Nanoscale zero-valent iron (nZVI) is thought to be the most effective remediation material for contaminated soil, especially when it comes to heavy metal pollutants. In the current high-industrial and technologically advanced period, water pollution has emerged as one of the most significant causes for concern. In this instance, silica was coated with zero-valent iron nanoparticles at 650 and 800 ℃. Ferric iron with various counter-ions, nitrate (FN) and chloride (FC), and sodium borohydride as a reducing agent were used to create nanoscale zero-valent iron in an ethanol medium with nitrogen ambient conditions. X-ray diffraction (XRD) and field emission scanning electron microscopy (FE-SEM) techniques were employed to describe the structures of the generated zero-valent iron nanoparticles. Further, we investigated the electrical properties and adsorption characteristics of dyes such as alizarin red in an aqueous medium. As a result, zero-valent nano iron (nZVI), a core-shell environmental functional material, has found extensive application in environmental cleanup. The knowledge in this work will be useful for nZVI-related future research and real-world applications.

This study investigates the relationship between hydrological processes, watershed management, and road infrastructure resilience, focusing on the impact of flooding on roads intersecting with streams in River Nile State, Sudan. Situated between 16.5° N to 18.5° N latitude and 33° E to 34° E longitude, this region faces significant flooding challenges that threaten its ecological and economic stability. Using precise Digital Elevation Models (DEMs) and advanced hydrological modeling, the research aims to identify optimal flood mitigation solutions, such as overpass bridges. The study quantifies the total road length in the area at 3572.279 km, with stream orders distributed as follows: First Order at 2276.79 km (50.7%), Second Order at 521.48 km (11.6%), Third Order at 331.26 km (7.4%), and Fourth Order at 1359.92 km (30.3%). Approximately 27% (12 out of 45) of the identified road flooding points were situated within third- and fourth-order streams, mainly along the Atbara-Shendi Road and near Al-Abidiya and Merowe. Blockages varied in distance, with the longest at 256 m in Al-Abidiya, and included additional measurements of 88, 49, 112, 106, 66, 500, and 142 m. Some locations experienced partial flood damage despite having water culverts at 7 of these points, indicating possible design flaws or insufficient hydrological analysis during construction. The findings suggest that enhanced scrutiny, potentially using high-resolution DEMs, is essential for better vulnerability assessment and management. The study proposes tailored solutions to protect infrastructure, promoting sustainability and environmental stewardship.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

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.