ZnO nanostructures were obtained by electrodeposition on Ni foam, where graphene was previously grown by chemical vapor deposition (CVD). The resulting heterostructures were characterized by X-ray diffraction and SEM microscopy, and their potential application as a catalyst for the photodegradation of methylene blue (MB) was evaluated. The incorporation of graphene to the Ni substrate increases the amount of deposited ZnO at low potentials in comparison to bare Ni. SEM images show homogeneous growth of ZnO on Ni/G but not on bare Ni foam. A percent removal of almost 60% of MB was achieved by the Ni/G/ZnO sample, which represents a double quantity than the other catalysts proved in this work. The synergistic effects of ZnO-graphene heterojunctions play a key role in achieving better adsorption and photocatalytic performance. The results demonstrate the ease of depositing ZnO on seedless graphene by electrodeposition. The use of the film as a photocatalyst delivers interesting and competitive removal percentages for a potentially scalable degradation process enhanced by a non-toxic compound such as graphene.

Synthesis of macro-mesoporous Titania (Titanium dioxide-TiO₂) nanospheres was successfully achieved using a modified template-free methodology to incorporate macroporous channels into a mesoporous TiO₂ framework to form mixed macro-mesoporous TiO₂ spheres (MMPT), which were doped with carbon dots (C-dots) to form improved nanocomposites (C-dots@MMPT). Elemental composition, surface bonding and optical properties of these nanocomposites were characterized by X-ray diffraction (XRD), Fourier transforms infrared spectroscopy (FTIR) and ultraviolet-visible absorption spectroscopy (UV-VIS). Evaluation of photocatalytic activity for each (C-Dots@MMPT) sample was performed via degrading the Methylene Blue (MB) dye compared with bare samples (MMPT) under visible light irradiation using 300 Watt halogen lamp.

Tropical peat swamp is an essential ecosystem experiencing increased degradation over the past few decades. Therefore, this study used the social-ecological system (SES) perspective to explain the complex relationship between humans and nature in the Sumatran Peatlands Biosphere Reserve. The peat swamp forest has experienced a significant decline, followed by a significant increase in oil palm and forest plantations in areas designated for peat protection. Human systems have evolved to become complex and hierarchical, constituting individuals, groups, organizations, and institutions. Studies on SES conducted in the tropical peatlands of Asia have yet to address the co-evolutionary processes occurring in this region, which could illustrate the dynamic relationship between humans and nature. This study highlights the co-evolutionary processes occurring in the tropical peatland biosphere reserve and provides insights into their sustainability trajectory. Moreover, the coevolution process shows that biosphere reserve is shifting toward an unsustainable path. This is indicated by ongoing degradation in three zones and a lack of a comprehensive framework for landscape-scale water management. Implementing landscape-scale water management is essential to sustain the capacity of peatlands social-ecological systems facing disturbances, and it is important to maintain biodiversity. In addition, exploring alternative development pathways can help alter these trajectories toward sustainability.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

Ce⁴⁺-doped nanometer ZnO powder was synthesized by so-l gel method. The microstructures and properties of the samples were characterized through XRD, UV-Vis and FTIR. The results indicated that the Ce⁴⁺ was successfully incorporated into ZnO, and the diameter of the nanometer was about 10.7nm. It induced the redshifting in the UV-Vis spectra. The photocatalytic activity of the samples was investigated using methylene blue (MB) as the model reaction under irradiation with ultraviolet light. The results showed that the doping of Ce⁴⁺ could increase the photocatalytic activities of ZnO nanopowders and that the best molar ratio of Ce⁴⁺ was n(Ce)/n(Zn) = 0.05, that the surfactant was sodium dodecyl sulfate, and that the nanometer ZnO was calcinated at 550 ℃ for 3 hours. Meanwhile, it inspected the effect of photocatalytic efficiency through the pH of MB, the amount of catalyst, and illumination time. The experimental results revealed that the initial mass concentration of MB was 10 mg/L, that the pH value was 7-8, that the dosage of Ce⁴⁺/ZnO photo-catalyst was 5 g/L, that the UV-irradiation time was 2 h, and that the removal rate of MB reached above 85%. Under the optimized conditions, the degradation rate of real dye wastewater was up to 87.67% and the removal efficiency of COD was 63.5%.