Characterization of a high-density, direct-current reflex discharge plasma source operating in Ar and N-2

The characterization of a direct current, low-pressure, and high-density reflex discharge plasma source operating in argon and in nitrogen, over a range of pressures 1.0-10(-2) mbar, discharge currents 20-200 mA, and magnetic fields 0-120 G, and its parametric characterization is presented. Both external parameters, such as the breakdown potential and the discharge voltage-current characteristic, and internal parameters, like the charge carrier's temperature and density, plasma potential, floating potential, and electron energy distribution function, were measured. The electron energy distribution functions are bi-Maxwellian, but some structure is observed in these functions in nitrogen plasmas. There is experimental evidence for the existence of three groups of electrons within this reflex discharge plasma. Due to the enhanced hollow cathode effect by the magnetic trapping of electrons, the density of the cold group of electrons is as high as 10(18) m(-3), and the temperature is as low as a few tenths of an electron volt. The bulk plasma density scales with the dissipated power. Another important feature of this reflex plasma source is its high degree of uniformity, while the discharge bulk region is free of electric field. (C) 2002 American Institute of Physics.

Space and phase resolved optical emission in mode transitions of radio-frequency inductively coupled plasmas

Inductively coupled radio-frequency plasmas can be operated in two distinct modes. At low power and comparatively low plasma densities the plasma is sustained in capacitive mode (E-mode). As the plasma density increases a transition to inductive mode (H-mode) is observed. This transition region is of particular interest and governed by non-linear dynamics, which under certain conditions results in structure formation with strong spatial gradients in light emission. These modes show pronounced differences is various measureable quantities e.g. electron densities, electron energy distribution functions, ion energy distribution functions, dynamics of optical light emission. Here the transition from E- to H- mode in an oxygen containing inductively coupled plasma (ICP) is investigated using space and phase resolved optical emission spectroscopy (PROES). The emission, measured phase resolved, allows investigation of the electron dynamics within the rf cycle, important for understanding the power coupling and ionization mechanisms in the discharge. The temporal variation of the emission reflects the dynamics of relatively high-energy electrons. It is possible to distinguish between E- and H-mode from the intensity and temporal behaviour of the emission.

Arbitrary amplitude ion-acoustic solitary excitations in the presence of excess superthermal electrons

Velocity distribution functions with an excess of superthermal particles are commonly observed in space plasmas, and are effectively modeled by a kappa distribution. They are also found in some laboratory experiments. In this paper we obtain existence conditions for and some characteristics of ion-acoustic solitary waves in a plasma composed of cold ions and kappa-distributed electrons, where kappa>3/2 represents the spectral index. As is the case for the usual Maxwell-Boltzmann electrons, only positive potential solitons are found, and, as expected, in the limit of large kappa one recovers the usual range of possible soliton Mach numbers, viz., 1 < M < 1.58. For lower values of kappa, modeling the presence of a greater superthermal component, the range of accessible Mach numbers is reduced. It is found that the amplitude of the largest possible solitons that may be generated in a given plasma (corresponding to the highest allowed Mach number for the given plasma composition) falls off with decreasing kappa, i.e., an increasing superthermal component. On the other hand, at fixed Mach number, both soliton amplitude and profile steepness increase as kappa is decreased. These changes are seen to be important particularly for kappa < 4, i.e., when the electrons have a "hard" spectrum.

Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.

Computational Verification of Two Universal Relations for Simple Ionic Liquids. Kinetic Properties of a Model 2:1 Molten Salt

Two semianalytical relations [Nature, 1996, 381, 137 and Phys. Rev. Lett. 2001, 87, 245901] predicting dynamical coefficients of simple liquids on the basis of structural properties have been tested by extensive molecular dynamics simulations for an idealized 2:1 model molten salt. In agreement with previous simulation studies, our results support the validity of the relation expressing the self-diffusion coefficient as a Function of the radial distribution functions for all thermodynamic conditions such that the system is in the ionic (ie., fully dissociated) liquid state. Deviations are apparent for high-density samples in the amorphous state and in the low-density, low-temperature range, when ions condense into AB(2) molecules. A similar relation predicting the ionic conductivity is only partially validated by our data. The simulation results, covering 210 distinct thermodynamic states, represent an extended database to tune and validate semianalytical theories of dynamical properties and provide a baseline for the interpretation of properties of more complex systems such as the room-temperature ionic liquids.

A Neutron Diffraction and Molecular Dynamics Investigation of Acetate-based Ionic Liquids as Solvents for Glucose

The liquid state structure of the ionic liquid, 1-ethyl-3-methylimidazolium acetate, and the solute/solvent structure of glucose dissolved in the ionic liquid at a 1: 6 molar ratio have been investigated at 323 K by molecular dynamics simulations and neutron diffraction experiments using H/D isotopically substituted materials. Interactions between hydrogen-bond donating cation sites and polar, directional hydrogen-bond accepting acetate anions are examined. Ion-ion radial distribution functions for the neat ionic liquid, calculated from both MD and derived from the empirical potential structure refinement model to the experimental data, show the alternating shell-structure of anions around the cation, as anticipated. Spatial probability distributions reveal the main anion-to-cation features as in-plane interactions of anions with imidazolium ring hydrogens and cation-cation planar stacking. Interestingly, the presence of the polarised hydrogen-bond acceptor anion leads to increased anion-anion tail-tail structuring within each anion shell, indicating the onset of hydrophobic regions within the anion regions of the liquid.

Comparison of methods used to calculate typical threshold values for potentially toxic elements in soil

The environmental quality of land can be assessed by calculating relevant threshold values, which differentiate between concentrations of elements resulting from geogenic and diffuse anthropogenic sources and concentrations generated by point sources of elements. A simple process allowing the calculation of these typical threshold values (TTVs) was applied across a region of highly complex geology (Northern Ireland) to six elements of interest; arsenic, chromium, copper, lead, nickel and vanadium. Three methods for identifying domains (areas where a readily identifiable factor can be shown to control the concentration of an element) were used: k-means cluster analysis, boxplots and empirical cumulative distribution functions (ECDF). The ECDF method was most efficient at determining areas of both elevated and reduced concentrations and was used to identify domains in this investigation. Two statistical methods for calculating normal background concentrations (NBCs) and upper limits of geochemical baseline variation (ULBLs), currently used in conjunction with legislative regimes in the UK and Finland respectively, were applied within each domain. The NBC methodology was constructed to run within a specific legislative framework, and its use on this soil geochemical data set was influenced by the presence of skewed distributions and outliers. In contrast, the ULBL methodology was found to calculate more appropriate TTVs that were generally more conservative than the NBCs. TTVs indicate what a "typical" concentration of an element would be within a defined geographical area and should be considered alongside the risk that each of the elements pose in these areas to determine potential risk to receptors.

Efficient Marginal Likelihood Computation for Gaussian Process Regression

In a Bayesian learning setting, the posterior distribution of a predictive model arises from a trade-off between its prior distribution and the conditional likelihood of observed data. Such distribution functions usually rely on additional hyperparameters which need to be tuned in order to achieve optimum predictive performance; this operation can be efficiently performed in an Empirical Bayes fashion by maximizing the posterior marginal likelihood of the observed data. Since the score function of this optimization problem is in general characterized by the presence of local optima, it is necessary to resort to global optimization strategies, which require a large number of function evaluations. Given that the evaluation is usually computationally intensive and badly scaled with respect to the dataset size, the maximum number of observations that can be treated simultaneously is quite limited. In this paper, we consider the case of hyperparameter tuning in Gaussian process regression. A straightforward implementation of the posterior log-likelihood for this model requires O(N^3) operations for every iteration of the optimization procedure, where N is the number of examples in the input dataset. We derive a novel set of identities that allow, after an initial overhead of O(N^3), the evaluation of the score function, as well as the Jacobian and Hessian matrices, in O(N) operations. We prove how the proposed identities, that follow from the eigendecomposition of the kernel matrix, yield a reduction of several orders of magnitude in the computation time for the hyperparameter optimization problem. Notably, the proposed solution provides computational advantages even with respect to state of the art approximations that rely on sparse kernel matrices.

Vlasov-kinetic computer simulations of electrostatic waves in dusty plasmas: an overview of recent results

A series of numerical simulations based on a recurrence-free Vlasov kinetic model using kinetic phase point trajectories are presented. Electron-ion plasmas and three-component (electron-ion-dust) dusty or complex plasmas are considered, via independent simulations. Considering all plasma components modeled through a kinetic approach, the linear and nonlinear behavior of ion-acoustic excitations is investigated. Maxwellian and kappa-type (superthermal) distribution functions are assumed, as initial conditions, in separate simulations for the sake of comparison. The focus is on the parametric dependence of ion-acoustic waves on the electron-to-ion temperature ratio and on the dust concentration. © 2014 EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg.

Neutron Scattering of Aromatic and Aliphatic Liquids

Organic solvents, such as cyclohexane, cyclohexene, methylcyclohexane, benzene and toluene, are widely used as both reagents and solvents in industrial processes. Despite the ubiquity of these liquids, the local structures that govern the chemical properties have not been studied extensively. Herein, we report neutron diffraction measurements on liquid cyclohexane, cyclohexene, methylcyclohexane, benzene and toluene at 298 K to obtain a detailed description of the local structure in these compounds. The radial distribution functions of the centres of the molecules, as well as the partial distribution functions for the double bond for cyclohexene and methyl group for methylcyclohexane and toluene have been calculated. Additionally, probability density functions and angular radial distribution functions were extracted to provide a full description of the local structure within the chosen liquids. Structural motifs are discussed and compared for all liquids, referring specifically to the functional group and aromaticity present in the different liquids.

Elucidation of a mechanism of cell lysis by chorhexidine : a biophysical approach

Chlorhexidine is an effective antiseptic used widely in disinfecting products (hand soap), oral products (mouthwash), and is known to have potential applications in the textile industry. Chlorhexidine has been studied extensively through a biological and biochemical lens, showing evidence that it attacks the semipermeable membrane in bacterial cells. Although extremely lethal to bacterial cells, the present understanding of the exact mode of action of chlorhexidine is incomplete. A biophysical approach has been taken to investigate the potential location of chlorhexidine in the lipid bilayer. Deuterium nuclear magnetic resonance was used to characterize the molecular arrangement of mixed phospholipid/drug formulations. Powder spectra were analyzed using the de-Pake-ing technique, a method capable of extracting both the orientation distribution and the anisotropy distribution functions simultaneously. The results from samples of protonated phospholipids mixed with deuterium-labelled chlorhexidine are compared to those from samples of deuterated phospholipids and protonated chlorhexidine to determine its location in the lipid bilayer. A series of neutron scattering experiments were also conducted to study the biophysical interaction of chlorhexidine with a model phospholipid membrane of DMPC, a common saturated lipid found in bacterial cell membranes. The results found the hexamethylene linker to be located at the depth of the glycerol/phosphate region of the lipid bilayer. As drug concentration was increased in samples, a dramatic decrease in bilayer thickness was observed. Differential scanning calorimetry experiments have revealed a depression of the DMPC bilayer gel-to-lamellar phase transition temperature with an increasing drug concentration. The enthalpy of the transition remained the same for all drug concentrations, indicating a strictly drug/headgroup interaction, thus supporting the proposed location of chlorhexidine. In combination, these results lead to the hypothesis that the drug is folded approximately in half on its hexamethylene linker, with the hydrophobic linker at the depth of the glycerol/phosphate region of the lipid bilayer and the hydrophilic chlorophenyl groups located at the lipid headgroup. This arrangement seems to suggest that the drug molecule acts as a wedge to disrupt the bilayer. In vivo, this should make the cell membrane leaky, which is in agreement with a wide range of bacteriological observations.

Exact Nonparametric Two-Sample Homogeneity Tests for Possibly Discrete Distributions.

In this paper, we study several tests for the equality of two unknown distributions. Two are based on empirical distribution functions, three others on nonparametric probability density estimates, and the last ones on differences between sample moments. We suggest controlling the size of such tests (under nonparametric assumptions) by using permutational versions of the tests jointly with the method of Monte Carlo tests properly adjusted to deal with discrete distributions. We also propose a combined test procedure, whose level is again perfectly controlled through the Monte Carlo test technique and has better power properties than the individual tests that are combined. Finally, in a simulation experiment, we show that the technique suggested provides perfect control of test size and that the new tests proposed can yield sizeable power improvements.

Quantile based entropy function

Quantile functions are efficient and equivalent alternatives to distribution functions in modeling and analysis of statistical data (see Gilchrist, 2000; Nair and Sankaran, 2009). Motivated by this, in the present paper, we introduce a quantile based Shannon entropy function. We also introduce residual entropy function in the quantile setup and study its properties. Unlike the residual entropy function due to Ebrahimi (1996), the residual quantile entropy function determines the quantile density function uniquely through a simple relationship. The measure is used to define two nonparametric classes of distributions

Profile simulations of gas chopping etching processes : model development and comparison with experiments

The progress in microsystem technology or nano technology places extended requirements to the fabrication processes. The trend is moving towards structuring within the nanometer scale on the one hand, and towards fabrication of structures with high aspect ratio (ratio of vertical vs. lateral dimensions) and large depths in the 100 µm scale on the other hand. Current procedures for the microstructuring of silicon are wet chemical etching and dry or plasma etching. A modern plasma etching technique for the structuring of silicon is the so-called "gas chopping" etching technique (also called "time-multiplexed etching"). In this etching technique, passivation cycles, which prevent lateral underetching of sidewalls, and etching cycles, which etch preferably in the vertical direction because of the sidewall passivation, are constantly alternated during the complete etching process. To do this, a CHF3/CH4 plasma, which generates CF monomeres is employed during the passivation cycle, and a SF6/Ar, which generates fluorine radicals and ions plasma is employed during the etching cycle. Depending on the requirements on the etched profile, the durations of the individual passivation and etching cycles are in the range of a few seconds up to several minutes. The profiles achieved with this etching process crucially depend on the flow of reactants, i.e. CF monomeres during the passivation cycle, and ions and fluorine radicals during the etching cycle, to the bottom of the profile, especially for profiles with high aspect ratio. With regard to the predictability of the etching processes, knowledge of the fundamental effects taking place during a gas chopping etching process, and their impact onto the resulting profile is required. For this purpose in the context of this work, a model for the description of the profile evolution of such etching processes is proposed, which considers the reactions (etching or deposition) at the sample surface on a phenomenological basis. Furthermore, the reactant transport inside the etching trench is modelled, based on angular distribution functions and on absorption probabilities at the sidewalls and bottom of the trench. A comparison of the simulated profiles with corresponding experimental profiles reveals that the proposed model reproduces the experimental profiles, if the angular distribution functions and absorption probabilities employed in the model is in agreement with data found in the literature. Therefor the model developed in the context of this work is an adequate description of the effects taking place during a gas chopping plasma etching process.

Bayes quadratic unbiased estimator of spatial covariance parameters

This work presents Bayes invariant quadratic unbiased estimator, for short BAIQUE. Bayesian approach is used here to estimate the covariance functions of the regionalized variables which appear in the spatial covariance structure in mixed linear model. Firstly a brief review of spatial process, variance covariance components structure and Bayesian inference is given, since this project deals with these concepts. Then the linear equations model corresponding to BAIQUE in the general case is formulated. That Bayes estimator of variance components with too many unknown parameters is complicated to be solved analytically. Hence, in order to facilitate the handling with this system, BAIQUE of spatial covariance model with two parameters is considered. Bayesian estimation arises as a solution of a linear equations system which requires the linearity of the covariance functions in the parameters. Here the availability of prior information on the parameters is assumed. This information includes apriori distribution functions which enable to find the first and the second moments matrix. The Bayesian estimation suggested here depends only on the second moment of the prior distribution. The estimation appears as a quadratic form y'Ay , where y is the vector of filtered data observations. This quadratic estimator is used to estimate the linear function of unknown variance components. The matrix A of BAIQUE plays an important role. If such a symmetrical matrix exists, then Bayes risk becomes minimal and the unbiasedness conditions are fulfilled. Therefore, the symmetry of this matrix is elaborated in this work. Through dealing with the infinite series of matrices, a representation of the matrix A is obtained which shows the symmetry of A. In this context, the largest singular value of the decomposed matrix of the infinite series is considered to deal with the convergence condition and also it is connected with Gerschgorin Discs and Poincare theorem. Then the BAIQUE model for some experimental designs is computed and compared. The comparison deals with different aspects, such as the influence of the position of the design points in a fixed interval. The designs that are considered are those with their points distributed in the interval [0, 1]. These experimental structures are compared with respect to the Bayes risk and norms of the matrices corresponding to distances, covariance structures and matrices which have to satisfy the convergence condition. Also different types of the regression functions and distance measurements are handled. The influence of scaling on the design points is studied, moreover, the influence of the covariance structure on the best design is investigated and different covariance structures are considered. Finally, BAIQUE is applied for real data. The corresponding outcomes are compared with the results of other methods for the same data. Thereby, the special BAIQUE, which estimates the general variance of the data, achieves a very close result to the classical empirical variance.