GO-CeO2 nanohybrid for ultra-rapid fluoride removal from drinking water.

The presence of excess fluoride (F- > 1.5 mg/L) in drinking water affects more than 260 million people globally and leads to dental and skeletal fluorosis among other health problems. This study investigated fluoride removal by graphene oxide-ceria nanohybrid (GO-CeO2) and elucidated the mechanisms involved. The nanohybrid exhibited ultra-rapid kinetics for fluoride removal and the equilibrium (85% removal, 10 mg F-/L initial concentration) was achieved within 1 min which is one of the fastest kinetics for fluoride removal reported so far. Fluoride removal by the nanohybrid followed Langmuir isotherm with a maximum adsorption capacity of 8.61 mg/g at pH 6.5 and that increased to 16.07 mg/g when the pH was lowered to 4.0. Based on the experimental results and characterization data, we have postulated that both electrostatic interaction and surface complexation participated in the fluoride removal process. The O2- ions present in the CeO2 lattice were replaced by F- ions to make a coordination compound (complex). While both Ce4+ and Ce3+ were present in ceria nanoparticles (CeO2 NPs), Ce3+ participated in fluoride complexation. During fluoride removal by GO-CeO2, the GO sheets acted as electron mediators and help to reduce Ce4+ to Ce3+ at the CeO2 NPs-GO interface, and the additional Ce3+ enhanced fluoride removal by the nanohybrid.

Modeling arsenic removal by nanoscale zero-valent iron.

Arsenic removal by nanoscale zero-valent iron (NZVI) was modeled using the USGS geochemical program PHREEQC. The Dzombak and Morel adsorption model was used. The adsorption of As(V) onto NZVI was assumed to happen because of the hydrous ferric oxide (Hfo) which was the surface oxide for the model. The model predicted results were compared with the experimental data. While the experimental study reported that 99.57% arsenic removal by NZVI, the model predicted 99.82% removal which is about 0.25% variation. All the arsenic species have also been predicted to be significantly removed by adsorption onto NZVI surface. The effect of pH on As(V) removal efficiency was also evaluated using the model and it was found that above point-of-zero-charge (PZC), the adsorption of As(V) decreases with the increase of pH. The authors conclude that PHREEQC can be used to model contaminant adsorption by nanomaterials.