Nanomaterials as a sustainable choice for treating wastewater.

Wastewater containing toxic substances is a major threat to the health of both aquatic and terrestrial ecosystems. In order to treat wastewater, nanomaterials are currently being studied intensively due to their unprecedented properties. The unique features of nanoparticles are prompting an increasing number of studies into their use in wastewater treatment. Although several studies have been undertaken in recent years, most of them did not focus on some of the nanomaterials that are now often utilized for wastewater treatment. It is essential to investigate the most recent advances in all the types of nanomaterials that are now frequently employed for wastewater treatment. The recent advancements in common nanomaterials used for sustainable wastewater treatment is comprehensively reviewed in this paper. This paper also thoroughly assesses unique features, proper utilization, future prospects, and current limitations of green nanotechnology in wastewater treatment. Zero-valent metal and metal oxide nanoparticles, especially iron oxides were shown to be more effective than traditional carbon nanotubes (CNTs) for recovering heavy metals in wastewater. Iron oxide achieved 75.9% COD (chemical oxygen demand) removal efficiency while titanium oxide (TiO2) achieved 75.5% COD. Iron nanoparticles attained 72.1% methyl blue removal efficiency. However, since only a few types of nanomaterials have been commercialized, it is important to also focus on the economic feasibility of each nanomaterial. This study found that the large surface area, high reactivity, and strong mechanical properties of nanoparticles means they can be considered as a promising option for successful wastewater treatment.

Designing and development of agricultural rovers for vegetable harvesting and soil analysis.

To address the growing demand for sustainable agriculture practices, new technologies to boost crop productivity and soil health must be developed. In this research, we propose designing and building an agricultural rover capable of autonomous vegetable harvesting and soil analysis utilizing cutting-edge deep learning algorithms (YOLOv5). The precision and recall score of the model was 0.8518% and 0.7624% respectively. The rover uses robotics, computer vision, and soil sensing technology to perform accurate and efficient agricultural tasks. We go over the rover's hardware and software, as well as the soil analysis system and the tomato ripeness detection system using deep learning models. Field experiments indicate that this agricultural rover is effective and promising for improving crop management and soil monitoring in modern agriculture, hence achieving the UN's SDG 2 Zero Hunger goals.