ABSTRACT
The fall armyworm (FAW) poses a significant global threat to food security, and economics. Timely detection is crucial, and this research explores innovative techniques like data analysis, remote sensing, satellite imagery, and AI with machine learning algorithms for predicting and managing outbreaks. Emphasizing the importance of community engagement and international collaboration, social network analysis (SNA) is employed to uncover collaborative networks in FAW management research. The study analyzes a decade of research, revealing trends, influential institutions, authors, and countries, providing insights for efficient FAW management strategies. The research highlights a growing interest in Spodoptera frugiperda (Smith and Abbott 1797) research, focusing on biological control, chemical insecticides, plant extracts, and pest resistance. Co-Citation analysis identifies key research concepts, while collaboration analysis emphasizes the contributions of actors and institutions, such as China, the USA, and Brazil, with international collaboration playing a vital role. Current research trends involve evolving resistance, insecticidal protein gene discovery, and bio-control investigations. Leveraging insights from collaborative networks is essential for formulating effective strategies to manage fall armyworm and ensure global food security. This comprehensive analysis serves as a valuable resource for researchers and stakeholders, guiding efforts to combat this pervasive agricultural pest.
Subject(s)
Spodoptera , Animals , Insecticides , Insect Control/methods , Pest Control, Biological , Insecticide Resistance , Research , International CooperationABSTRACT
Water is crucial for life. Being the world's third-largest industry, the textile industry pollutes 93 billion cubic meters of water each year. Only 28% of textile wastewater is treated by lower- to middle-income countries due to the costly treatment methods. The present work demonstrates the utilization of surface oxygen defects and nanopores in Mg0.8Li0.2Fe2O4 (Li-MgF) to treat textile effluents by a highly economical, scalable, and eco-friendly process. Nanoporous, oxygen-deficient Li-MgF splits water by a nonphotocatalytic process at room temperature to produce green electricity as hydroelectric cell. The adsorbent Li-MgF can be easily regenerated by heat treatment. A 70-90% reduction in the UV absorption intensity of adsorbent-treated textile effluents was observed by UV-visible spectroscopy. The oxygen defects on Li-MgF surface and nanopores were confirmed by X-ray photoelectron spectroscopy and Brunauer-Emmett-Teller (BET) measurements, respectively. To analyze the adsorption mechanism, three known organic water-soluble dyes, brilliant green, crystal violet, and congo red, were treated with nanoporous Li-MgF. The dye decolorization efficiency of Li-MgF was recorded to be 99.84, 99.27, and 99.31% at 250 µM concentrations of brilliant green, congo red, and crystal violet, respectively. The results of Fourier transform infrared (FTIR) spectroscopy confirmed the presence of dyes on the material surface attached through hydroxyl groups generated by water splitting on the surface of the material. Total organic carbon analysis confirmed the removal of organic carbon from the dye solutions by 82.8, 77.0, and 46.5% for brilliant green, Congo red, and crystal violet, respectively. Based on the kinetic and isotherm models, the presence of a large number of surface hydroxyl groups on the surface of the material and OH- ions in solutions generated by water splitting was found to be responsible for the complete decolorization of all of the dyes. Adsorption of chemically diverse dyes by the nanoporous, eco-friendly, ferromagnetic, economic, and reusable Li-MgF provides a sustainable and easy way to treat textile industry effluents in large amounts.
