Uranium removal from contaminated groundwater by synthetic resins

Synthetic resins are shown to be effective in removing uranium from contaminated groundwater. Batch and field column tests showed that strong-base anion-exchange resins were more effective in removing uranium from both near-neutral-pH (6.5)- and high-pH (8)-low-nitrate-containing groundwaters, than metal-chelating resins, which removed more uranium from acidic-pH (5)-high-nitrate-containing groundwater from the Oak Ridge Reservation (ORR) Y-12 S-3 Ponds area in Tennessee, USA. Dowex 1-X8 and Purolite A-520E anion-exchange resins removed more uranium from high-pH (8)-low-nitrate-containing synthetic groundwater in batch tests than metal-chelating resins. The Dowex™ 21K anion-exchange resin achieved a cumulative loading capacity of 49.8 mg g^-1 before breakthrough in a field column test using near-neutral-pH (6.5)-low-nitrate-containing groundwater. However, in an acidic-pH (5)-high-nitrate-containing groundwater, metal-chelating resins Diphonix and Chelex-100 removed more uranium than anion-exchange resins. In 15 mL of acidic-pH (5)-high-nitrate-containing groundwater spiked with 20 mg L^-1 uranium, the uranium concentrations ranged from 0.95 mg L^-1 at 1-h equilibrium to 0.08 mg L^-1 at 24-h equilibrium for Diphonix and 0.17 mg L^-1 at 1-h equilibrium to 0.03 mg L^-1 at 24-h equilibrium for Chelex-100. Chelex-100 removed more uranium in the first 10 min in the 100 mL of acidic-(pH 5)-high-nitrate-containing groundwater (~5 mg L^-1 uranium); however, after 10 min, Diphonix equaled or out-performed Chelex-100. This study presents an improved understanding of the selectivity and sorption kenetics of a range of ion-exchange resins that remove uranium from both low- and high-nitrate-containing groundwaters with varying pHs..

SrTiO3(001)(2x1) reconstructions: First-principles calculations of surface energy and atomic structure compared with scanning tunneling microscopy images

(1x1) and (2x1) reconstructions of the (001) SrTiO3 surface were studied using the first-principles full-potential linear muffin-tin orbital method. Surface energies were calculated as a function of TiO2 chemical potential, oxygen partial pressure and temperature. The (1x1) unreconstructed surfaces were found to be energetically stable for many of the conditions considered. Under conditions of very low oxygen partial pressure the (2x1) Ti2O3 reconstruction [Martin R. Castell, Surf. Sci. 505, 1 (2002)] is stable. The question as to why STM images of the (1x1) surfaces have not been obtained was addressed by calculating charge densities for each surface. These suggest that the (2x1) reconstructions would be easier to image than the (1x1) surfaces. The possibility that the presence of oxygen vacancies would destabilise the (1x1) surfaces was also investigated. If the (1x1) surfaces are unstable then there exists the further possibility that the (2x1) DL-TiO2 reconstruction [Natasha Erdman Nature (London) 419, 55 (2002)] is stable in a TiO2-rich environment and for p(O2)>10(-18) atm.

Epipolar geometry estimation based on evolutionary agents

This paper presents a novel approach based on the use of evolutionary agents for epipolar geometry estimation. In contrast to conventional nonlinear optimization methods, the proposed technique employs each agent to denote a minimal subset to compute the fundamental matrix, and considers the data set of correspondences as a 1D cellular environment, in which the agents inhabit and evolve. The agents execute some evolutionary behavior, and evolve autonomously in a vast solution space to reach the optimal (or near optima) result. Then three different techniques are proposed in order to improve the searching ability and computational efficiency of the original agents. Subset template enables agents to collaborate more efficiently with each other, and inherit accurate information from the whole agent set. Competitive evolutionary agent (CEA) and finite multiple evolutionary agent (FMEA) apply a better evolutionary strategy or decision rule, and focus on different aspects of the evolutionary process. Experimental results with both synthetic data and real images show that the proposed agent-based approaches perform better than other typical methods in terms of accuracy and speed, and are more robust to noise and outliers.

Thresholding of noisy shoeprint images based on pixel context

In a typical shoeprint classification and retrieval system, the first step is to segment meaningful basic shapes and patterns in a noisy shoeprint image. This step has significant influence on shape descriptors and shoeprint indexing in the later stages. In this paper, we extend a recently developed denoising technique proposed by Buades, called non-local mean filtering, to give a more general model. In this model, the expected result of an operation on a pixel can be estimated by performing the same operation on all of its reference pixels in the same image. A working pixel’s reference pixels are those pixels whose neighbourhoods are similar to the working pixel’s neighbourhood. Similarity is based on the correlation between the local neighbourhoods of the working pixel and the reference pixel. We incorporate a special instance of this general case into thresholding a very noisy shoeprint image. Visual and quantitative comparisons with two benchmarking techniques, by Otsu and Kittler, are conducted in the last section, giving evidence of the effectiveness of our method for thresholding noisy shoeprint images.