Novel molybdenum (Mo)-doped nickel oxide (NiO) Nanoparticles (NPs) were synthesized by using a simple sonochemical methodology and the synthesized NPs were investigated for antioxidant, and antibacterial applications. The X-ray diffraction (XRD) analysis revealed that the crystal systems of rhombohedral (21.34 nm) and monoclinic (17.76 nm) were observed for pure NiO and Mo-doped NiO NPs respectively. The scanning electron microscopy (SEM) results show that the pure NiO NPs possess irregular spherical shape with an average particle size of 93.89 nm while the Mo-doped NiO NPs exhibit spherical morphology with an average particle size of 85.48 nm. The ultraviolet–visible (UV–Vis) spectrum further indicated that the pure and Mo-doped NiO NPs exhibited strong absorption band at the wavelengths of 365 and 349 nm, respectively. The free radical scavenging activity of NiO and Mo-doped NiO NPs was also investigated by utilizing several biochemical assays. The Mo-doped NiO NPs showed better antioxidant activity (84.2%) towards ABTS. + at 200 µg/mL in comparison to their pure counterpart which confirmed that not only antioxidant potency of the doped NPs was better than pure NPs but this efficacy was also concentration dependant as well. The NiO and Mo-doped NiO NPs were further evaluated for their antibacterial activity against gram-positive (Staphylococcus aureus and Bacillus subtilis) and gram-negative (Pseudomonas aeruginosa and Escherichia coli) bacterial strains. The Mo-doped NiO NPs displayed better antibacterial activity (25 mm) against E. coli in comparison to the pure NPs. The synthesized NPs exhibited excellent aptitude for multi-dimensional applications.

In this paper, the Levenberg–Marquardt (LM), Bayesian regularization (BR), resilient backpropagation (RP), gradient descent momentum (GDM), Broyden–Fletcher–Goldfarb–Shanno (BFGS), and scaled conjugate gradient (SCG) algorithms constructed using artificial neural networks (ANN) are applied to the problem of MPPT energy harvesting in solar photovoltaic (PV) systems for the purpose of creating a comparative evaluation of the performance of the six distinct algorithms. The goal of this analysis is to determine which of the six algorithms has the best overall performance. In the study, the performance of managing the training dataset is compared across the algorithms. The maximum power point tracking energy harvesting system is created using the environment of MATLAB or Simulink, and the produced model is examined using the artificial neural network toolkit. A total of 1000 datasets of solar irradiance, temperature, and voltage were used to train the suggested model. The data are split into three categories: training, validation, and testing. Eighty percent of the total data is used for training the model, and the remaining twenty percent is divided equally for testing and validation. According to the results, the regression values of LM, RP, BR, and BFGS are 1, whereas the regression values for SCG and GDM are less than 1. The gradient values for LM, RP, BFGS, SCG, BR, and GDM are 7.983 × 10⁻⁶, 0.033415, 1.0211 × 10⁻⁷, 0.14161, 0.00010493, and 11.485, respectively. Similarly, the performance values for these algorithms are 2.0816 × 10⁻¹⁰, 2.8668 × 10⁻⁶, 9.98 × 10⁻¹⁷, 0.052985, 1.583 × 10⁻⁷, and 0.15378. Overall, the results demonstrate that the LM and BFGS algorithms exhibit superior performance in terms of gradient and overall performance. The RP and BR algorithms also perform well across various metrics, while the SCG and GDM algorithms show comparatively less effectiveness in addressing the proposed problem. These findings provide valuable insights into the relative performance of the six evaluated algorithms for MPPT energy harvesting in solar PV systems.

Substantial improvement is needed in efficient and affordable decolorization and disinfection methods to solve the issues caused by dyes and harmful bacteria in water and wastewater. This work involves the photocatalytic degradation of methylene blue (MB) as well as gram-negative and gram-positive bacteria by cobalt-doped tin oxide (Co-SnO₂) nanoparticles (NPs) and Co-SnO₂/SGCN (sulfur-doped graphitic carbon nitride) nanocomposites (NCs) under sunlight. The coprecipitation approach was used to synthesize the photocatalysts. Maximum methylene blue (MB) photocatalytic degradation was seen with the 7% Co-SnO₂ NPs compared to other (1, 3, 5, and 9 wt.%) Co-SnO₂ NPs. The 7% Co-SnO₂ NPs were then homogenized with different amounts (10, 30, 50, and 70 weight %) of sulfur-doped graphitic carbon nitride (SGCN) to develop Co-SnO₂/SGCN heterostructures with the most significant degree of MB degradation. The synthesized samples were identified by modern characterization methods such as FT-IR, SEM, EDX, UV-visible, and XRD spectroscopies. The Co-SnO₂/50% SGCN composites showed a significant increase in MB degradation and degraded 96% of MB after 150 min of sunlight irradiation. Both gram-negative (E. coli) and gram-positive (B. subtiles) bacterial strains were subjected to antibacterial activity. All samples were shown to have vigorous antibacterial activity against gram-positive and gram-negative bacteria, but the Co-SnO₂/50% SGCN composites exhibited the maximum bactericidal action. Thus, the proposed NC is an efficient organic/inorganic photocatalyst that is recyclable and stable without lowering efficiency. Hence, Co-SnO₂/50% SGCNNC has the potential to be employed in water treatment as a dual-functional material that simultaneously removes organic pollutants and eradicates bacteria.

Organic pesticides are major sources of soil pollution in agricultural lands. Most of these pesticides are persistent and tend to bio accumulate in humans upon consumption of contaminated plants. In this study, we investigate different natural soil samples that were collected from agricultural lands. The samples revealed the presence of 18 pesticides that belong to four different groups including organochlorines (OCP), organophosphorus (OPP), carbamates (Carb), and pyrethroids (Pyrth). The photocatalytic degradation of the five most abundant pesticides was studied in the presence and absence of 1% TiO₂ or ZnO photocatalysts under UV irradiation at a wavelength of 306 nm. The five abundant pesticides were Atrazine (OCP), Chlorpyrifos methyl (OPP), Dimethoate (OPP), Heptachlor (OCP), and Methomyl (Carb). The results showed that photolysis of all pesticides was complete under UV radiation for irradiation times between 64–100 h. However, both photocatalysts enhanced photocatalytic degradation of the pesticides in comparison with photolysis. The pesticides were photocatalytically degraded completely within 20–24 h of irradiation. The TiO₂ photocatalyst showed higher activity compared to ZnO. The organochlorine heptachlor, which is very toxic and persistent, was completely degraded within 30 h using TiO₂ photocatalyst for the first time in soil. The mechanism of photocatalytic degradation of the pesticides was explained and the effects of different factors on the degradation process in the soil were discussed.

In this work, chitosan, a biopolymer was cross-linked using glutaraldehyde (Gl) and modified by natural apatite. The Cs-Gl@Fap composite was used to adsorb methyl orange from an aqueous solution. The materials were characterised by SEM-EDX, FTIR, XRD and TGA-DTG analysis confirming the successful synthesis of Cs-Gl@Fap. The effects of experimental variables on adsorption efficiency were examined. Adsorption studies showed that the maximum adsorption capacity was 225.55 mg/g at an equilibration time of 120 min. The pseudo-second-order rate expression supported data best, while the Langmuir equation could satisfactorily describe isotherm data. The thermodynamic constants of the adsorption process were evaluated. The results showed that the stabilisation by contact of methyl orange on Cs-Gl@Fap was endothermic and spontaneous. Additionally, when reused, the hybrid adsorbent remains at approximately 83% of its original capacity after five adsorption cycles.

An ultrasound-assisted probe sonication route effectively prepared pure CuO and two-dimensional CuO-ZnO nanocomposites (NCs) for different ratios of CuO and ZnO, and the experimental and theoretical methods investigated the structural, photocatalytic, and electrochemical properties. The XRD (X-ray diffraction) patterns revealed a crystallite size (D) range of 25 to 31 nm for pure CuO and CuO-ZnO NCs. According to calculations, the sample’s optical energy bandgap value (Eg) for the NCs is between 1.72 and 2.15 eV. Under UV light irradiation, the photocatalytic discoloration of pure CuO and CuO-ZnO NCs on fast blue (FB) dye was assessed. Under the influence of UV light, the CuO with 10% ZnO composite degrades 83.4% of the dye, which is greater than pure CuO and other NCs. The electrochemical properties of the prepared NCs materials have been studied using cyclic voltammetry and electrochemical impedance spectroscopy (EIS). The specific capacitance values were found to be 248 Fg⁻¹, 301 Fg⁻¹, 352 Fg⁻¹, and 277 Fg⁻¹ for CuO, CuO + 5% ZnO, CuO + 10% ZnO, and CuO + 15% ZnO, respectively, at 1 A/g current density. Galvanostatic charge–discharge tests for these designed NCs show excellent capacitance performance in supercapacitors applications. These innovative results could be considered for expanding novel resources to scale for dual applications in photocatalysis and supercapacitors.