Attraction of iodide ions by the free water surface, revealed by simulations with a polarizable force field based on Drude oscillators

Recent theoretical and experimental studies have shown that polarizable anions, such as iodide and bromide, preferentially accumulate close to the surface of electrolyte solutions. This finding is in sharp contrast to the previously prevailing idea that salts are dielectrically excluded from the free water surface and opens up new avenues for research in specific salt effects. In this work, we have verified the ability of a recently introduced polarizable water model, SWM4-DP, to reproduce this behavior, by simulations of a NaI/water slab, corresponding to a 1.2 M solution. The water and ion polarizabilities are modeled by classical Drude oscillator particles. As revealed by the simulations, a double layer is formed close to the free water surface, with the iodide ions located closer to the interface and the sodium ions at a neighboring, interior layer. Near the surface, all solution species acquire an induced dipole moment, that is perpendicular to the surface and points toward the exterior. The double charge layer causes ordering of water at a subsurface region. Simulations with a simpler system of a single iodide ion in a water slab show that the surface position is stabilized by induced charge interactions; in contrast, the charge-dipole interactions between the iodide permanent charge and the water permanent dipole moment favor the bulk position. Thus, the polarizabilities of ion and water are essential for explaining the increased preference of iodide for the air-water interface, in accordance with other studies.     

Study of copper sulfide crystallization in PEO-SDS solutions

The crystallization of copper sulfide in aqueous supersaturated solutions in the presence of the polymer poly(ethylene oxide), PEO, and the surfactant sodium dodecyl sulfate, SDS, was investigated. In these systems, copper sulfide precipitation competes with the reaction between copper cations and dodecyl sulfate anions. The competition of the two reactions may affect the reaction products significantly; therefore it is important to study the properties of the surfactant salt, copper dodecyl sulfate (Cu(DS)2), in detail. The thermodynamic solubility constant of Cu(DS)2 was measured at 8 degrees C and was equal to (2.4 +/- 0.4) x 10(-10) M3. The Krafft point of Cu(DS)2 and its solubility curve (precipitation temperature for a range of concentrations) were also measured. The latter was found to be very close to room temperature. Temperature is thus a very significant parameter in these systems and must be carefully controlled in all experiments. The crystallization of copper sulfide in PEO-SDS solutions was investigated in solutions with compositions above and below the solubility curve. Copper sulfide nanoparticles predominate and are stabilized at temperatures above the solubility curve. Surprisingly, at temperatures below the solubility curve CuxS coexists with Cu(DS)2, which appears in the form of lamellar crystals. The system is further complicated by the presence of at least two different types of copper sulfides corresponding to different oxidation states of copper. Our results suggest that the predominance of Cu(DS)2 at lower temperatures is due to its limited solubility and is modified by the CuI/CuII redox equilibrium in combination with the solution pH.     

Modelling of discontinuities through dipole distribution

An indirect boundary element method using dipole distribution is employed in order to model discontinuities inside the flow region. The problem of flow under a dam is treated with a sheet-pile in its foundation. The discontinuity across the sheet-pile is demonstrated, a general boundary element procedure for a mixed problem is outlined and the coefficients of the linear system are given in analytical form. Very good agreement with existing analytical results is obtained.     

Genetic algorithms and cellular automata in aquifer management

A groundwater management problem is presented involving pumping cost minimization with both well discharges and well locations as decision variables. A grid of candidate well locations is set up and optimal arrangements of wells are sought within this discrete space. A genetic algorithm approach is presented with the following particular features: (a) A suitable scaling is applied to the objective function in order to alleviate its regionally flat behavior. (b) No penalty functions are involved in constraint handling. Instead, the feasible region is transformed into a rectangular domain. The transformation introduced is proved to be bijective. (c) A binary representation of well configurations is presented and compared to a combinatorial one. The binary representation necessitates the introduction of specially designed genetic operators. Besides purely genetic algorithms, the concept of cellular automaton is introduced as the basis of an alternative formulation of the optimization problem. The lattice of the cellular automaton provides the discrete set of candidate well positions. The well configuration is represented by a group of agents occupying an equal number of lattice sites. The agents change positions as dictated by the structure of the automaton and, also, by an associated genetic algorithm, which directs the evolution of the whole scheme toward an optimal configuration. An improved performance of this approach is noted and discussed in comparison to the purely genetic algorithm schemes of the present work. A simulated annealing approach is also applied to the same problem for comparison purposes. Finally, a new and more efficient hybrid annealing–genetic approach is introduced and discussed.     

Combined land-use and water allocation planning

A general framework for a combined land use and water management is described. An optimization problem is formulated that combines combinatorial and spatial characteristics. The aim of the planning is to maximize economic benefit, while minimizing water extraction and transportation cost under ecological constraints. A genetic algorithm is employed endowed with a new neighborhood operator. This operator acts on a local level, but it produces global results. Although the computational scheme does not include compactness as a separate objective, compact patterns are produced as emergent results. The algorithm is tested on a fictive area represented as a grid with 15×15 land blocks and, also, on a real-world case study.     

A new multi-objective self-organizing optimization algorithm (MOSOA) for spatial optimization problems

Spatial planning is an important and complex activity. It includes land use planning and resource allocation as basic components. An abundance of papers can be found in the literature related to each one of these two aspects separately. On the contrary, a much smaller number of research reports deal with both aspects simultaneously. This paper presents an innovative evolutionary algorithm for treating combined land use planning and resource allocation problems. The new algorithm performs optimization on a cellular automaton domain, applying suitable transition rules on the individual neighbourhoods. The optimization process is multi-objective, based on non-domination criteria and self-organizing. It produces a Pareto front thus offering an advantage to the decision maker, in comparison to methods based on weighted-sum objective functions. Moreover, the present multi-objective self-organizing algorithm (MOSOA) can handle both local and global spatial constraints. A combined land use and water allocation problem is treated, in order to illustrate the cellular automaton optimization approach. Water is allocated after pumping from an aquifer, thus contributing a nonlinearity to the objective function. The problem is bi-objective aiming at (a) the minimization of soil and groundwater pollution and (b) the maximization of economic profit. An ecological and a socioeconomic constraint are imposed: (a) Groundwater levels at selected places are kept above prescribed thresholds. (b) Land use quota is predefined. MOSOA is compared to a standard multi-objective genetic algorithm and is shown to yield better results both with respect to the Pareto front and to the degree of compactness. The latter is a highly desirable feature of a land use pattern. In the land use literature, compactness is part of the objective function or of the constraints. In contrast, the present approach renders compactness as an emergent result.