Pentadecane functionalized graphite oxide sheets as a tool for the preparation of electrical conductive polyethylene/graphite oxide composites

Covalent functionalization of pentadecane-decorated thermally reduced graphite oxide (GO) sheets has been studied as a tool for the preparation of polyethylene/GO composites exhibiting rheological and electrical percolation thresholds. It was accomplished through pentadecane based radical addition onto unsaturated bonds located on the GO sheets' surface using dicumyl peroxide as hydrogen abstractor. This chemical functionalization influences the affinity of the formed pentadecane grafted GO sheets for various solvents. Then, the compounding of the composites pentadecane grafted GO/PE was performed at a processing temperature of 140 °C with 25, 20, 15, 10, 8 and 5 wt% loadings. Rheological and electrical percolation thresholds were found between 10 and 15 wt% for polyethylene/pentadecane functionalized graphene oxide composites while the composite graphite/PE at the same loading percentage did not reach any percolation threshold.     

1,2,3-Triazolium-based poly(acrylate ionic liquid)s

Nitroxide-mediated radical polymerization of a tailor-made acrylate carrying a 1,2,3-triazole group with an undecanoyl spacer affords a well-defined (M_n = 7860 g mol⁻¹ and D = 1.39) neutral polyacrylate precursor. A series of 1,2,3-triazolium-based poly(ionic liquid)s (TPILs) is then obtained by straightforward quaternization of the 1,2,3-triazole groups with methyl iodide and subsequent anion metathesis reactions. Among the prepared materials, TPIL with bis(trifluoromethane)sulfonimide anion exhibits low glass transition temperature (T_g = −40 °C), high thermal stability (T_d10 = 325 °C) and anhydrous ionic conductivity of 4 × 10⁻⁶ S cm⁻¹ at 30 °C, as measured by differential scanning calorimetry, thermogravimetric analysis and broadband dielectric spectroscopy, respectively.     

Electricity generation in microbial fuel cell systems with Thiobacillus ferrooxidans as the cathode microorganism

Microbial fuel cells (MFC) are systems that enable biochemical activities of bacteria to generate the electricity. These systems are of great interest because of their designs that enable biological activity in organic wastes to be transformed into direct electrical energy. In order to increase the commercial usage of MFCs, it is necessary to increase the power output of the system. So as to improve MFC performance, used material selection, the pH value of the used bacterial medium and the choice of the appropriate substrate are very important. In this study, oxidation bacteria Thiobacillus ferrooxidans on the cathode and mixed culture bacteria on the anode of MFC were used. Different anode and cathode pH values were examined in MFC. Best open circuit potential result (0.8 V) was obtained at anode pH 8 and cathode pH 2 conditions. In addition, three different substrates had been used in the anode. In the conditions of acetate the most stable and high valued curve was obtained. The open circuit potential had reached 0.726 V, and power density had reached 0.88 mW/cm².     

Non-asymptotic confidence bounds for the optimal value of a stochastic program

We discuss a general approach to building non-asymptotic confidence bounds for Stochastic Optimization problems. Our principal contribution is the observation that a Sample Average Approximation of a problem supplies upper and lower bounds for the optimal value of the problem which are essentially better than the quality of the corresponding optimal solutions. At the same time, such bounds are more reliable than ‘standard’ confidence bounds obtained through the asymptotic approach. We also discuss bounding the optimal value of MinMax Stochastic Optimization and stochastically constrained problems. We conclude with a simulation study illustrating the numerical behaviour of the proposed bounds.     

An interior point technique for solving bilevel programming problems

This paper deals with bilevel programs with strictly convex lower level problems. We present the theoretical basis of a kind of necessary and sufficient optimality conditions that involve a single-level mathematical program satisfying the linear independence constraint qualification. These conditions are obtained by replacing the inner problem by their optimality conditions and relaxing their inequality constraints. An algorithm for the bilevel program, based on a well known technique for classical smooth constrained optimization, is also studied. The algorithm obtains a solution of this problem with an effort similar to that required by a classical well-behaved nonlinear constrained optimization problem. Several illustrative problems which include linear, quadratic and general nonlinear functions and constraints are solved, and very good results are obtained for all cases.     

Zirconium and hafnium oxide interface with silicon: Computational study of stress and strain effects

Computations of the periodic double-interface MO₂/Si (M = Zr and Hf) systems using density functional theory (DFT) demonstrate that very thin epitaxial MO₂ layers at silicon (1 0 0) surface adopt anatase-like structure with six-coordinate M atoms, while tetragonal-like MO₂ structure with seven-coordinated M atoms has a lower stability (metastable). This results from a closer match between Si(1 0 0) surface unit cell and anatase-like ZrO₂ and HfO₂ leading to the overall stress reduction if MO₂ adopts the anatase-like structure on silicon. In agreement with the previous study of Gavartin et al. [Gavartin, Fonseca, Bersuker, Shluger, Microelectron. Eng. 80 (2005) 412–415] formation of oxygen vacancy at the interface is demonstrated to be lower than in bulk film. This result appears to be independent from the film or interface structure but originates from the energy balance between M–O, M–M, Si–O and M–Si bonds, as revealed by a simple molecular model.     

Positional control of catalyst nanoparticles for the synthesis of high density carbon nanofiber arrays

Precise arrangement of nanoscale elements within larger systems, is essential to controlling higher order functionality and tailoring nanophase material properties. Here, we present findings on growth conditions for vertically aligned carbon nanofibers that enable synthesis of high density arrays and individual rows of nanofibers, which could be used to form barriers for restricting molecular transport, that have regular spacings and few defects. Growth through plasma-enhanced chemical vapor deposition was initiated from precisely formed nickel catalyst dots of varying diameter and spacing that were patterned through electron beam lithography. Nanofiber growth conditions, including power, precursor gas ratio, growth temperature and pressure were varied to optimize fiber uniformity and minimize defects that result from formation and migration of catalyst particles prior to growth. It was determined that both catalyst dot diameter and initial plasma power have a considerable influence on the number and severity of defects, while growth temperature, gas ratio (C₂H₂:NH₃) and pressure can be varied within a considerable range to fine-tune nanofiber morphology.     

A biophysical implementation of a bidirectional graph search algorithm to solve multiple goal navigation tasks

The model presented here extends formal analysis (Hasselmo et al., Neural Networks, 15, pp. 689-707, 2002b) and abstract modelling (Gorchetchnikov and Hasselmo, Neurocomputing, 44-46, pp. 423-427, 2002a) of interactions within the hippocampal area (or other cortical areas), which can be flexibly used to navigate toward any arbitrary goal or multiple goals that change on a trial-by-trial basis. The algorithm is a version of a bidirectional breadth-first graph search implemented in simulated neurons using two flows of neural activity. The new model changes the continuous firing rate neuronal representations (Gorchetchnikov and Hasselmo 2002a) to more detailed compartmental versions with realistic parameters, while preserving the qualitative properties analysed previously (Hasselmo et al., 2002b, Gorchetchnikov and Hasselmo 2002a). The case of multiple goals being present in the environment is studied in this paper. The first set of simulations tests the algorithm in the selection of the closest goal. A small difference in distance between the simulated animal and different goals is sufficient for a correct selection. The second set of simulations studies the behaviour of the model when the goals have different saliences. A small salience-based difference between firing rates of the cells providing goal-related input to the model is sufficient for the selection of a more salient goal. This behaviour was tested in three types of environments: a linear track, a T-maze and an open field. Further investigation of quantitative properties of the model should allow it to handle cases when the exact location of the goal is uncertain.     

Nonlinear black-box modeling in system identification: a unified overview

A nonlinear black-box structure for a dynamical system is a model structure that is prepared to describe virtually any nonlinear dynamics. There has been considerable recent interest in this area, with structures based on neural networks, radial basis networks, wavelet networks and hinging hyperplanes, as well as wavelet-transform-based methods and models based on fuzzy sets and fuzzy rules. This paper describes all these approaches in a common framework, from a user's perspective. It focuses on what are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques. It is pointed out that the nonlinear structures can be seen as a concatenation of a mapping form observed data to a regression vector and a nonlinear mapping from the regressor space to the output space. These mappings are discussed separately. The latter mapping is usually formed as a basis function expansion. The basis functions are typically formed from one simple scalar function, which is modified in terms of scale and location. The expansion from the scalar argument to the regressor space is achieved by a radial- or a ridge-type approach. Basic techniques for estimating the parameters in the structures are criterion minimization, as well as two-step procedures, where first the relevant basis functions are determined, using data, and then a linear least-squares step to determine the coordinates of the function approximation. A particular problem is to deal with the large number of potentially necessary parameters. This is handled by making the number of ‘used’ parameters considerably less than the number of ‘offered’ parameters, by regularization, shrinking, pruning or regressor selection.     

Robust stability of systems with delayed feedback

Some issues in the stability of differential delay systems in the linear and the nonlinear case are investigated. In particular, sufficient robustness conditions are derived under which a system remains stable, independent of the length of the delay(s). Applications in the design of delayed feedback systems are given. Two approaches are presented, one based on Lyapunov theory, the other on a transformation to Jordan form. In the former, sufficient conditions are obtained in the form of certain Riccati-type equations.