of C52, the bond length increases up to 1.292 A & ring; which is quite greater than the experimental value of free HCl (1.275 A & ring;). Exclusively the band gap energies of both the clusters (HCl)n=1-2 are also considered to study H-bond connectivity with carbon atoms by using their (HOMO-LUMO).
Over the years, nanotechnology has been utilized in the agricultural sector for sustainable production. The application of endophytes-mediated silver nanoparticles is gaining momentum in various sectors including agriculture. This current study aims to evaluate the impact of endophytic bacteria-mediated silver nanoparticles in promoting plant growth and improving biochemical constituents in Vigna radiata by using the nanopriming method. Bacillus cereus isolated from the flower of Nyctanthes arbor-tristis was identified and utilized for silver nanoparticles synthesis, this is called Bacillus cereus silver nanoparticles (BC-AgNPs). Synthesis of silver nanoparticles was confirmed by colour change and characterization by UV-Vis spectrophotometer, FESEM, FTIR, DLS, and EDAX. Seeds of Vigna radiata were treated with different concentrations (1 ppm, 2.5 ppm, 5 ppm and 10 ppm) of BC-AgNPs. Plant growth was enhanced at a concentration of 5 ppm of BC-AgNPs. In addition, an increase in photosynthetic pigments, protein, and carbohydrate was observed in the nanoprimed seedlings when compared to that of the control. Also, the ability of the synthesized particles to inhibit the growth of phytopathogenic fungi Penicillium janthinellum was examined. The results suggest that silver nanoparticles synthesized via endophytic bacteria can be utilized as dual-function agent in sustainable agriculture-both as a plant growth enhancer and as a biocontrol agent.
This work employs density functional theory (DFT) to explore the interactions between the anticancer agent (3-Lapachone ((3-Lap) and pristine B40 fullerenes as well as their potassium-and magnesium-encapsulated counterparts (K@B40 and Mg@B40). The results reveal significant interactions affecting the electronic structures of these molecules, with stable complexes formed and a notable reduction in energy gaps, indicating effective (3-Lap adsorption. The (3-Lap drug exhibits stronger binding to metal-encapsulated fullerenes than to pristine B40 in both aqueous and gas environments, with binding energies in water of approximately-5.2 kcal/mol for B40, -82.9 kcal/mol for Mg@B40, and-55.7 kcal/mol for K@B40. In aqueous media, the dipole moments of encapsulated complexes rise to nearly twice their gas-phase values. To complement the electronic structure insights, we employ Quantum Theory of Atoms in Molecules (QTAIM) to study the electron densities and their Laplacians, and Natural Bond Orbital (NBO) analysis to evaluate donor-acceptor interactions and charge redistribution within these complexes. Despite the strong adsorption energies, the interaction weakens in acidic conditions, facilitating drug release. These findings suggest that B40 and its metal-encapsulated derivatives are promising nanocarriers for (3-Lap delivery, combining strong binding with controlled release potential in biological environments.
To maximize performance and profitability, power markets may use a variety of trading methods and operational techniques for multi-type energy storage systems. In accordance with the capacity and structure of the market, these systems may take part in energy markets, supplementary services markets, along with local or P2P marketplaces. With the use of pricing signals and demand estimates, operational strategies aim to maximize income through intelligently charging and releasing the various storage kinds. Regional distribution networks that rely on distribution network operators can now quantitatively determine their energy storage supply and demand with the help of this study's suggested approach for determining Hybrid Golden Flower Pollination (HGFP) Method energy storage action deviations. This method will be crucial for their future participation in market trading. Second, taking into account the economic advantages for all parties involved, the study created a pricing mechanism that incorporates a valley compensation mechanism to encourage autonomous and active user participation using the SARSA Deep Learning technique. The trading mechanism relies on combinatorial auctions and accommodates various types of market participants. Taking into consideration the variations in HGFP energy storage action when different regional networks are involved, numerical simulations were run to confirm the trading mechanism's practicality and rationale.
The temperature of the contacts is a crucial signal for successfully identifying thermal failures in GIS (Gas Insulated Switchgear) disconnects switches, which may cause dangerous electrical mishaps. This highlights the critical need of doing temperature field computations for GIS. In any electrical system, gas insulated switchgear (GIS) plays an essential role. The insulating gas's efficacy and the device's lifespan would be diminished if the conductor on the GIS busbar were to overheat. Subsequently, we optimize the busbar's output by adjusting its center distance, conductor thickness, and rotation angle. Gas insulated switchgear and gas insulated transmission lines are essential components of any reliable electrical system. For long-distance, high-power transmission, GILs are progressively displacing traditional overhead lines because to their lower overall cost and improved efficiency in space usage and power transfer. Studying GILs' power loss with temperature profiles is important for their dependable functioning and their design optimization ease. Variations in conductor thickness accounted for over 70% of the variation in maximum temperature as well as power loss, according to the research. Combining the aforementioned methods (A1, B5, C5) allows for a decrease in GIS thermal load and energy usage. Also, following structural adjustment, the study demonstrates that SF6 gas maintains its exceptional insulating ability. This is proven by calculating the gas breakdown margin. Finally, this work adds to our knowledge of how threephase GIS busbars conduct heat, which is useful for optimizing and designing these systems. The skin effect of current is a major contributor to the temperature of gas insulated bus bars, which are the result of linked multiphysics fields including fluid, thermal, and eddy current. This study builds a 3D model of a GIS bus bar and uses additional fine grids to account for the skin impact of current in order to get an accurate loss value. The three-phase GIS bus bar's temperature field distribution is predicted using the finite element approach in conjunction with fluid analysis. The study shows that multi-physics field coupling simulations are useful for enhancing the performance and reliability of power system components, particularly GILs, by looking at the temperature field of 500kV GIS circuit breakers. Doing so improves the efficiency and dependability of the power system's components. The primary finding of this study is that three-phase GIS busbars should be designed with optimal heat transfer and other factors in mind. A 500 kV GIS disconnect switch is modeled using a multi the physics simulation in this article. To begin, the distribution of losses is determined by electromagnetic modeling calculations. The distributions of both the temperature and the flow field are then obtained by coupling the computed losses to the fluid field's temperature as heat sources. In anomalous contact situations, this is used as a basis for additional temperature field investigation for varied contact resistance values. According to the findings, the GIS disconnect switch's top contacts are where the majority of the temperature spikes occur. When there is insufficient contact, the maximum temperature is 45. 23 percent greater than when everything is running well. For precise analysis of the temperature field of a 500kV GIS circuit breaker, multi-physics field coupling simulation is essential.
This study investigates the seasonal variability of meteorological feature importance in forecasting Global Horizontal Irradiance (GHI) using machine learning (ML) and deep learning models. High-resolution solar and meteorological data from NREL's NSRDB (24.25 degrees N, 45.34 degrees E, 740 m) were seasonally partitioned into winter, spring, summer, and autumn. Feature selection was conducted using Pearson correlation (threshold 0.25), followed by dimensionality reduction through Principal Component Analysis (PCA). Six ML models XGBoost, LightGBM, Random Forest, SVR, MLP, and LSTM were trained on the processed datasets, and SHAP analysis was used to interpret feature contributions. The results revealed that clear-sky irradiance parameters (GHI, DNI, DHI) consistently dominated GHI prediction (correlation >0.95; SHAP >10(-)& sup1;), while features like temperature and relative humidity varied across seasons. Wind direction, though weakly correlated, showed increased influence in winter. PCA enhanced model stability in spring and winter but slightly reduced accuracy during periods of high irradiance variability. Overall, XGBoost and Random Forest models provided the most accurate and reliable forecasts across seasons.
As patterns of energy usage become more complicated, smart, data-driven strategies are needed for effective forecasting and management. In order to optimise decision support systems in the energy industry, this study suggests a hybrid data mining framework that combines Extreme Gradient Boosting (XGBoost) with K-Means clustering. The model is intended to increase the precision and interpretability of energy usage forecasts while detecting discrete consumption behaviour clusters by utilising the publicly accessible UCI Individual Household Electric Power Consumption dataset. The suggested XGBoost + K-Means model performs noticeably better than conventional models like Linear Regression, Decision Tree, and Random Forest, according to a comparative analysis. It achieves a high R2 score of 0.91, a mean absolute error (MAE) of 39.7 Wh, and aroot mean square error (RMSE) of 49.6 Wh. Furthermore, evaluation criteria including F1-score, precision, and recall attest to the model's resilience and appropriateness for real-time applications. These results demonstrate how hybrid machine learning techniques can convert energy data into useful insights, which will ultimately help develop more intelligent and sustainable energy management plans.
The rapid growth of the photovoltaic device market is imperative for meeting global sustainability goals, such as reducing chlorofluorocarbon emissions to improve air quality, fulfilling the increasing public energy demands, and ultimately lowering the cost of electricity production. The development and application of advanced energy materials are gaining significant attention within the scientific and industrial communities. In this context, recent research has focused on exploring various photophysical mechanisms that can be integrated into photovoltaic devices to achieve conversion efficiencies theoretically surpassing the Shockley-Queisser limit. This limit, which represents the maximum theoretical efficiency for single-junction solar cells, has long been considered a fundamental constraint. However, innovative strategies such as multi-junction architectures, down-conversion and up-conversion layers, hot carrier extraction, and plasmonic enhancements are being investigated to overcome these limitations. It is noteworthy that approximately 55% of incident photon energy is lost, predominantly due to sub-bandgap losses, where photons have insufficient energy to excite electrons across the bandgap, and thermalization losses, where excess photon energy above the bandgap is dissipated as heat. Addressing these intrinsic loss mechanisms is crucial for the development of next-generation photovoltaic technologies capable of delivering higher efficiencies and supporting a sustainable energy future.
This paper presents an innovative Edge AI framework to detect nonlinear energy usage patterns in real-time with the application to Smart Grid infrastructures. Designed for use on Edge devices where there is limited processing power but required to be highly analytic error-free, the proposed framework is lightweight. The authors developed GBOCLE-Energy, an efficient Anomaly Detector Model based on advanced Gradient Boosting methods specifically for low latency and real-time energy consumption data analysis. This model uses compressed forms of Light Gradient Boosting and One-Class SVM Algorithms to discover temporal, contextual, and relational anomalies across multiple Nodes on the Smart Grid network. Additionally, three techniques, Simplified Terrestrial Analysis, Adaptive Isolation-based Scoring, and Lightweight Graph-Based Neighbour Mechanisms, were used to further enable the detection of nonlinear relationships and interactions among the different components within the grid. The model uses Compact Nonlinear Indicators (e.g., Consumption Dev Index, Device Influence Vector and Reduced-Order Chaotic Metrics) as Analytical Features and Hidden Indicators for Anomalous Energy Usage. The experimental findings indicate that the suggested ensemble model has excellent detection accuracy (0.984), precision (0.974), sensitivity (0.975), specificity (0.965), F1-score (0.975), and AUC (0.984) while minimizing the computational and memory requirements for edge deployment. These findings support the conclusion that optimized edge-based detection is an efficient and feasible solution for detecting energy consumption anomalies and therefore offers the potential to enable predictive management and enhance operational performance in the Smart Grid environment.
