Groundwater quality for irrigation is increasingly threatened by overextraction, salinization and agricultural intensification, yet existing sodium hazard prediction methods often fail to capture complex temporal and relational dynamics. This study proposes a Dual-Channel Temporal Graph Attention Network (DC-TGAN), integrated with advanced preprocessing and hybrid feature selection, to enhance the prediction of farming water quality characteristics-Kelly's ratio (KR), residual sodium carbonate (RSC), sodium adsorption ratio (SAR), and %sodium (%Na)-in Southern Rajasthan, India's Pratapgarh district. The approach's originality is found in its ability to simultaneously model multivariate spatiotemporal dependencies and optimize convergence through Quokka Swarm Optimization. The model was found to best predict groundwater when using 10 years (2010-2020) of groundwater data, with correlation coefficients (R-2) near to 1.0 and testing RMSE of 0.04 in KR and 0.16 in SAR. On the whole, the DC-TGAN was superior to the models of random forest, ANN, LSTM and WDO-ANN with an accuracy of 99.8%, F1-score of 99.7%, and the T of 0.02 s. These findings indicate that the model can be viewed as a powerful and scalable instrument of timely identifying sodium hazards to contribute to sustainable groundwater management and irrigation planning in semiarid areas.
This study investigates the parametric optimization of friction stir welding (FSW) for joining AA1100 alloys using the (MOGOA) multi-objective grasshopper optimization algorithm the non-dominated sorting genetic algorithm II (NSGA-II). Regression equations was employed to determine to forecast hardness and tensile strength of frictional stir welding joints, whereas tensile testing and hardness measurements were conducted to acquire the empirical evidence. The SECA–COCOSO framework, together with the empirical model, was utilized to structure experimental methodology, while empirical results and the adequacy of the predicted were evaluated through a systematic examination of difference. Five distinct instrument categories was evaluated for various amounts of input parametric rates. Optimum input parameters included a tool rotation speed of 1300 rpm, a welding speed of 60 mm/min, an axial force of 5.5 kN, and a cylindrical threaded tool pin, which demonstrated the maximum. The axial force emerged as the predominant input parameter affecting microhardness and the output tensile strength, succeeded by the tool pin shape and welding speed. NSGA-II demonstrated superior optimization relative to MOGOA. Fractography study revealed a ductile fracture in sample ‘2’, which had the highest UTS of 173.36 MPa and an improved Vickers hardness of 99.13 HV, whereas maximum hardness recorded were 93.75 HV in samples 30 throughout the empirical experiments.
This work reports the green synthesis of mesoporous anatase-phase TiO₂ nanosheets using Rosmarinus officinalis leaf extract as a bio-reducing and stabilizing agent. XRD analysis suggest that nanocrystalline and tetragonal with anatase phase. The SEM images revealed well-defined nanosheet structures with a relatively uniform distribution. The lateral dimensions of the TiO₂ nanosheets ranged from approximately 80 to 150 nm, with an average diameter of 115 ± 15 nm. Their multifunctional potential was systematically evaluated in dye-sensitized solar cells (DSSCs), photocatalysis, and electrochemical energy storage. As a DSSC photoanode, the TiO₂ nanosheets achieved a photo conversion efficiency (PCE) of 8.42 ± 0.05
Synthesis of silver nanoparticles using the leaf extract of Melaleuca alternifolia (M-AgNPs), as evidenced by various physicochemical characterizations, including UV-Visible spectroscopy (UV-Vis), Fourier-transform infrared spectroscopy (FT-IR), dynamic light scattering (DLS), X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray analysis (EDAX), high-resolution transmission electron microscopy (HR-TEM), and zeta potential analysis. The UV-Vis spectrum exhibited a characteristic absorption peak at 402 nm, confirming the formation of M-AgNPs. FT-IR analysis identified the presence of various functional groups associated with the silver nanoparticles. DLS measurements indicated a hydrodynamic size of 45.79 nm with a polydispersity index (PDI) of 0.335. Zeta potential analysis revealed a value of -21 mV, suggesting good stability of the nanoparticles. XRD analysis showed a crystalline size of 25.47 nm and confirmed the face-centered cubic (FCC) structure. SEM images revealed well-defined and uniformly dispersed nanoparticles. EDAX confirmed the presence of silver at the elemental level. HR-TEM analysis demonstrated that the actual size of the nanoparticles was approximately 10 nm. Antimicrobial studies demonstrated the effectiveness of the synthesized nanoparticles against both bacterial and fungal strains. The antioxidant activity of the M-AgNPs was measured at 41.17 µL/mL. Furthermore, cytotoxicity studies showed that the nanoparticles exhibited an IC50 value of 8.16 µg/mL against MCF-7 breast cancer cells. The synthesized M-AgNPs possess significant potential as therapeutic agents, particularly against MCF-7 cancer cells, and may serve as promising candidates for future medical applications.
Recent developments in carbon quantum dot (CQD)‐based detection technologies, with a focus on applications in food and environmental safety. CQDs, an emerging nanomaterial, are widely used to detect pollutants such as heavy metals, pesticides, microbial contaminants, and organic pollutants. The tremendous optical properties, biocompatibility, low toxicity, and tunable surface functionality of CQDs make them suitable for various real‐time monitoring. It also discusses the role of CQD‐based sensors in detecting contaminants in complex matrices, such as food and water, as well as in detecting contaminants using fluorescence, electrochemical, and upconversion emission methods. These sensors can address current field tasks, including expanding the detection field, improving cost‐effectiveness, and increasing accuracy. In addition, the synthesis of novel materials and the development of advanced, targeted sensors by blending CQDs with other nanomaterials are expected to enhance food safety control and risk assessment for environmental protection.
