Research

This research explored the use of a magnetic graphene oxide calcium alginate composite (MGO@CA) for rapid removal of Pb²⁺ ions from aqueous systems. The composite demonstrated a maximum adsorption capacity of 270.27 mg/g, as per the Langmuir isotherm model, and followed pseudo-second-order kinetics, suggesting chemisorption.

MGO@CA's magnetic properties facilitated easy separation, maintaining 82.28% of its adsorption capacity after five regeneration cycles. Calcium alginate (CA) effectively bound graphene oxide (GO), preventing leaching and preserving its adsorption efficiency. FTIR analysis indicated a reduction in peak intensity post-adsorption, confirming the involvement of functional groups in Pb²⁺ ion binding.

Additionally, magnetic properties were quantified using a vibrating sample magnetometer (VSM), demonstrating the composite's ferromagnetic behavior, making it highly efficient for large-scale water treatment applications.

This study synthesized a reduced graphene oxide-zinc oxide (rGO-ZnO) nanocomposite using green tea extract for rGO reduction and neem leaf extract for ZnO nanoparticle synthesis, exemplifying sustainable green chemistry approaches.

The photocatalytic activity of the rGO-ZnO composite was evaluated for dye removal, achieving 92.23% photodegradation efficiency for maxilon pink dye (MP) under visible light irradiation. The removal efficiency increased significantly with the addition of rGO, with 5% rGO-ZnO showing 92.23% degradation after 120 minutes, compared to only 7.12% for pure ZnO.

rGO enhances light absorption, facilitating photon absorption and electron-hole pair generation in ZnO, thereby improving the photocatalytic activity. The composite's high surface area, visible light responsiveness, and increased electron migration led to enhanced degradation performance.

This comprehensive study explores the potential of graphene oxide (GO)-based membranes for desalination, focusing on the challenges and opportunities they offer in addressing water scarcity. The research uses an integrated methodology combining bibliometric analysis, AI-powered topic modeling, burst keyword detection, research evolution forecasting, and patent analysis.

SciBERT was used for topic modeling, while HDBSCAN clustered research into themes, and Kleinberg's burst detection algorithm identified emerging trends. The Prophet algorithm forecasted future research developments. Python scripts in Google Colab and R in RStudio were employed for processing large datasets from Scopus and Lens, performing burst keyword detection, and visualizing bibliometric data.

This AI-driven analysis revealed key research hotspots and predicted future trends in GO membrane technology. The study found that GO membranes offer enhanced water permeability and salt rejection with reduced energy consumption (15–46% lower), while also highlighting challenges in scalability and stability. The integration of R and Python allowed for efficient data handling, modeling, and visualization, which provided valuable insights for informed decision-making.