Facile Biogenic Synthesis and Characterization of Seven Metal-Based Nanoparticles Conjugated with Phytochemical Bioactives Using Fragaria ananassa Leaf Extract

In this investigation, for the first time, we used Fragaria ananassa (strawberry) leaf extract as a source of natural reducing, capping or stabilizing agents to develop an eco-friendly, cost-effective and safe process for the biosynthesis of metal-based nanoparticles including silver, copper, iron, zinc and magnesium oxide. Calcinated and non-calcinated zinc oxide nanoparticles also synthesized during a method different from our previous study. To confirm the successful formation of nanoparticles, different characterization techniques applied. UV-Vis spectroscopy, X-ray Diffraction (XRD) spectroscopy, Field Emission Scanning Electron Microscopy (FESEM) coupled with Energy Dispersive X-ray Spectroscopy (EDS), Photon Cross-Correlation Spectroscopy (PCCS) and Fourier Transformed Infrared Spectroscopy (FT-IR) were used to study the unique structure and properties of biosynthesized nanoparticles. The results show the successful formation of metal-based particles in the range of nanometer, confirmed by different characterization techniques. Finally, the presented approach has been demonstrated to be effective in the biosynthesis of metal and metal oxide nanoparticles

Mapping of soil erosion susceptibility using advanced machine learning models at Nghe An, Vietnam

Soil Erosion Susceptibility Mapping (SESM) is one of the practical approaches for managing and mitigating soil erosion. This study applied four Machine Learning (ML) models, namely the Multilayer Perceptron (MLP) classifier, AdaBoost, Ridge classifier, and Gradient Boosting classifier to perform SESM in a region of Nghe An province, Vietnam. The development of these models incorporated seven factors influencing soil erosion: slope degree, slope aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), rainfall, and soil type. These factors were determined based on 685 identified soil erosion locations. According to SHapley Additive exPlanations (SHAP) analysis, soil type emerged as the most significant factor influencing soil erosion. Among all the developed models, the Gradient Boosting classifier demonstrated the highest prediction power, followed by the MLP classifier, Ridge classifier, and AdaBoost, respectively. Therefore, the Gradient Boosting classifier is recommended for accurate SESM in other regions too, taking into account the local geo-environmental factors. HIGHLIGHTS Soil erosion has been modeled and a soil erosion susceptibility map was generated.; Several ML models, including the MLP classifier, Ada Boost, Ridge classifier, and Gradient Boosting classifier were implemented.; Developed models were tuned using the Grid Search CV technique.; The Gradient Boosting classifier performed the best.; About 33% of the study area has a high and very high susceptibility to soil erosion occurrence.