This research deals with a triple-layered ZnO-SiO₂@Ag nanohybrid, which has been convincingly demonstrated to photo-degrade methylene blue (MB) and malachite green (MG) dyes under Ultra Violet radiation. The decoration of SiO₂ and Ag onto ZnO has upgraded surface area and improved charge separation efficiency by restricting electron-hole recombination, as verified by various characterization studies. The functionality, crystallite size, morphology, elemental constituency, band gap energy, recombination of photogenerated electrons and holes, degraded daughter fragments of dye, and surface area of the composite were all considered through characterization using FT-IR, XRD, FESEM, EDX, UV-DRS, PL, LCMS, and BET. To verify the photocatalytic activity, the effect of catalytic dosage, dye concentration, stirring speed, contact time, and pH was methodically assessed. The material afforded almost 98
Multi-drug-resistant (MDR) microbial pathogens pose significant challenges for antibiotic therapy and wound healing. Infections caused by MDR microorganisms often lead to prolonged hospitalisation and incur high treatment costs. In this study, we fabricated chitosan biopolymer-coated titanium/coumarin nanoparticles via emulsification. This study indicates that the combination of titanium and coumarin showed a synergistic antibacterial effect, with a zone of inhibition measuring 23 mm against E. coli and 20 mm against S. aureus. Additionally, plate count assays demonstrated that TiO2/CU@CS nanoparticles can eliminate approximately 90
Waterborne polyurethane (WBPU) coatings represent eco-friendly alternatives to solvent-based systems due to their low volatile organic compounds (VOCs) emissions and reduced toxicity. However, their inherent hydrophilicity limits water repellence, thereby restricting their use in demanding environments. In this research, pristine calcium carbonate (CaCO3), sodium stearate-modified CaCO3 (SS-CaCO3), and a mixture of SS-CaCO3 with silica nanoparticles (SiO2NPs) improved the hydrophobicity of WBPU. Four formulations, namely, pristine WBPU (P1), WBPU+CaCO3 (P2), WBPU + SS-CaCO3 (P3), and P3 + SiO2NPs (P4), were prepared and applied onto mild steel substrates. The composite formation was confirmed using Fourier-transform infrared (FTIR) spectroscopy, with the intensity of the O-H band in P4 being lower. According to the X-ray diffraction (XRD) study, the dispersion of the SiO2NPs in P4 was improved, which reduced the crystallite size to 22.38 nm. The scanning electron microscope (SEM) images indicated that the surface roughness of P4 increased with the enrichment of SiO2NPs on the surface of the coating. The spectroscopic evidence of the energy-dispersive X-ray (EDX) spectroscopy revealed the corresponding elemental distribution and a higher proportion of oxygen. The measurements of water contact angle (WCA) showed that P4 had the best hydrophobicity (105.08 ± 0.74°), which can be explained by the fact that P4 possessed the best surface roughness produced by the nanoparticles. These results indicate that the presence of SS-CaCO3 and SiO2NPs enhances the performance and water repellence of WBPU, which further improves its prospects as an environmentally friendly coating.
We report the green synthesis of mixed-phase indium sulfide nano powder via a cost-effective wet chemical route at a temperature of 100 °C, employing minimal reagents. X-ray diffraction analysis reveals the coexistence of tetragonal In₂S₃ and monoclinic In₆S₇ phases, with an average crystallite size of 70.98 nm. Scanning electron microscopy confirms the microcrystalline morphology, while X-ray photoelectron spectroscopy excludes the presence of In–O bonds associated with In(OH)₃ impurities. Brunauer–Emmett–Teller analysis demonstrates meso-porosity with a pore size of 3.39 nm and a specific surface area of 45.59 m2/g. The mesoporous architecture and optimal band gap render mixed-phase indium sulfide nano powder highly effective for photocatalytic degradation of structurally related organic dyes, Rhodamine B and Sulforhodamine B. Comparative studies with single-phase indium sulfide samples synthesized at lower bath temperatures (30 0C and 70 0C) yielded degradation efficiencies of 14.3
Perovskite types of photovoltaic sources have become the primary candidates for next-generation photovoltaic sources thanks to their high-quality optoelectronic characteristics and their fast-enhancing power-conversion parameters. Nonetheless, the computational space of perovskites is quite large and first-principles is expensive, and this makes systematic optimization of materials a challenging problem. This paper presents a unified multiscale system that entails the implementation of density functional theory (DFT), machine learning (ML), and device-level simulation to hasten the search and development of high-performance perovskite solar cell materials. Physically significant descriptors are generated through DFT calculations such as structural parameters, electronic bandgaps, formation energies, and properties of defect-related characteristics. The descriptors are utilized to train the supervised ML models that can predict the important material and photovoltaic performance indicators with high accuracy at a rapid rate. Predictions of the ML are then combined with practicing simulations of the device to assess open-circuit voltage, short-circuit current density, fill factor, and power conversion efficiency. The findings show that there are good correlations among defect tolerance, bandgap optimization, and photovoltaic performance with defect suppression being a leading channel of increasing the efficiency. The current DFT-ML scheme consumes much less computational power; however, still has physical interpretability, and can offer efficient and predictive solution to perovskite material screening as well as solar cell design scaling.
