1.
The role of graphene oxide and graphene oxide-based nanomaterials in the removal of pharmaceuticals from aqueous media: a review.
Khan, A, Wang, J, Li, J, Wang, X, Chen, Z, Alsaedi, A, Hayat, T, Chen, Y, Wang, X
Environmental science and pollution research international. 2017;(9):7938-7958
Abstract
In this review paper, the ill effects of pharmaceuticals (PhAs) on the environment and their adsorption on graphene oxide (GO) and graphene oxide-based (GO-based) nanomaterials have been summarised and discussed. The adsorption of prominent PhAs discussed herein includes beta-blockers (atenolol and propranolol), antibiotics (tetracycline, ciprofloxacin and sulfamethoxazole), pharmaceutically active compounds (carbamazepine) and analgesics such as diclofenac. The adsorption of PhAs strictly depends upon the experimental conditions such as pH, adsorbent and adsorbate concentrations, temperature, ionic strength, etc. To understand the adsorption mechanism and feasibility of the adsorption process, the adsorption isotherms, thermodynamics and kinetic studies were also considered. Except for some cases, GO and its derivatives show excellent adsorption capacities for PhAs, which is crucial for their applications in the environmental pollution cleanup.
2.
Transport and aggregation of rutile titanium dioxide nanoparticles in saturated porous media in the presence of ammonium.
Xu, X, Xu, N, Cheng, X, Guo, P, Chen, Z, Wang, D
Chemosphere. 2017;:9-17
Abstract
The widely used artificial nanoparticles (NPs) and the excess of ammonium (NH4+) fertilizers are easily released into the natural environment. So, clarifying the mobility of NPs in the presence of NH4+ is therefore of great urgency and high priority. Currently, few studies focus on the transport and deposition of nanoparticle titanium dioxide (nTiO2) in single and binary systems containing NH4+, especially describing this process by a mathematical model. In this work, the comparison between the transport and retention of rutile nTiO2 in single and binary electrolyte solutions of NH4Cl and/or NaCl (0.5-50 mM) were conducted at pH 6.0 and 8.0 through running the column experiments. Experimental results show that the aggregation and retention of nTiO2 in solution containing mono-valence cations obeys the order as follows: NH4+ > Na+ > Na+ + NH4+ at the same ion strength (IS). It is attributed to the lower critical coagulation concentration (CCC) of rutile nTiO2 in NH4+ than that in Na+ solution. In particular, the simultaneous presence of NH4+ and Na+ favors the transportability of nTiO2 due to the strong competitive adsorption on the surface of NPs. The two-site kinetic retention model provides the good simulation for their transport behavior. The likely mechanism is that the secondary energy minimum of nTiO2 in NH4+ system associated with the greater K2 at surface Site 2 (from model) on sand can be explained for the more reversible deposition. Ammonium leachate associated with NPs can thus be considered a serious concern.
3.
Application of neural networks with novel independent component analysis methodologies to a Prussian blue modified glassy carbon electrode array.
Wang, L, Yang, D, Fang, C, Chen, Z, Lesniewski, PJ, Mallavarapu, M, Naidu, R
Talanta. 2015;:395-403
Abstract
Sodium potassium absorption ratio (SPAR) is an important measure of agricultural water quality, wherein four exchangeable cations (K(+), Na(+), Ca(2+) and Mg(2+)) should be simultaneously determined. An ISE-array is suitable for this application because its simplicity, rapid response characteristics and lower cost. However, cross-interferences caused by the poor selectivity of ISEs need to be overcome using multivariate chemometric methods. In this paper, a solid contact ISE array, based on a Prussian blue modified glassy carbon electrode (PB-GCE), was applied with a novel chemometric strategy. One of the most popular independent component analysis (ICA) methods, the fast fixed-point algorithm for ICA (fastICA), was implemented by the genetic algorithm (geneticICA) to avoid the local maxima problem commonly observed with fastICA. This geneticICA can be implemented as a data preprocessing method to improve the prediction accuracy of the Back-propagation neural network (BPNN). The ISE array system was validated using 20 real irrigation water samples from South Australia, and acceptable prediction accuracies were obtained.