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To evaluate the role played by the substrates on the change and the actual difference of the studied samples on their structural, morphological, optical and photocatalytic properties, thin layers of zinc oxide grafted with magnesium were prepared with the same number of layers, doping ratio and experimental conditions with the use of two types of porous ceramic substrates and glassy. The XRD analysis detected the polycrystalline structure with wurtzite-type for all synthesized samples. The grain size was found to vary between 39 and 21 nm for DD3Z and 23 nm to 17 for glass. UV–visible absorbance data indicated that all MZO films absorb visible light at around 410 nm. In addition, a blue shift toward shorter wavelengths side was noted with increasing magnesium content up to 4 wt%, while the band gap showed an increasing trend achieving 3.07 eV for ceramics substrate. SEM analysis showed that the doping greatly affected the morphology of ZnO samples. Based on the results shown by the photocatalytic activities of orange II, the doping with magnesium gave a significant improvement to the samples with a ceramic substrate compared to the samples with a glass substrate. To reveal the mechanism of photolysis Hole/radical scavengers were used. It was found that the addition of Mg–ZnO networks increases the adsorption of hydroxyl ions on the surface and thus acts as a trap site leading to the reduction of hole/electron pair and thus increasing the activity and enhancing photodegradation.

The significant natural energy sources for reducing the global usage of fossil fuels are renewable energy (RE) sources. Solar energy is a crucial and reliable RE source. Site selection for solar photovoltaic (PV) farms is a crucial issue in terms of spatial planning and RE policies. This study adopts a Geographic Information System (GIS)-based Multi-Influencing Factor (MIF) technique to enhance the precision of identifying and delineating optimal locations for solar PV farms. The choice of GIS and MIF is motivated by their ability to integrate diverse influencing factors, facilitating a holistic analysis of spatial data. The selected influencing factors include solar radiation, wind speed, Land Surface Temperature (LST), relative humidity, vegetation, elevation, land use, Euclidean distance from roads, and aspect. The optimal sites of solar PV power plant delineated revealed that ‘very low’ suitability of site covering 4.866% of the study area, ‘low’ suitability of site 13.190%, ‘moderate’ suitability of site 31.640%, ‘good’ suitability of site 32.347%, and ‘very good’ suitability of site for solar PV power plant encompassing 17.957% of the study area. The sensitivity analysis results show that the solar radiation, relative humidity, and elevation are the most effective on the accuracy of the prediction. The validation of the results shows the accuracy of solar PV power plant prediction using MIF technique in the study area was 81.80%. The integration of GIS and MIF not only enhances the accuracy of site suitability assessment but also provides a practical implementation strategy. This research offers valuable insights for renewable energy policymakers, urban planners, and other stakeholders seeking to identify and develop optimal locations for solar energy power farms in their respective regions.

This study aimed to classifying wheat genotypes using support vector machines (SVMs) improved with ensemble algorithms and optimization techniques. Utilizing data from 302 wheat genotypes and 14 morphological attributes to evaluate six SVM kernels: linear, radial basis function (RBF), sigmoid, and polynomial degrees 1–3. Various optimization methods, including grid search, random search, genetic algorithms, differential evolution, and particle swarm optimization, were used. The radial basis function kernel achieves the highest accuracy at 93.2%, and the weighted accuracy ensemble further improves it to 94.9%. This study shows the effectiveness of these methods in agricultural research and crop improvement. Notably, optimization-based SVM classification, particularly with particle swarm optimization, saw a significant 1.7% accuracy gain in the test set, reaching 94.9% accuracy. These findings underscore the efficacy of RBF kernels and optimization techniques in improving wheat genotype classification accuracy and highlight the potential of SVMs in agricultural research and crop improvement endeavors.

Nutrient management plays a crucial role in optimizing crop yield while ensuring soil sustainability. However, there is a need to explore balanced approaches that reduce chemical fertilizer dependency without compromising productivity. This study investigates the effects of nutrient regime and microbial inoculant on the soil enzymatic activities, microbial populations, soil nutrient dynamics and yield of Indian mustard ( Brassica juncea L.). The experiment, conducted over two cropping seasons (2021–22 and 2022–23) at the Agricultural Research Farm, Banaras Hindu University, employed a split-plot design with three main plots (Nutrient regime) and six sub-plots (Microbial inoculant). Results demonstrated that the application of 100% Recommended Dose of Fertilizer (RDF) significantly enhanced growth parameters, yield attributes, and overall productivity of mustard. The integration of microbial inoculants, particularly NPK consortia combined with Zinc Solubilizing Bacteria (ZSB), showed remarkable improvements in soil microbial populations, enzyme activities, and nutrient availability. Although 75% RDF resulted in slightly lower yield and growth compared to 100% RDF, it was not far behind, and when combined with NPK consortia and ZSB, it emerged as a promising sustainable alternative. Principal Component Analysis (PCA) further confirmed the strong positive correlations between yield attributes, soil nutrient availability, and enzyme activities.

Microplastics, widespread environmental pollutants, have received considerable attention because of their distribution and possible effects on human health and ecosystems. This study thoroughly examines current progress in identifying, detecting, and understanding the significance of microplastics in different environmental contexts. The paper provides an analysis of the dispersion and origins of microplastics, uncovering areas of high concentration and trends of buildup with key findings indicating microplastic accumulation of up to 2 million particles/km 2 in some regions. The intricate relationships between microplastics and biological systems are such that their toxicological impacts on human health and their ecological ramifications can be easily observed. The amalgamation of existing research highlights the pressing need for efficient mitigation measures and regulations to tackle the escalating menace of microplastics. Strategies like biodegradable polymer development, wastewater filtration technologies, and global policy interventions are being sought to control this pollution. This study aims to thoroughly comprehend microplastic contamination, promoting well-informed choices and initiatives to protect the environment and public health.

Copper–semicarbazone ligands have been extensively investigated for several medicinal applications. In this contribution, a novel copper(II) complex with a pyridoxal–semicarbazone ligand, [Cu(PLSC)Cl(H2O)](NO3)(H2O), was synthesized and characterized by X-ray crystallography, elemental analysis, UV-VIS, and FTIR spectroscopies. The stabilization interactions within the structure were assessed using the Hirshfeld surface analysis. The crystallographic structure was optimized at the B3LYP/6-311++G(d,p)(H,C,N,O)/LanL2DZ(Cu) level of theory. A comparison between the experimental and theoretical bond lengths and angles was undertaken to verify the applicability of the selected level of theory. The obtained high correlation coefficients and low mean absolute errors confirmed that the optimized structure is suitable for further investigating the interactions between donor atoms and copper, along with the interactions between species in a neutral complex, using the Quantum Theory of Atoms in Molecules approach. The electrostatic potential surface map was used to reveal distinct charge distributions. The experimental and calculated FTIR spectra were compared, and the most prominent bands were assigned. The interactions with human serum albumin (HSA) were assessed by spectrofluorometric titration. The spontaneity of the process was proven, and the thermodynamic parameters of binding were calculated. Molecular docking analysis identified the most probable binding site, providing additional insight into the nature of the interactions.

This work examines the effect of gadolinium (Gd) promotion on nickel-based SBA-16 catalysts for the dry reforming of methane (DRM), with the goal of improving syngas production by optimizing catalyst composition and operating conditions. Catalysts with varying Gd loadings (0.5–3 wt.%) were synthesised using co-impregnation. XRD, N2 physisorption, FTIR, XPS, and H2-TPR–CO2-TPD–H2-TPR were used to examine the structural features, textural properties, surface composition, and redox behaviour of the catalysts. XPS indicated formation of enhanced metal–support interactions, while initial and post-treatment H2–TPR analyses showed that moderate Gd loadings (1–2 wt.%) maintained a balanced distribution of reducible Ni species. The catalysts were tested for DRM performance at 800 °C and a gas hourly space velocity (GHSV) of 42,000 mL g−1 h−1. 1–2 wt.% Gd-promoted catalysts achieved the highest H2 (~67%) and CO yield (~76%). Response surface methodology (RSM) was used to identify optimal reaction conditions for maximum H2 yield. RSM predicted 848.9 °C temperature, 31,283 mL g−1 h−1 GHSV, and a CH4/CO2 ratio of 0.61 as optimal, predicting a H2 yield of 96.64%, which closely matched the experimental value of H2 yield (96.66%). The 5Ni–2Gd/SBA-16 catalyst exhibited minimal coke deposition, primarily of a graphitic character, as evidenced by TGA–DSC and Raman analyses. These results demonstrate the synergy between catalyst design and process optimization in maximizing DRM efficiency.

2-Hydroxy-3-methoxybenzaldehyde semicarbazone (HMBS) is a multidentate ligand with interesting coordination behavior that depends on the central metal ion and the overall complex geometry. In this contribution, the structural characteristics of five HMBS-containing complexes with different metal ions (Dy, Er, Ni, and V) were investigated. Four binuclear and one mononuclear complex were selected from the Cambridge Structural Database. The crystallographic structures and intermolecular interactions in the solid state were analyzed, and the effect of central metal ions was elucidated. The different contributions of the most numerous contacts were explained by examining additional ligands in the structure. Density functional theory (DFT) optimizations were performed for the selected complexes, and the applicability of different computational methods was discussed. The Quantum Theory of Atoms in Molecules (QTAIMs) approach was employed to identify and quantify interactions in nickel and vanadium complexes, highlighting the role of weak intermolecular interactions between ligands in stabilizing the overall structure. Molecular docking studies of the interaction between these complexes and Human Serum Albumin (HSA) demonstrated that all compounds bind within the active pocket of the protein. The overall size and presence of aromatic rings emerged as key factors in the formation of stabilizing interactions.

Water pollution stands as a critical worldwide concern, bearing extensive repercussions that extend to human health and the natural ecosystem. The sources of water pollution can be diverse, arising from natural processes and human activities and the pollutants may range from chemical and biological agents to physical and radiological contaminants. The contamination of water disrupts the natural functioning of the system, leading to both immediate and prolonged health problems. Various technologies and procedures, ranging from conventional to advanced, have been developed to eliminate water impurities, with the choice depending on the type and level of contamination. Assessing risks is a crucial element in guaranteeing the safety of drinking water. Till now, research is continuing the removal of contaminates for the sake of supplying safe drinking water. The study examined physical, inorganic, organic, biological and radiological contaminants in drinking water. It looked at where these contaminants come from, their characteristics, the impact they have and successful methods used in real-world situations to clean the contaminated water. Risk assessment methodologies associated with the use of unsafe drinking water as future directives are also taken into consideration in the present study for the benefit of public concern. The manuscript introduces a comprehensive study on water pollution, focusing on assessing and mitigating risks associated with physical, inorganic, organic, biological and radiological contaminants in drinking water, with a novel emphasis on future directives and sustainable solutions for public safety.

The coordination chemistry, structural characterization, and biomolecular interactions of europium(III) and iron(III) complexes with the pyridoxal-semicarbazone (PLSC) ligand were thoroughly examined using experimental and computational approaches. Single-crystal X-ray diffraction revealed that the europium complex exhibits a nine-coordinate geometry with one protonated and one deprotonated PLSC ligand and nitrato and aqua ligands. In contrast, the iron complex adopts a six-coordinate structure featuring a monoprotonated PLSC, two chlorido, and an aqua ligand. Hirshfeld surface analysis confirmed the significance of intermolecular contacts in stabilizing the crystal lattice. Theoretical geometry optimizations using DFT methods demonstrated excellent agreement with experimental bond lengths and angles, thereby validating the reliability of the chosen computational levels for subsequent quantum chemical analyses. Quantum Theory of Atoms in Molecules (QTAIM) analysis was employed to investigate the nature of metal–ligand interactions, with variations based on the identity of the donor atom and the ligand’s protonation state. The biological potential of the complexes was evaluated through spectrofluorimetric titration and molecular docking. Eu-PLSC displayed stronger binding to human serum albumin (HSA), while Fe-PLSC showed higher affinity for calf thymus DNA (CT-DNA), driven by intercalation. Thermodynamic data confirmed spontaneous and enthalpy-driven interactions. These findings support using PLSC-based metal complexes as promising candidates for future biomedical applications, particularly in drug delivery and DNA targeting.