Adsorption of Cadmium Ions from an Aqueous Solution on a Highly Stable Dopamine-Modified Magnetic Nano-Adsorbent.

Magnetic nanomaterials were functionalized with dopamine hydrochloride as the functional reagent to afford a core-shell-type Fe3O4 modified with polydopamine (Fe3O4@PDA) composite, which was used for the adsorption of cadmium ions from an aqueous solution. In addition, the effects of environmental factors on the adsorption capacity were investigated. Furthermore, the adsorption kinetics, isotherm, and thermodynamics of the adsorbents were discussed. Results revealed that the adsorption of cadmium by Fe3O4@PDA reaches equilibrium within 120 min, and kinetic fitting data are consistent with the pseudo-second-order kinetics (R2 > 0.999). The adsorption isotherm of Cd2+ on Fe3O4@PDA was in agreement with the Freundlich model, with the maximum adsorption capacity of 21.58 mg/g. The thermodynamic parameters revealed that adsorption is inherently endothermic and spontaneous. Results obtained from the adsorption-desorption cycles revealed that Fe3O4@PDA exhibits ultra-high adsorption stability and reusability. Furthermore, the adsorbents were easily separated from water under an enhanced external magnetic field after adsorption due to the introduction of an iron-based core. Hence, this study demonstrates a promising magnetic nano-adsorbent for the effective removal of cadmium from cadmium-containing wastewater.

A direct approach toward global minimization for multiphase labeling and segmentation problems.

This paper intends to extend the minimization algorithm developed by Bae, Yuan and Tai [IJCV, 2011] in several directions. First, we propose a new primal-dual approach for global minimization of the continuous Potts model with applications to the piecewise constant Mumford-Shah model for multiphase image segmentation. Different from the existing methods, we work directly with the binary setting without using convex relaxation, which is thereby termed as a direct approach. Second, we provide the sufficient and necessary conditions to guarantee a global optimum. Moreover, we provide efficient algorithms based on a reduction in the intermediate unknowns from the augmented Lagrangian formulation. As a result, the underlying algorithms involve significantly fewer parameters and unknowns than the naive use of augmented Lagrangian-based methods; hence, they are fast and easy to implement. Furthermore, they can produce global optimums under mild conditions.