Photocatalysis, an innovative technology, holds promise for addressing industrial pollution issues across aqueous solutions, surfaces, and gaseous effluents. The efficiency of photodegradation is notably influenced by light intensity and duration, underscoring the importance of optimizing these parameters. Furthermore, temperature and pH have a significant impact on pollutant speciation, surface chemistry, and reaction kinetics; therefore, process optimization must consider these factors. Photocatalytic degradation is an effective method for treating water in environmental remediation, providing a flexible and eco-friendly way to eliminate organic contaminants from wastewater. Selectivity in photocatalytic degradation is achieved by a multidisciplinary approach that includes reaction optimization, catalyst design, and profound awareness of chemical processes. To create efficient and environmentally responsible methods for pollution removal and environmental remediation, researchers are working to improve these components.

The electrospinning precursor solution was prepared by dissolving polyvinyl pyrrolidone as template, tetrabutyl titanate as titanium source, and acetic acid as inhibitor. The TiO₂ nanofilms were prepared by precursor solution electrospinning and subsequent calcination. Thermal gravimetric analysis (TG), scanning electron microscopy (SEM), X-ray powder diffraction (XRD), and transmission electron microscopy (TEM) were used to characterize and analyze the samples. The influence of technological parameters on spinning fiber morphology was also studied. The results indicate that the TiO₂ nanofibers morphology is good when the parameters are as follows: voltage 1.4×10⁴ V，spinning distance 0.2 m，translational velocity 2.5×10^-3 m·s^-1, flow rate 3×10^-4 m·s^-1, and needle diameter 3×10^-4 m. The diameter of the fibers is about 150 nm. With the 1×10^-4 mol·L^-1 methylene blue solution used as simulated degradation target, the degradation rate is 95.8% after 180 minutes.

Alginate-silver nanocomposites in the form of spherical beads and films were prepared using a green approach by using the aqueous extract of Ajwa date seeds. The nanocomposites were fabricated by in situ reduction and gelation by ionotropic crosslinking using calcium ions in solution. The rich phytochemicals of the date seed extract played a dual role as a reducing and stabilizing agent in the synthesis of silver nanoparticles. The formation of silver nanoparticles was studied using UV-Vis absorption spectroscopy, and a distinct surface plasmon resonance peak at 421 nm characteristic of silver nanoparticles confirmed the green synthesis of silver nanoparticles. The morphology of the nanocomposite beads and film was compact, with an even distribution of silver nanoclusters. The catalytic property of the nanocomposite beads was evaluated for the degradation of 2-nitrophenol in the presence of sodium borohydride. The degradation followed pseudo-first-order kinetics with a rate constant of 1.40 × 10⁻³ s⁻¹ at 23 ℃ and an activation energy of 18.45 kJ mol⁻¹. The thermodynamic parameters, such as changes in enthalpy and entropy, were evaluated to be 15.22 kJ mol⁻¹ and −197.50 J mol⁻¹ K⁻¹, respectively. The nanocomposite exhibited properties against three clinically important pathogens (gram-positive and gram-negative bacteria).

Hospital waste containing antibiotics is toxic to the ecosystem. Ciprofloxacin is one of the essential, widely used antibiotics and is often detected in water bodies and soil. It is vital to treat these medical wastes, which urge new research towards waste management practices in hospital environments themselves. Ultimately minimizes its impact in the ecosystem and prevents the spread of antibiotic resistance. The present study highlights the decomposition of ciprofloxacin using nano-catalytic ZnO materials by reactive oxygen species (ROS) process. The most effective process to treat the residual antibiotics by the photocatalytic degradation mechanism is explored in this paper. The traditional co-precipitation method was used to prepare zinc oxide nanomaterials. The characterization methods, X-Ray diffraction analysis (XRD), Fourier Transform infrared spectroscopy (FTIR), Ulraviolet-Visible spectroscopy (UV-Vis), Scanning Electron microscopy (SEM) and X-Ray photoelectron spectroscopy (XPS) have done to improve the photocatalytic activity of ZnO materials. The mitigation of ciprofloxacin catalyzed by ZnO nano-photocatalyst was described by pseudo-first-order kinetics and chemical oxygen demand (COD) analysis. In addition, ZnO materials help to prevent bacterial species, S. aureus and E. coli, growth in the environment. This work provides some new insights towards ciprofloxacin degradation in efficient ways.

Nowadays, copper and zinc nanoparticles are widely employed in a variety of applications. With nanoscale particle sizes, copper oxide/zinc oxide composite is easily synthesized using a variety of techniques, including hydrothermal, microwave, precipitation, etc. In the current work, chemical precipitation is used to create a copper oxide/zinc oxide nanocomposite. XRD analysis was used to determine the nanocomposite’s structural characteristics. Through SEM analysis, the surface morphological properties are investigated. EDAX is used to study the chemical composition of produced materials, while UV/Visible spectroscopy is used to determine their optical properties. The assessment of the copper oxide/zinc oxide nanocomposite’s degrading property on dyes like methyl red and methyl orange under UV and visible light are the main objectives of the current work.

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.