Adsorption of Vanadium (V) from SCR Catalyst Leaching Solution and Application in Methyl Orange.

In this study, we explored an effective and low-cost catalyst and its adsorption capacity and catalytic capacity for Methyl Orange Fenton oxidation degradation were investigated. The catalyst was directly prepared by reuse of magnetic iron oxide (Fe3O4) after saturated adsorption of vanadium (V) from waste SCR (Selective Catalytic Reduction) catalyst. The obtained catalyst was characterized by FTIR, XPS and the results showed that vanadium (V) adsorption process of Fe3O4 nanoparticles was non-redox reaction. The effects of pH, adsorption kinetics and equilibrium isotherms of adsorption were assessed. Adsorption of vanadium (V) ions by Fe3O4 nanoparticles could be well described by the Sips isotherm model which controlled by the mixed surface reaction and diffusion (MSRDC) adsorption kinetic model. The results show that vanadium (V) was mainly adsorbed on external surface of the Fe3O4 nanoparticles. The separation-recovering tungsten (VI) and vanadium (V) from waste SCR catalyst alkaline solution through pH adjustment was also investigated in this study. The results obtained from the experiments indicated that tungsten (VI) was selectively adsorbed from vanadium (V)/tungsten (VI) mixed solution in certain acidic condition by Fe3O4 nanoparticle to realize their recovery. Tungsten (V) with some impurity can be obtained by releasing from adsorbent, which can be confirmed by ICP-AES. The Methyl Orange degradation catalytic performance illustrated that the catalyst could improve Fenton reaction effectively at pH = 3.0 compare to Fe3O4 nanoparticles alone. Therefore, Fe3O4 nanoparticle adsorbed vanadium (V) has a potential to be employed as a heterogeneous Fenton-like catalyst in the present contribution, and its catalytic activity was mainly evaluated in terms of the decoloration efficiency of Methyl Orange.

Improved Inference for Imputation-Based Semisupervised Learning Under Misspecified Setting.

Semisupervised learning (SSL) has been extensively studied in related literature. Despite its success, many existing learning algorithms for semisupervised problems require specific distributional assumptions, such as "cluster assumption" and "low-density assumption," and thus, it is often hard to verify them in practice. We are interested in quantifying the effect of SSL based on kernel methods under a misspecified setting. The misspecified setting means that the target function is not contained in a hypothesis space under which some specific learning algorithm works. Practically, this assumption is mild and standard for various kernel-based approaches. Under this misspecified setting, this article makes an attempt to provide a theoretical justification on when and how the unlabeled data can be exploited to improve inference of a learning task. Our theoretical justification is indicated from the viewpoint of the asymptotic variance of our proposed two-step estimation. It is shown that the proposed pointwise nonparametric estimator has a smaller asymptotic variance than the supervised estimator using the labeled data alone. Several simulated experiments are implemented to support our theoretical results.

Selective adsorption and separation of chromium (VI) on the magnetic iron-nickel oxide from waste nickel liquid.

The selective adsorption of Cr (VI) from the wastewater of Cr (VI)-Ni (II) by magnetically iron-nickel oxide was investigated in this study. Synthetic iron-nickel oxide magnetic particles in the co-sedimentation method were used as adsorbent to remove hexavalent chromium ions. The characteristic of adsorption was evaluated by Langmuir, Freundlich isotherm and Dubinin-Kaganer-Radushkevich (DKR) equations in the simulation wastewater of Cr (VI)-Ni (II) bi-system. The energy spectra and FT-IR analysis were used to test adsorbent before and after adsorption. The obtained results suggest that the uptake of chromium (VI) effect is obvious from phosphate anions and that from others is unobvious. The maximum adsorption capacity of hexavalent chromium is about 30 mg/g at pH 5.00+/-0.02, and it was reduced by increasing the total dissolved substance (TDS) of system. Adsorption energies E are about 10.310-21.321 kJ/mol which were obtained from DKR equation in difference TDS conditions. The regeneration shows that the iron-nickel oxide has good reuse performance and the hexavalent chromium was recycled. The major adsorption mechanism proposed was the ions exchange; however the surface coordination was a main role in the condition of TDS less than 200mg/L.