Karpagam Institute of Technology, Coimbatore

This scientific research examines the synergy effect of boron carbide (B4C)/Titanium diboride (TiB2) hybrid reinforcements on the microstructure, mechanical, and tribological behaviour of the AA336 alloy. Hybrid composites were fabricated by stir casting using a TiB2 constant value of 2.5 wt.

Zinc doped nickel molybdate (Zn-NiMoO₄) nanoparticles were synthesized via a facile hydrothermal method at a low reaction temperature of 160 °C. The resulting material was comprehensively analyzed to assess its structural, optical, and electrochemical properties. X-ray diffraction (XRD) analysis confirmed the prepared molybdates were monoclinic crystal structure with average crystallite size increased from 26 to 33 nm. Fourier transform infrared spectroscopy (FTIR) detected characteristic metal-oxide stretching vibrations in the range of 400 to 800 cm− 1. Field emission scanning electron microscopy (FESEM) and high-resolution transmission electron microscopy (HRTEM) analyses confirmed the formation of nanoparticles with a distinct cubic morphology. X-ray photoelectron spectroscopy (XPS) analysis identified the oxidation states and confirmed the elemental composition, including the presence of Zn2+, Ni2+, and Mo6+. Nitrogen adsorption-desorption measurements indicated a mesoporous structure with a high specific surface area of 112 m²/g. Furthermore, the optical band gap decreased from 2.90 eV for pure NiMoO4 to 2.75 eV with 2

This study introduces green carbon fibers (GCF), a novel adsorbent material made from banana pseudostem for the purpose of environmentally friendly wastewater treatment. GCF effectively removes lead (Pb2+) from water by virtue of its high porosity, thermal stability, and surface activity. Distilled from agricultural byproducts, this material stands out from the ordinary activated carbons because to its ability to retain the original fiber shape and hierarchical pore patterns. The experimental conditions included a pH of 5.5, temperatures between 15 and 45℃, lead ion concentrations of 60 and 100 mg/L, and adsorbent doses ranging from 30 to 70 mg. Zeta potential studies, energy dispersive spectroscopy (EDS), thermogravimetric analysis (TGA), and zeta potential all confirmed that the GCF structure was quite porous and that the surface chemistry was rather beneficial. With maximum capacity of 79.62 mg/g, adsorption kinetics pursued a pseudo-second-order (PSO) model, indicating chemisorption (R2 = 0.993). Due to heterogeneous multilayer adsorption, the Freundlich isotherm model yielded a R² value of 0.9343. With an R2 of 0.9676 and an MSE of 54.981, the MLP Regressor outperformed all other machine learning models, demonstrating that adsorbent weight is the most important factor, whereas the RSM model showed strong prediction reliability with a Reduced 2FI model. These results demonstrate that it is possible to convert agricultural waste into effective adsorbents, which would be a green and cost-effective way to remove heavy metals from wastewater.

Pure and (0.02, 0.04 and 0.06 M) Cu2+doped NiO nanoparticles (NPs) were synthesized via hydrothermal method and characterized for supercapacitor and photocatalytic applications. XRD analysis confirmed the cubic NiO phase with slight lattice contraction and reduced crystallite size upon Cu2+ doping. FESEM images showed spherical, agglomerated sheet-like morphology, with decreasing particle size as doping increased, while EDAX confirmed the presence of Ni, O, and Cu without impurities. UV-Vis DRS confirmed a direct transition with the bandgap decreasing from 3.15 eV to 2.67 eV with increasing doping concentration. XPS confirmed Cu2+ incorporation in NiO lattice with mixed Ni2+/Cu2+ states, oxygen vacancies, and surface hydroxyl groups. BET analysis showed a mesoporous structure with reduced surface area after Cu2+ doping, while VSM confirmed weak ferromagnetism due to defect and oxygen vacancy-induced spin alignment. Electrochemical evaluations showed that 0.06 M Cu2+ doped NiO NPs exhibit enhanced pseudocapacitive behavior due to additional Cu^2+/Cu^3+ and Ni^2+/Ni^3+ redox reactions and delivered higher specific capacitance of 421.85 F g− 1 (CV at 10 mV s− 1) and 351.13 F g− 1 (GCD at 1 A g− 1), while EIS showed a reduced charge transfer resistance compared to pure NiO. The (0.06 M) Cu2+ doped NiO NPs demonstrated superior photocatalytic degradation of Congo Red (CR) dye under visible sunlight, achieving 78.35

Background: Histopathological images are crucial for breast cancer diagnosis but suffer from variability in staining and imaging conditions, leading to poor feature extraction and misclassification. Traditional approaches often fail to capture both local and global tissue structures, impacting diagnostic accuracy. Methods: To address these challenges, propose an Adaptive Stain Augmentation method that integrates Macenko's colour normalization and colour jittering to standardize and diversify image appearance. A Canny Multi-Scale Patch Extraction technique is applied using Canny edge detection and high-resolution segmentation to identify key tissue regions. For classification, a Vision-Efficient Gradient-weighted Class Activation Mapping (Grad-CAM) Network is introduced, combining EfficientNet and Vision Transformer (ViT) to capture fine-grained and contextual features. Multi-scale feature fusion enhances representation, while focal loss mitigates class imbalance. Grad-CAM provides interpretability by highlighting influential regions in model decisions. Results: The proposed framework achieves 99.5% accuracy, precision, and recall, significantly outperforming existing methods in breast cancer detection. The combination of advanced augmentation, multi-scale feature extraction, and attention-based classification ensures superior performance and robustness. Conclusion: This research presents a novel, interpretable, and high-accuracy framework for breast cancer detection using histopathological images. By integrating adaptive augmentation, multi-scale processing, and a ViT-based classification, the model enhances reliability and clinical applicability for early diagnosis.