Globally, the contamination of water with arsenic is a serious health issue. Recently, several researches have endorsed the efficiency of biomass to remove As (III) via adsorption process, which is distinguished by its low cost and easy technique in comparison with conventional solutions. In the present work, biomass was prepared from indigenous Bacillus thuringiensis strain WS3 and was evaluated to remove As (III) from aqueous solution under different contact time, temperature, pH, As (III) concentrations and adsorbent dosages, both experimentally and theoretically. Subsequently, optimal conditions for As (III) removal were found; 6 (ppm) As (III) concentration at 37 °C, pH 7, six hours of contact time and 0.50 mg/ml of biomass dosage. The maximal As (III) loading capacity was determined as 10.94 mg/g. The equilibrium adsorption was simulated via the Langmuir isotherm model, which provided a better fitting than the Freundlich model. In addition, FESEM-EDX showed a significant change in the morphological characteristic of the biomass following As (III) adsorption. 128 batch experimental data were taken into account to create an artificial neural network (ANN) model that mimicked the human brain function. 5-7-1 neurons were in the input, hidden and output layers respectively. The batch data was reserved for training (75%), testing (10%) and validation process (15%). The relationship between the predicted output vector and experimental data offered a high degree of correlation (R2 = 0.9959) and mean squared error (MSE; 0.3462). The predicted output of the proposed model showed a good agreement with the batch work with reasonable accuracy.
A Gram-negative, arsenite-resistant psychrotolerant bacterial strain, Yersinia sp. strain SOM-12D3, was isolated from a biofilm sample collected from a lake at Svalbard in the Arctic area. To our knowledge, this is the first study on the ability of acid-treated and untreated, non-living biomass of strain SOM-12D3 to absorb arsenic. We conducted batch experiments at pH 7, with an initial As(III) concentration of 6.5 ppm, at 30 °C with 80 min of contact time. The Langmuir isotherm model fitted the equilibrium data better than Freundlich, and the sorption kinetics of As(III) biosorption followed the pseudo-second-order rate equation well for both types of non-living biomass. The highest biosorption capacity of the acid-treated biomass obtained by the Langmuir model was 159 mg/g. Further, a high recovery efficiency of 96% for As(III) was achieved using 0.1 M HCl within four cycles, which indicated high adsorption/desorption. Fourier transformed infrared (FTIR) demonstrated the involvement of hydroxyl, amide, and amine groups in As(III) biosorption. Field emission scanning electron microscopy-energy dispersive analysis (FESEM-EDAX) indicated the different morphological changes occurring in the cell after acid treatment and arsenic biosorption. Our results highlight the potential of using acid-treated non-living biomass of the psychrotolerant bacterium, Yersinia sp. Strain SOM-12D3 as a new biosorbent to remove As(III) from contaminated waters.
Several parts of the world have been facing the problem of nitrite and nitrate contamination in ground and surface water. The acute toxicity of nitrite has been shown to be 10-fold higher than that of nitrate. In the present study, aminated silica carbon nanotube (ASCNT) was synthesised and tested for nitrite removal. The synergistic effects rendered by both amine and silica in ASCNT have significantly improved the nitrite removal efficiency. The IEP increased from 2.91 for pristine carbon nanotube (CNT) to 8.15 for ASCNT, and the surface area also increased from 178.86 to 548.21 m2 g-1. These properties have promoted ASCNT a novel adsorbent to remove nitrite. At optimum conditions of 700 ppm of nitrite concentration at pH 7 and 5 h of contact with 15 mg of adsorbent, the ASCNT achieved the maximal loading capacity of 396 mg/g (85% nitrite removal). The removal data of nitrite onto ASCNT fitted the Langmuir isotherm model better than the Freundlich isotherm model with the highest regression value of 0.98415, and also, the nonlinear analysis of kinetics data showed that the removal of nitrite followed pseudo-second-order kinetic. The positive values of both ΔS° and ΔH° suggested an endothermic reaction and an increase in randomness at the solid-liquid interface. The negative ΔG° values indicated a spontaneous adsorption process. The ASCNT was characterised using FESEM-EDX and FTIR, and the results obtained confirmed the removal of nitrite. Based on the findings, ASCNT can be considered as a novel and promising candidate for the removal of nitrite ions from wastewater.
This review discusses the application of bacterial nanocellulose (BNC) and modified BNC in treating wastewater containing heavy metals and dye contaminants. It also highlights the challenges and future perspectives of BNC and its composites. Untreated industrial effluents containing toxic heavy metals are systematically discharged into public waters. In particular, lead (Pb), copper (Cu), cadmium (Cd), nickel (Ni), zinc (Zn), and arsenic (As) are very harmful to human health and, in some cases, may lead to death. Several methods such as chemical precipitation, ion exchange, membrane filtration, coagulation, and Fenton oxidation are used to remove these heavy metals from the environment. However, these methods involve the use of numerous chemicals whilst producing high amount of toxic sludge. Meanwhile, the development of the adsorption-based technique has provided an alternative way of treating wastewater using BNC. Bacterial nanocellulose requires less energy for purification and has higher purity than plant cellulose. In general, the optimum growth parameters are crucial in BNC production. Even though native BNC can be used for the removal of heavy metals and dyes, the incorporation of other materials, such as polyethyleneimine, graphene oxide, calcium carbonate and polydopamine can improve sorption efficiencies.
Sustainable wastewater treatment is one of the biggest issues of the 21st century. Metals such as Zn2+ have been released into the environment due to rapid industrial development. In this study, dried watermelon rind (D-WMR) is used as a low-cost adsorption material to assess natural adsorbents' ability to remove Zn2+ from synthetic wastewater. D-WMR was characterized using scanning electron microscope (SEM) and X-ray fluorescence (XRF). According to the results of the analysis, the D-WMR has two colours, white and black, and a significant concentration of mesoporous silica (83.70%). Moreover, after three hours of contact time in a synthetic solution with 400 mg/L Zn2+ concentration at pH 8 and 30 to 40 °C, the highest adsorption capacity of Zn2+ onto 1.5 g D-WMR adsorbent dose with 150 μm particle size was 25 mg/g. The experimental equilibrium data of Zn2+ onto D-WMR was utilized to compare nonlinear and linear isotherm and kinetics models for parameter determination. The best models for fitting equilibrium data were nonlinear Langmuir and pseudo-second models with lower error functions. Consequently, the potential use of D-WMR as a natural adsorbent for Zn2+ removal was highlighted, and error analysis indicated that nonlinear models best explain the adsorption data.