Ultrafast and highly capture of U(VI) by hierarchical mesoporous carbon

In this study, the hierarchical mesoporous carbon (HMC) was synthesized by the hydrothermal method. The batch adsorption experiments showed that HMC exhibited the ultrafast equilibrium fate (80 % U(VI) capture efficiency within 5 min), high UO22+ capture capacity (210 mg/g, pH = 4.5) and well recyclability. The investigations of XPS techniques indicated the oxygen-containing functional groups were responsible for high efficient UO22+ adsorption. The pH-dependent adsorption was simulated by three surface complexation modellings, revealing that UO22+ adsorption on HMC was excellently fitted by triple layer model using two inner-sphere complexes (i. e. SOUO2+ and SOUO2(CO3)35− species) compared to constant capacitance model and diffuse layer model. These findings are crucial for expanding actual applications of HMC towards the removal of radionuclides under environmental cleanup.

Simulation of antimony adsorption on nano-zero valent iron and kaolinite and analyzing the influencing parameters

Antimony is one of the most toxic pollutants in industrial and mineral wastewaters threatening the life of humans and other creatures. We simulated the adsorption of antimony in the presence of nano-zero valent iron (nZVI) adsorbent, on kaolinite and in the presence of nZVI coated on kaolinite from mineral wastewater using VISUAL MINTEQ 3.1 software. Our aim was to determine the factors affecting the adsorption of antimony by applying simulation. The simulation was performed using an adsorption model of a diffuse layer model. The results of the simulation indicated that the nZVI concentration, initial concentrations of antimony and pH factor are effective on the adsorption of antimony. In the conducted stimulation, the optimum pH was 2–5 and the highest adsorption occurred in an acidic state. With increasing initial concentrations of antimony in the simulation, we concluded that nZVI had absorbed various concentrations above 90% and, by increasing the concentration of nZVI, antimony adsorption rate increased. The increased surface area of nZVI and the expansion of more interchangeable surfaces available for reaction with antimony ions causes more antimony ions to be adsorbed. In all cases, the coefficient of determination between the laboratory results and the model predictions that was obtained was more than 0.9.

Predicting PbII adsorption on soils: the roles of soil organic matter, cation competition and iron (hydr)oxides

Environmental context Lead is a common and persistent soil and water contaminant. This study provides a unique set of parameters for chemical models that can be used for predicting Pb adsorption by soil. The suggested modelling approach can be used to quantitatively predict Pb retention and release in soils with changing environmental conditions. Abstract Lead (PbII) adsorption on 14 non-calcareous New Jersey soils was studied with a batch method. Both adsorption edge and adsorption isotherm experiments were conducted covering a wide range of soil compositions, Pb concentrations and solution pHs. Visual MINTEQ was used to calculate the Pb adsorption equilibrium by coupling the Stockholm Humic Model, the CD-MUSIC model, a diffuse layer model and a cation exchange model for Pb reactions with soil organic matter (SOM), Fe (hydr)oxides, Al hydroxides and clay minerals. For model predictions, reactive organic matter (ROM), the fraction of SOM responsible for Pb binding, and reactive Al and FeIII in soils were quantified. The models predicted Pb adsorption to soils reasonably well with varying SOM and mineral content at various pHs and Pb concentrations. For 3.0<pH<6.0, the log partition coefficient root mean square error was 0.34. However at higher pHs the models were less successful. Both ROM and Al competition had a significant effect on model predictions. ROM was the dominant adsorption phase at pHs between 3.0 and 5.0. For pH>5.0, Pb adsorption to Fe (hydr)oxides became significant. The modelling approach presented in this study can be used to understand and quantitatively predict Pb adsorption on soil.