Chromium (VI) is a ubiquitous groundwater contaminant and it is dangerous to both ecological and human health. Iron nanoparticles (nFe) have a large specific surface area and they are highly efficient in removing chromium (VI) from aqueous solution. However, since the traditional reductive synthesis of nFe is relatively expensive and often causes secondary pollution, it is necessary to develop a low-cost green synthetic method using plant extracts. Synthetic conditions are important for obtaining highly active chromium-removing nanomaterials. In this paper, a green tea extract was used to prepare nFe and the effects of synthetic conditions on subsequent remediation performance were investigated. The optimal conditions included a green tea extract/Fe2+ ratio of 1:2 (91.6%), a green tea extract temperature of 353 K (88.3%) and a synthetic temperature of 298 K (88.1%). Advanced material characterization techniques, including XPS, SEM-EDS, TEM, and Brunauer–Emmett–Teller (BET) confirmed that the average particle size was between 50–80 nm, with a specific surface area of 42.25 m2·g−1. Furthermore nFe had a core-shell structure, where Fe (0) constituted the core and a shell was composed of iron oxide. Finally, a mechanism for synthesizing nFe by green tea extract was proposed, providing a theoretical basis for optimized synthetic conditions for preparing nFe when using green tea extract.

No abstract

Chromium and its compounds are widely used in many industries in China and play a very important role in the national economy. At the same time, heavy metal chromium pollution poses a great threat to the ecological environment and human health. Therefore, it's necessary to safely and effectively remove the chromium from pollutants. In practice, there are many factors which influence the removal efficiency of the chromium. However, few studies have investigated the relationship between multiple factors and the removal efficiency of the chromium till now. To this end, this paper uses the green synthetic iron nanoparticles to remove the chromium and investigates the impacts of multiple factors on the removal efficiency of the chromium. A novel model that maps multiple given factors to the removal efficiency of the chromium is proposed through the advanced machine learning methods, i.e., XGBoost and random forest (RF). Experiments demonstrate that the proposed method can predict the removal efficiency of the chromium precisely with given influencing factors, which is very helpful for finding the optimal conditions for removing the chromium from pollutants.