Iron and acid removal from acid mine drainage in open limestone systems

Passive systems, like wetlands, anoxic limestone drains, and open limestone channels, show promise for treating acid mine drainage (AMD) discharges. Open limestone channels have been developed for over ten years and the treatment effectiveness of AMD by open limestone systems shows wide variation. The variation is due to site conditions (slope and size of the limestone channel) but more importantly to the surface area of the limestone and iron concentrations in the water. Iron in AMD coats the limestone in open limestone systems thereby reducing the surface area available for reaction. Over time, the limestone appears to lose its ability to neutralize acidity and the water quality from open limestone systems is hard to predict.;This study was conducted to understand the effects of limestone surface area, iron concentration of the AMD, and coating thickness of iron on limestone neutralization of AMD. Experiments on limestone surface area showed that the required reaction time to reach a desired final pH is a function of limestone mass specific area, and initial and final acidity of proton acid solutions. The required reaction time, t = aAMS k, can be used to predict the outlet water quality for a hydrogen acid solution in an open limestone system based on a specified limestone particle size and lifetime.;Effects of iron concentration on neutralization of acid solutions by limestone were also researched by batch experiments in the laboratory. The required reaction time for neutralization was modified from previously developed equations by: tFe=tFe= 0+42ln Fe+35.1 pHf-176ln Fe-143 where t(Fe) is the required reaction time for iron acid solutions, t(Fe = 0) is the reaction time for hydrogen acid solutions, [Fe] is the iron concentration, and pHf is the final pH. In addition, equations were established to estimate limestone neutralization of iron acid solutions, [Ca]Fe = 0.309CT + 0.27[Fe3+] i + 0.448, where [Ca]Fe is the total dissolved Ca in mmol/L, CT is the total initial acidity (meq/L), and [Fe3+] i is the initial iron concentration (mmol/L).;Thicknesses of the iron coatings on limestone effects on acid neutralization also were evaluated. By the use of different coating thicknesses on limestone, the required reaction time can be calculated by: t=fpHi,pH f,Fe3+ ,AMS,h where t is required reaction time (min), pHf is the final outlet pH, pHi is initial pH of the acid solution, A MS is specific surface area of limestone particles, [Fe3+] is ferric iron concentration of the acid solution (mmol/L), and eta is the term representing the coating effects on limestone dissolution.;With these equations, open limestone channels can be designed with optimized surface area to meet a specified outlet water pH. Predictions can also be made as to the longevity of the treatment and when channels may need replacement

2-{2,6-Bis[bis(4-fluorophenyl)methyl]-4-chlorophenylimino} -3-aryliminobutylnickel(II) bromide complexes: Synthesis, characterization, and investigation of their catalytic behavior

The series of 2-{2,6-bis[di(4-fluorophenyl)methyl]-4-chlorophenylimino}-3- aryliminobutane derivatives (L1-L5) and their nickel(II) dibromide complexes (Ni1-Ni5) were synthesized, and all organic compounds were fully characterized by the Fourier transform infrared (FT-IR) and nuclear magnetic resonance (NMR) spectroscopy and by elemental analysis, while the nickel complexes were characterized by FT-IR spectroscopy, elemental analysis, as well as by single-crystal X-ray diffraction for two representative examples, namely Ni1 and Ni4. A distorted tetrahedral geometry was observed for these four-coordinate nickel complexes. Upon the activation with either Methylaluminoxane or modified methylaluminoxane as co-catalyst, all nickel complex precatalysts showed very high activity toward ethylene polymerization with activities of up to 10 7 g(PE)·mol -1 (Ni)·h -1 , and afforded highly branched polyethylene with a bimodal distribution. © 2014 Elsevier B.V

Precise influence evaluation in complex networks

Evaluating node influence is fundamental for identifying key nodes in complex networks. Existing methods typically rely on generic indicators to rank node influence across diverse networks, thereby ignoring the individualized features of each network itself. Actually, node influence stems not only from general features but the multi-scale individualized information encompassing specific network structure and task. Here we design an active learning architecture to predict node influence quantitively and precisely, which samples representative nodes based on graph entropy correlation matrix integrating multi-scale individualized information. This brings two intuitive advantages: (1) discovering potential high-influence but weak-connected nodes that are usually ignored in existing methods, (2) improving the influence maximization strategy by deducing influence interference. Significantly, our architecture demonstrates exceptional transfer learning capabilities across multiple types of networks, which can identify those key nodes with large disputation across different existing methods. Additionally, our approach, combined with a simple greedy algorithm, exhibits dominant performance in solving the influence maximization problem. This architecture holds great potential for applications in graph mining and prediction tasks