bluuebunny/crossref_metadata_2025 huggingface dataset:
NMDA-Based Pattern Discrimination in a Modeled Cortical Neuron
Authors: Mel Bartlett W.
Published: March 2008

Compartmental simulations of an anatomically characterized cortical pyramidal cell were carried out to study the integrative behavior of a complex dendritic tree. Previous theoretical (Feldman and Ballard 1982; Durbin and Rumelhart 1989; Mel 1990; Mel and Koch 1990; Poggio and Girosi 1990) and compartmental modeling (Koch et al. 1983; Shepherd et al. 1985; Koch and Poggio 1987; Rall and Segev 1987; Shepherd and Brayton 1987; Shepherd et al. 1989; Brown et al. 1991) work had suggested that multiplicative interactions among groups of neighboring synapses could greatly enhance the processing power of a neuron relative to a unit with only a single global firing threshold. This issue was investigated here, with a particular focus on the role of voltage-dependent N-methyl-D-asparate (NMDA) channels in the generation of cell responses. First, it was found that when a large proportion of the excitatory synaptic input to dendritic spines is carried by NMDA channels, the pyramidal cell responds preferentially to spatially clustered, rather than random, distributions of activated synapses. Second, based on this mechanism, the NMDA-rich neuron is shown to be capable of solving a nonlinear pattern discrimination task. We propose that manipulation of the spatial ordering of afferent synaptic connections onto the dendritic arbor is a possible biological strategy for pattern information storage during learning.
Four Types of Learning Curves
Authors: Amari Shun-ichi , Fujita Naotake , Shinomoto Shigeru
Published: March 2008

If machines are learning to make decisions given a number of examples, the generalization error ε(t) is defined as the average probability that an incorrect decision is made for a new example by a machine when trained with t examples. The generalization error decreases as t increases, and the curve ε(t) is called a learning curve. The present paper uses the Bayesian approach to show that given the annealed approximation, learning curves can be classified into four asymptotic types. If the machine is deterministic with noiseless teacher signals, then (1) ε ∼ at-1 when the correct machine parameter is unique, and (2) ε ∼ at-2 when the set of the correct parameters has a finite measure. If the teacher signals are noisy, then (3) ε ∼ at-1/2 for a deterministic machine, and (4) ε ∼ c + at-1 for a stochastic machine.
Nonlinear Dynamics and Symbolic Dynamics of Neural Networks
Authors: Lewis John E. , Glass Leon
Published: March 2008

A piecewise linear equation is proposed as a method of analysis of mathematical models of neural networks. A symbolic representation of the dynamics in this equation is given as a directed graph on an N-dimensional hypercube. This provides a formal link with discrete neural networks such as the original Hopfield models. Analytic criteria are given to establish steady states and limit cycle oscillations independent of network dimension. Model networks that display multiple stable limit cycles and chaotic dynamics are discussed. The results show that such equations are a useful and efficient method of investigating the behavior of neural networks.
Learning to Segment Images Using Dynamic Feature Binding
Authors: Mozer Michael C. , Zemel Richard S. , Behrmann Marlene , Williams Christopher K. I.
Published: March 2008

Despite the fact that complex visual scenes contain multiple, overlapping objects, people perform object recognition with ease and accuracy. One operation that facilitates recognition is an early segmentation process in which features of objects are grouped and labeled according to which object they belong. Current computational systems that perform this operation are based on predefined grouping heuristics. We describe a system called MAGIC that learns how to group features based on a set of presegmented examples. In many cases, MAGIC discovers grouping heuristics similar to those previously proposed, but it also has the capability of finding nonintuitive structural regularities in images. Grouping is performed by a relaxation network that attempts to dynamically bind related features. Features transmit a complex-valued signal (amplitude and phase) to one another; binding can thus be represented by phase locking related features. MAGIC's training procedure is a generalization of recurrent backpropagation to complex-valued units.
Stimulus-Dependent Assembly Formation of Oscillatory Responses: III. Learning
Authors: König Peter , Janosch Bernd , Schillen Thomas B.
Published: March 2008

A temporal structure of neuronal activity has been suggested as a potential mechanism for defining cell assemblies in the brain. This concept has recently gained support by the observation of stimulus-dependent oscillatory activity in the visual cortex of the cat. Furthermore, experimental evidence has been found showing the formation and segregation of synchronously oscillating cell assemblies in response to various stimulus conditions. In previous work, we have demonstrated that a network of neuronal oscillators coupled by synchronizing and desynchronizing delay connections can exhibit a temporal structure of responses, which closely resembles experimental observations. In this paper, we investigate the self-organization of synchronizing and desynchronizing coupling connections by local learning rules. Based on recent experimental observations, we modify synchronizing connections according to a two-threshold learning rule, involving synaptic potentiation and depression. This rule is generalized to its functional inverse for weight changes of desynchronizing connections. We show that after training, the resulting network exhibits stimulus-dependent formation and segregation of oscillatory assemblies in agreement with the experimental data. These results indicate that local learning rules during ontogenesis can suffice to develop a connectivity pattern in support of the observed temporal structure of stimulus responses in cat visual cortex.
Seeing Beyond the Nyquist Limit
Authors: Ruderman Daniel L. , Bialek William
Published: March 2008

In many biological systems the primary transduction of sensory stimuli occurs in a regular array of receptors. Because of this discrete sampling it is usually assumed that the organism has no knowledge of signals beyond the Nyquist frequency. In fact, higher frequency signals are expected to mask the available lower frequency information as a result of aliasing. It has been suggested that these considerations are important in understanding, for example, the design of the receptor lattice in the mammalian fovea. We show that if the organism has knowledge of the probability distribution from which the signals are drawn, outputs from a discrete receptor array can be used to estimate signals beyond the Nyquist limit. In effect, a priori knowledge can be used to de-alias the image, and the estimated signal above the Nyquist cutoff is in fact coherent with the real signal at these high frequencies. We address initially the problem of stimulus reconstruction from a noisy receptor array responding to a Gaussian stimulus ensemble. In this case, the best reconstruction strategy is a simple linear transformation. In the more interesting (and natural) case of nongaussian stimuli, optimal reconstruction requires nonlinear operations, but the higher order correlations in the stimulus ensemble can be used to improve the estimate of super-Nyquist signals.
Local Synaptic Learning Rules Suffice to Maximize Mutual Information in a Linear Network
Authors: Linsker Ralph
Published: March 2008

A network that develops to maximize the mutual information between its output and the signal portion of its input (which is admixed with noise) is useful for extracting salient input features, and may provide a model for aspects of biological neural network function. I describe a local synaptic Learning rule that performs stochastic gradient ascent in this information-theoretic quantity, for the case in which the input-output mapping is linear and the input signal and noise are multivariate gaussian. Feedforward connection strengths are modified by a Hebbian rule during a "learning" phase in which examples of input signal plus noise are presented to the network, and by an anti-Hebbian rule during an "unlearning" phase in which examples of noise alone are presented. Each recurrent lateral connection has two values of connection strength, one for each phase; these values are updated by an anti-Hebbian rule.
Learning Curves for Error Minimum and Maximum Likelihood Algorithms
Authors: Kabashima Y. , Shinomoto S.
Published: March 2008

For the problem of dividing the space originally partitioned by a blurred boundary, every learning algorithm can make the probability of incorrect prediction of an individual example ε decrease with the number of training examples t. We address here the question of how the asymptotic form of ε(t) as well as its limit of convergence reflect the choice of learning algorithms. The error minimum algorithm is found to exhibit rather slow convergence of ε(t) to its lower bound ε0, ε(t) - ε0 ∼ O(t-2/3). Even for the purpose of minimizing prediction error, the maximum likelihood algorithm can be utilized as an alternative. If the true probability distribution happens to be contained in the family of hypothetical functions, then the boundary estimated from the hypothetical distribution function eventually converges to the best choice. Convergence of the prediction error is then ε(t) - ε0 ∼ O(t-1). If the true distribution is not available from the algorithm, however, the boundary generally does not converge to the best choice, but instead ε(t) - ε1 ∼ ±O(t-1/2), where ε1 > ε0 > 0.
The Evidence Framework Applied to Classification Networks
Authors: MacKay David J. C.
Published: March 2008

Three Bayesian ideas are presented for supervised adaptive classifiers. First, it is argued that the output of a classifier should be obtained by marginalizing over the posterior distribution of the parameters; a simple approximation to this integral is proposed and demonstrated. This involves a "moderation" of the most probable classifier's outputs, and yields improved performance. Second, it is demonstrated that the Bayesian framework for model comparison described for regression models in MacKay (1992a,b) can also be applied to classification problems. This framework successfully chooses the magnitude of weight decay terms, and ranks solutions found using different numbers of hidden units. Third, an information-based data selection criterion is derived and demonstrated within this framework.
Rotor Neurons: Basic Formalism and Dynamics
Authors: Gislén Lars , Peterson Carsten , Söderberg Bo
Published: March 2008

Rotor neurons are introduced to encode states living on the surface of a sphere in D dimensions. Such rotors can be regarded as continuous generalizations of binary (Ising) neurons. The corresponding mean field equations are derived, and phase transition properties based on linearized dynamics are given. The power of this approach is illustrated with an optimization problem—placing N identical charges on a sphere such that the overall repulsive energy is minimized. The rotor approach appears superior to other methods for this problem both with respect to solution quality and computational effort needed.
Factoring Networks by a Statistical Method
Authors: Morgan Nelson , Bourlard Hervé
Published: March 2008

We show that it is possible to factor a multilayered classification network with a large output layer into a number of smaller networks, where the product of the sizes of the output layers equals the size of the original output layer. No assumptions of statistical independence are required.
Tight Bounds on Transition to Perfect Generalization in Perceptrons
Authors: Lyuu Yuh-Dauh , Rivin Igor
Published: March 2008

“Sudden” transition to perfect generalization in binary perceptrons is investigated. Building on recent theoretical works of Gardner and Derrida (1989) and Baum and Lyuu (1991), we show the following: for α > αc = 1.44797 …, if α n examples are drawn from the uniform distribution on {+1, −1}n and classified according to a target perceptron wt ∈ {+1, −1}n as positive if wt · x ≥ 0 and negative otherwise, then the expected number of nontarget perceptrons consistent with the examples is 2−⊖(√n); the same number, however, grows exponentially 2⊖(n) if α ≤ αc. Numerical calculations for n up to 1,000,002 are reported.
Learning Factorial Codes by Predictability Minimization
Authors: Schmidhuber Jürgen
Published: March 2008

I propose a novel general principle for unsupervised learning of distributed nonredundant internal representations of input patterns. The principle is based on two opposing forces. For each representational unit there is an adaptive predictor, which tries to predict the unit from the remaining units. In turn, each unit tries to react to the environment such that it minimizes its predictability. This encourages each unit to filter "abstract concepts" out of the environmental input such that these concepts are statistically independent of those on which the other units focus. I discuss various simple yet potentially powerful implementations of the principle that aim at finding binary factorial codes (Barlow et al. 1989), i.e., codes where the probability of the occurrence of a particular input is simply the product of the probabilities of the corresponding code symbols. Such codes are potentially relevant for (1) segmentation tasks, (2) speeding up supervised learning, and (3) novelty detection. Methods for finding factorial codes automatically implement Occam's razor for finding codes using a minimal number of units. Unlike previous methods the novel principle has a potential for removing not only linear but also nonlinear output redundancy. Illustrative experiments show that algorithms based on the principle of predictability minimization are practically feasible. The final part of this paper describes an entirely local algorithm that has a potential for learning unique representations of extended input sequences.
An Adaptive Lattice Architecture for Dynamic Multilayer Perceptrons
Authors: Back Andrew D. , Tsoi Ah Chung
Published: March 2008

Time-series modeling is a topic of growing interest in neural network research. Various methods have been proposed for extending the nonlinear approximation capabilities to time-series modeling problems. A multilayer perceptron (MLP) with a global-feedforward local-recurrent structure was recently introduced as a new approach to modeling dynamic systems. The network uses adaptive infinite impulse response (IIR) synapses (it is thus termed an IIR MLP), and was shown to have good modeling performance. One problem with linear IIR filters is that the rate of convergence depends on the covariance matrix of the input data. This extends to the IIR MLP: it learns well for white input signals, but converges more slowly with nonwhite inputs. To solve this problem, the adaptive lattice multilayer perceptron (AL MLP), is introduced. The network structure performs Gram-Schmidt orthogonalization on the input data to each synapse. The method is based on the same principles as the Gram-Schmidt neural net proposed by Orfanidis (1990b), but instead of using a network layer for the orthogonalization, each synapse comprises an adaptive lattice filter. A learning algorithm is derived for the network that minimizes a mean square error criterion. Simulations are presented to show that the network architecture significantly improves the learning rate when correlated input signals are present.
A "Thermal" Perceptron Learning Rule
Authors: Frean Marcus
Published: March 2008

The thermal perceptron is a simple extension to Rosenblatt's perceptron learning rule for training individual linear threshold units. It finds stable weights for nonseparable problems as well as separable ones. Experiments indicate that if a good initial setting for a temperature parameter, T0, has been found, then the thermal perceptron outperforms the Pocket algorithm and methods based on gradient descent. The learning rule stabilizes the weights (learns) over a fixed training period. For separable problems it finds separating weights much more quickly than the usual rules.
Formation of Ge Nanocrystals in Al2O3 Matrix
Authors: Yerci S. , Kulakci M. , Serincan U. , Turan R. , Shandalov M. , Golan Y.
Published: March 2008

Ge nanocrystals were formed in Al2O3 matrix by implantation of Ge ions into sapphire (α-Al2O3) substrates and subsequent annealing. Diagnostic techniques, Raman spectroscopy, XRD, TEM, EDS, and SAED were employed to monitor and
study formation of Ge nanocrystals and their evolution during heat treatments. TEM and EDS analysis revealed the diffusion of Ge ions into the substrate during annealing process. While Ge nanocrystals with mean sizes of 15 nm were observed in the heavily implanted region small nanocrystals
with mean sizes of 4 nm were identified underneath this region. Some grains of transition aluminas were formed in the implanted region which was amorphized during the implantation. Extensive stress between the transition aluminas and sapphire matrices and its effects on the matrix were detected.
The effect of stress on the Raman and XRD spectra of Ge nanocrystals was discussed.
Optical Absorption Processes in CdSe Nanocrystals Embedded in Silicate Glass and Organic Polymer Matrices Under 7-MeV Electron Irradiation
Authors: Azhniuk Yu. M. , Gomonnai A. V. , Lopushansky V. V. , Megela I. G. , Raevskaya A. E. , Stroyuk A. L. , Kuchmii S. Ya.
Published: March 2008

Irradiation-induced bleaching occurs in the spectra of glass-embedded CdSe nanocrystals due to their ionization resulting from charge transfer between them and color centers formed in glass by electron irradiation. No bleaching in the spectra of CdSe nanocrystals encapsulated in gelatine
is observed because no deep traps are formed in the matrix under electron irradiation.
Formation of Ge Nanocrystals in SiO2 by Electron Beam Evaporation
Authors: Basa P. , Molnár G. , Dobos L. , Pécz B. , Tóth L. , Tóth A. L. , Koós A. A. , Dózsa L. , Nemcsics Á. , Horváth Zs. J.
Published: March 2008

Ge nanocrystals were formed by electron beam evaporation on SiO2 covered Si substrates. The size and distribution of the nanocrystals were studied by atomic force microscopy, scanning electron microscopy and cross-sectional transmission electron microscopy. Dependencies of
the nanocrystal size, of the nanocrystal surface coverage, and sheet resistance obtained by van der Pauw method of the Ge layer have been found on the evaporation time. The suggested growth mechanism for the formation of nanocrystals is the Volmer-Weber type. The sheet resistance exhibited
a power dependence on the nanocrystal size.
A Figure of Merit for Optimization of Nanocrystal Flash Memory Design
Authors: Dâna Aykutlu , Akca Imran , Aydinli Atilla , Turan Rasit , Finstad Terje G.
Published: March 2008

Nanocrystals can be used as storage media for carriers in flash memories. The performance of a nanocrystal flash memory depends critically on the choice of nanocrystal size and density as well as on the choice of tunnel dielectric properties. The performance of a nanocrystal memory
device can be expressed in terms of write/erase speed, carrier retention time and cycling durability. We present a model that describes the charge/discharge dynamics of nanocrystal flash memories and calculate the effect of nanocrystal, gate, tunnel dielectric and substrate properties on device
performance. The model assumes charge storage in quantized energy levels of nanocrystals. Effect of temperature is included implicitly in the model through perturbation of the substrate minority carrier concentration and Fermi level. Because a large number of variables affect these performance
measures, in order to compare various designs, a figure of merit that measures the device performance in terms of design parameters is defined as a function of write/erase/discharge times which are calculated using the theoretical model. The effects of nanocrystal size and density, gate work
function, substrate doping, control and tunnel dielectric properties and device geometry on the device performance are evaluated through the figure of merit. Experimental data showing agreement of the theoretical model with the measurement results are presented for devices that has PECVD grown
germanium nanocrystals as the storage media.
Growth and Characterization of Titanium Silicate Nanofilms for Gate Oxide Applications
Authors: Lee Seungjae , Kim Jaehyun , Yong Kijung
Published: March 2008

Atomic layer chemical vapor deposition (ALCVD) of titanium silicate nanofilms using a precursor combination of tetrakis-diethylamido-titanium (Ti(N(C2H5)2)4) and tetra-n-butyl-orthosilicate (Si(OnBu)4) was studied
for high dielectric gate oxides. ALCVD temperature window in our study was 170–210 °C with a growth rate of 0.8 Å/cycle. We investigated the effects of deposition conditions, such as deposition temperature, pulse time of precursor and purge injection, on the titanium silicate
nanofilm growth. The saturated composition of Ti/(Ti+Si) ratio was 0.6 and impurity concentrations were less than 1 atomic %. Dielectric constant (k) of the as-deposited titanium silicate film was ∼10.5. Hysteresis in capacitance–voltage (C–V) measurements was less than
0.35 V before and after annealing. The leakage current density of the as-deposited and 400 °C annealed film was 1.4 × 10−4 A/cm2, 4.2 × 10−4 A/cm2, respectively, at a bias of −1 V.
TiO2 Doped with Nitrogen: Synthesis and Characterization
Authors: Abazović Nadica D. , Montone Amelia , Mirenghi Luciana , Janković Ivana A. , Čomor Mirjana I.
Published: March 2008

In this study, nitrogen-doped titanium dioxide (TiO2) powders were synthesized in two ways: by heating of titanium hydroxide with urea and by direct hydrolysis of titanium tetraisopropoxide (TTIP) with ammonium hydroxide. The samples were characterized by structural (XRD),
analytical (XPS), optical (UV/Vis absorption/reflection and Raman spectroscopy) and morphological (SEM, TEM) techniques. The characterization suggested that the doped materials have anatase crystalline form without any detectable peaks that correspond to dopants. The absorption threshold of
titanium dioxide was moved in the visible range of optical spectrum from 3.2 eV to 2.20 eV. Particle sizes of synthesized powders were obtained from XRD measurements and from TEM data ranging from 6–20 nm. XPS and Raman spectroscopy were used for detection of nitrogen in doped samples.
Scanning Hall Probe Microscopy (SHPM) Using Quartz Crystal AFM Feedback
Authors: Dede M. , Ürkmen K. , Girişen Ö. , Atabak M. , Oral A. , Farrer I. , Ritchie D.
Published: March 2008

Scanning Hall Probe Microscopy (SHPM) is a quantitative and non-invasive technique for imaging localized surface magnetic field fluctuations such as ferromagnetic domains with high spatial and magnetic field resolution of ∼50 nm and 7 mG/Hz½ at room temperature.
In the SHPM technique, scanning tunneling microscope (STM) or atomic force microscope (AFM) feedback is used to keep the Hall sensor in close proximity of the sample surface. However, STM tracking SHPM requires conductive samples; therefore the insulating substrates have to be coated with
a thin layer of gold. This constraint can be eliminated with the AFM feedback using sophisticated Hall probes that are integrated with AFM cantilevers. However it is very difficult to micro fabricate these sensors. In this work, we have eliminated the difficulty in the cantilever-Hall probe
integration process, just by gluing a Hall Probe chip to a quartz crystal tuning fork force sensor. The Hall sensor chip is simply glued at the end of a 32.768 kHz or 100 kHz Quartz crystal, which is used as force sensor. An LT-SHPM system is used to scan the samples. The sensor assembly is
dithered at the resonance frequency using a digital Phase Locked Loop circuit and frequency shifts are used for AFM tracking. SHPM electronics is modified to detect AFM topography and the frequency shift, along with the magnetic field image. Magnetic domains and topography of an Iron Garnet
thin film crystal, NdFeB demagnetised magnet and hard disk samples are presented at room temperature. The performance is found to be comparable with the SHPM using STM feedback.
Microwave Properties of Polymer Composites Containing Combinations of Micro- and Nano-Sized Magnetic Fillers
Authors: Kolev Svetoslav , Koutzarova Tatyana , Yanev Andrey , Ghelev Chavdar , Nedkov Ivan
Published: March 2008

We investigated the microwave absorbing properties of composite bulk samples with nanostructured and micron-sized fillers. As magnetic fillers we used magnetite powder (Fe3O4 with low magnetocrystalline anisotropy) and strontium hexaferrite (SrFe12O19
with high magnetocrystalline anisotropy). The dielectric matrix consisted of silicone rubber. The average particle size was 30 nm for the magnetite powder and 6 μm for the strontium hexaferrite powder. The micron-sized SrFe12O19 powder was prepared using a solid-state
reaction. We investigated the influence of the filler concentration and the filler ratio (Fe3O4/SrFe12O19) in the polymer matrix on the microwave absorption in a large frequency range (1 ÷ 18 GHz). The results obtained showed that the highly
anisotropic particles become centers of clusterification and the small magnetite particles form magnetic balls with different diameter depending on the concentration. The effect of adding micron-sized SrFe12O19 to the nanosized Fe3O4 filler in composites
absorbing structures has to do with the ferromagnetic resonance (FMR) shifting to the higher frequencies due to the changes in the ferrite filler's properties induced by the presence of a magnetic material with high magnetocrystalline anisotropy. The two-component filler possesses new values
of the saturation magnetization and of the anisotropy constant, differing from those of both SrFe12O19 and Fe3O4, which leads to a rise in the effective anisotropy field. The results demonstrate the possibility to vary the composite's absorption
characteristics in a controlled manner by way of introducing a second magnetic material.
Synthesis and Characterization of Pure and Doped (Na, Ca, Sr) Nanograined LaMnO3 Magnetoresistive Ceramics
Authors: Maglia F. , Malavasi L. , Camurlu H. E. , Tacca A. , Chiodelli G. , Spinolo G. , Mozzati M. C. , Anselmi-Tamburini U. , Munir Z. A.
Published: March 2008

Pure and Na, Ca, and Sr doped lanthanum manganites (LaMnO3, La0.85Na0.15MnO3, La0.7Ca0.3MnO3, La0.7Sr0.3MnO3) were prepared in form of bulk nano-structured materials through
a two-step process. Nanometric powders were synthesized by the Pechini method and subsequently densified by Spark Plasma Sintering at 1273 K for 5 minutes under a pressure of 140 MPa. The method allowed the preparation of single phase manganites with a theoretical density above 98% together
with a very limited growth of the grain size. Grain sizes below 70 nm were obtained for all materials except La0.85Na0.15MnO3 which showed a much larger grain size. Curie temperatures (TC) and magnetoresistivity properties of the samples were
measured by a Superconducting Quantum Interference Device (SQUID). The decrease in the resistivity below TC occurred in a much wider range of temperature compared to ceramics having micron-size grains.
Preparation of Iron Oxide Nanoparticles by Microwave Synthesis and Their Characterization
Authors: Acarbas Ozge , Ozenbas Macit
Published: March 2008

In this study production of fine particle Fe2O3 via microwave processing of Fe(NO3)3·nH2O followed by low temperature annealing was reported. XRD was used to characterize the structural properties of nanoparticles.
Approximate particle sizes were between 3–13 nm according to Scherrer's equation. Single point BET measurement results also show that samples have large surface area and they are nanometer sized particles. TEM study was conducted to examine the structure of the nanoparticles. TEM figure
is in good agreement with the results obtained from Scherrer's equation using XRD spectra. In order to characterize the magnetic properties of the nanoparticles VSM (Vibrating Sample Magnetometer) was used. From these results it can be concluded that the sample containing only maghemite phase
exhibits superparamagnetic behaviour, on the other hand sample containing both hematite and maghemite phases shows paramagnetic behaviour above 300 K, superparamagnetic behavior at lower temperatures.
Nano-Structured Gold Catalysts Supported on CeO2 and CeO2-Al2O3 for NOx Reduction by CO: Effect of Catalyst Pretreatment and Feed Composition
Authors: Ilieva-Gencheva L. , Pantaleo G. , Mintcheva N. , Ivanov I. , Venezia A. M. , Andreeva D.
Published: March 2008

Gold catalysts supported on ceria and ceria-alumina were studied in NOx reduction by CO. Gold was loaded using deposition-precipitation method. The ceria-alumina (20 wt% alumina) support was synthesized by co-precipitation. The average size of gold and ceria nano-particles
was bellow 10 nm. It was established that the type of pretreatment do not have a substantial effect on the catalytic activity. The presence of O2 in the feed leads to a high conversion of CO to CO2 but no NO conversion was registered. Both NO and CO conversion was increased
adding H2 to the feed. The catalytic activity became higher upon adding higher amount of H2. Supplementary to the main reaction parallel reactions took place. Bellow 200 °C N2O formation and at 250 °C and above the NH3 formation was detected.
At around 200 °C it was established 100% selectivity to N2. The addition of water to the feed influenced positively the CO conversion and did not influence negatively the conversion of NO. The selectivity to N2 at around 200 °C remained 100% independent of the
presence of moisture. Alumina in the mixed support prevents the sintering of both gold and ceria nano-particles. The results obtained make the catalysts containing gold supported on ceria-alumina promising for practical application.
Photocatalytic Degradation of Methyl Orange: Influence of H2O2 in the TiO2-Based System
Authors: Andronic Luminita , Manolache Simona , Duta Anca
Published: March 2008

The purpose of this study was to investigate the photocatalytic oxidation of a reactive azo dye. The photocatalytic activity of the TiO2 was studied using a reactor equipped with UV-A sources, with maximum emission at 365 nm. The photocatalytic activity of the TiO2
powder (99.9% anatase) and thin films has been measured through the decomposition of methyl orange solutions. The thin film was prepared by doctor blade and spray pyrolysis deposition (SPD). The TiO2 suspensions were prepared at 1 g/L concentration, and the initial methyl orange
concentration was fixed at 7.8125 mg/L. The influence of the TiO2 (powder or thin films) and/or O2 and H2O2 on the photobleaching rate, was tested under different experiments, at pH = 5. Thin films (doctor blade) of TiO2 formed of mezo-sized
aggregates formed of nanosized anatase crystallites show better photobleaching efficiency than thin film(SPD) due to their large internal surface. The rate is even higher in H2O2 compared to oxygen environment.
Synthesis and Characterization of Nanosized Cu/ZnO Catalyst by Polyol Method
Authors: Altınçekiç Tuba Gürkaynak , Boz Ismail , Aktürk Selçuk
Published: March 2008

Nanosized catalysts composed of metallic copper supported on zinc oxide have been synthesized by the polyol process. Average crystallite size of copper was between 10 and 45 nm. Cu/ZnO catalyst particles were characterized by various techniques, such as X-ray Powder Diffraction (XRD),
Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Selected Area Electron Diffraction (SAED), and dynamic light scattering analysis (DLS).
Silica Surface Modification Reactions with Aluminum and Boron Alkyls and (Alkyl)Chlorides: Reactivities and Surface Nanostructures
Authors: Blitz Jonathan P. , Christensen Jeannine M. , Deakyne Carol A. , Gun'ko Vladimir M.
Published: March 2008

The functionalization of nanoporous and nanoparticulate silica surfaces requires a molecular level understanding of the chemistry and structures which result from surface reactions. Various types of reactive groups on silica can participate, giving rise to different nanostructures.
It is necessary to devise methods to alter the reactive nature of silica surfaces to control the nanoscale chemical structure. Various silica pretreatments are utilized to alter the silica surface prior to reaction with AlEt3, AlEtxCl3−x,
BEt3, BCl3, and TiCl4. Reactivities of these surface reactive reagents are compared. Aluminum compounds preferentially react with loss of alkane rather than HCl, in a thermodynamically controlled reaction as determined by ab initio computational methods.
Consideration of the structures resulting from reaction of the boron and aluminum compounds above with silica surface diols has been taken into account. Particular attention has been paid to the possibility of forming a cyclic 4-membered ring structure. While this is unlikely to form from
reactions with MCl3, such structures may be possible when reacting silicas with MMe3.
Hydrothermal Synthesis of Nanostructured TiO2 Particles and Characterization of Their Photocatalytic Antimicrobial Activity
Authors: Erdural Beril K. , Yurum Alp , Bakir Ufuk , Karakas Gurkan
Published: March 2008

Nanostructured titania particles were synthesized by using hydrothermal processing and the photocatalytic antimicrobial activities were characterized. Both sol–gel synthesized and commercial TiO2 (anatase) samples were processed with two step hydrothermal treatments,
under alkaline and neutral conditions. Scanning Electron Microscope (SEM) images showed that alkaline treatment yields nanofibers and lamellar structured particles from the commercial anatase and sol–gel synthesized samples respectively. Further treatment of nanofibers and nanostructured
lamellar particles with distilled water results with crystal growth and the formation of nano structured bipyramidal crystalline particles. The photocatalytic antimicrobial activities of the samples were determined against Escherichia coli under irradiation. It was observed that the
samples treated under alkaline conditions have improved activity than the original anatase samples. Limited activity and resulting time lag in bacterial inactivation were observed for hydrothermally treated samples with distilled water. However, a post treatment comprising the UV irradiation
in aqueous conditions enhanced the photocatalytic activity.
Slow Motion of Confined Molecules: NMR and Broadband Dielectric Spectroscopy Investigations
Authors: Erdem Özlen F. , Pampel André , Michel Dieter
Published: March 2008

Nuclear magnetic resonance (NMR) and broadband dielectric spectroscopy are used to investigate the dynamics of small glass-forming molecules confined to restricted geometries. Ethylene glycol molecules are embedded in the supercages of NaX zeolites. The combined application of NMR and
broadband dielectric spectroscopy advances the understanding of the slowing down of the motion near the glass transition temperature of these confined molecules. In combination with nuclear spin relaxation and nuclear magnetic resonance spectroscopy, dielectric relaxation studies on glass
forming molecules allow conclusions on the character of the motion. High resolution 1H magic angle spinning (MAS) NMR measurements not only enable a characterisation of the state of the adsorbed molecules via a chemical shift analysis. By means of an analysis of MAS spinning sidebands
we may also estimate a correlation time the meaning of which will be discussed in comparison to the results of longitudinal proton spin relaxation measurements. In addition to broadband dielectric spectroscopy slow molecular motions of partially deuterated ethylene glycol adsorbed in NaX are
studied by means of 2H NMR line-shape analysis.
Li+ and Li Interactions with Carbon Nanocage Structures
Authors: Peköz Rengin , Erkoç Şakir
Published: March 2008

Molecular dynamics simulations have been carried out to explore the structural properties of Li and Li+ confined inside single-walled carbon nanotubes (SWCNTs) and fullerene molecules. C–Li, C–Li+, Li–Li and Li+–Li+
interactions have been represented by pair functions and parameterized for the corresponding interactions. C–C interactions have been modeled by Tersoff potential. Open-ended SWCNTs with various sizes and chirality, as well as fullerenes with various sizes have been considered in the
simulations. C–Li interaction is stronger than that of C–Li+. Endohedral Li+ doping caused structural deformations in C60. It has been found that for both Li and Li+ cases endohedral doping is favorable with respect to exohedral doping.
This result is valid for both fullerenes and nanotubes.
A Study of Nanosize Fe3O4 Deposited on Carbon Matrix
Authors: Banov B. , Ljutzkanov L. , Dimitrov I. , Trifonova A. , Vasilchina H. , Aleksandrova A. , Mochilov A. , Hang Bui Thi , Okada Shigeto , Yamaki Jun-ichi
Published: March 2008

Nanosized crystalline Fe3O4 (with an average particle size of 16 nm) was successfully synthesized on a carbon matrix surface. The prepared sample was heat-treated in the temperature range 300 °C–750 °C to remove residual impurities and to obtain a
final product with a 77:23 ratio between magnetite and the carbon support. The sample was subjected to physicochemical and electrochemical tests. The purity of the phase and the particles size was determined by X-ray diffraction analysis and confirmed by field emission scanning electron micrographs.
The specific surface area of the sample measured by the B.E.T method was 120 m2 g−1. A series of electrochemical tests including EIS, CV and long-term constant current cycling have been performed. The obtained reversible capacity within 15 cycles was in the range
400–550 mA h · g−1. The electrochemical behavior of the test sample and its possible practical use as an anode material in lithium secondary batteries are discussed.
Bioactivity of Polycrystalline Silicon Layers
Authors: Pramatarova Lilyana , Pecheva Emilia , Montgomery Paul , Dimova-Malinovska Doriana , Petrov Todor , Toth Attila L. , Dimitrova Magdalena
Published: March 2008

After oxygen, silicon is the second most abundant element in the environment and is present as an impurity in most materials. The widespread occurrence of siliceous biominerals as structural elements in lower plants and animals suggests that Si plays a role in the production and maintenance
of connective tissue in higher organisms. It has been shown that the presence of Si is necessary in bones, cartilage and in the formation of connective tissue, as well as in some important metabolic processes. In this work, polycrystalline silicon layers are tested in terms of bioactivity,
i.e., their ability to induce hydroxyapatite formation from simulated body fluid. Hydroxyapatite is a biologically compatible material with chemical similarity to the inorganic part of bones and teeth. Polycrystalline silicon layers are obtained by aluminum induced crystallization of Al and
amorphous Si thin films deposited sequentially on glass substrates by radio-frequency magnetron sputtering and subsequently annealed in different atmospheres. The hydroxyapatite formation is induced by applying a method of laser-liquid-solid interaction. The method consists of irradiating
the samples with laser light while immersed in a solution that is supersaturated with respect to Ca and P. As a result, heterogeneous porous sponge-like carbonate-containing hydroxyapatite is grown on the polysilicon surfaces. Crystals that are spherical in shape, containing Ca, P and O, Na,
Cl, Mg, Al, Si and S, as well as well-faceted NaCl crystals are embedded in the hydroxyapatite layer. Enhancement of the hydroxyapatite growth and increased crystallinity is observed due to the applied laser-liquid-solid interaction.
Influence of Bilayer Period on the Characteristics of Nanometre-Scale ZrN/TiAlN Multilayers
Authors: Vladescu A. , Kiss A. , Popescu A. , Braic M. , Balaceanu M. , Braic V. , Tudor I. , Logofatu C. , Negrila C. C. , Rapeanu R.
Published: March 2008

In the last decade, considerable research effort was directed to the deposition of multilayer films with layer thicknesses in the nanometer range (superlattice coatings), in order to increase the performance of various cutting tools and machine parts. The goal of the present work was
to investigate the main microstructural, mechanical and wear resistance characteristics of a superlattice coating, consisting of alternate multilayer ZrN/TiAlN films, with various bilayer periods (5÷20 nm). The coatings were deposited by the cathodic arc method on Si, plain carbon steel
and high speed steel substrates to be used as wear resistance surfaces. The multilayer structures were prepared by using shutters placed in front of each cathode (Zr and Ti + Al). The characteristics of multilayer structures (elemental and phase composition, texture, Vickers microhardness,
thickness, adhesion, and wear resistance) were determined by using various techniques (AES, XPS, XRD, microhardness measurements, scratch, and tribological tests). A comparison with the properties of ZrN and TiAlN single-layer coatings was carried out.
Anomalous Surface Diffusion of C60 and Anisotropic Growth of Nano Islands on Ni(111)
Authors: Lin C. H. , Lin K. C. , Tang Tong B. , Pai Woei Wu
Published: March 2008

The migration behavior of C60 on Ni(111) has been inferred from its growth morphology at various substrate temperatures, as observed with scanning tunneling microscopy. The number density of islands increased and their average sizes decreased anomalously in the temperature
range of ∼573 K to ∼973 K. This trend contradicts the prediction in conventional nucleation theory. At low and high temperatures, C60 commence nucleation on both sides of surface steps in a "bi-directional step flow" mode. However, anisotropy occurs within an intermediate
temperature range, in which C60 nucleate predominantly at upper step edges. Surprisingly, in-situ growth measurements at this intermediate temperature range revealed that C60 actually start nucleating from lower step edges, with concomitant formation of Ni terraces
underneath. These anomalous thermal dependence of diffusivity and the peculiar growth morphology of C60 on Ni(111) are attributed to C60-induced reconstruction of Ni(111) at higher temperature.
Preparation of Nanocomposite Fluid by Electrical Arc Discharge Technique
Authors: Chang Ho
Published: March 2008

This study proposes a new method, the electrical arc discharge system, for preparing a nanocomposite fluid with the basic ingredients of Cu, Fe and O. The proposed system has the advantages of a high-power electric arc heating system, excellent stability of the electric arc, and well-developed
control technology. In the fabrication process, the positive electrode uses a copper rod and the negative electrode an iron rod, and the two electrodes are processed in the insulating processing liquid. The nanocomposite fluid generated by the synthesis system is analyzed by morphological
analysis, heat transfer analysis, magnetism analysis, and rheological testing. The experiment generates satisfactory results for nanocomposite fluid with an average particle diameter of 25 nm by process conditions of vacuum pressure of 30 torr, peak current of 7 A for arc discharge, breakdown
voltage of 250 V and duration time of 10 μs. The experimental results showed that the nanocomposite fluid is composed of Fe, FeO, Cu, and Cu2O. SEM images show that Fe and Cu are spherical, FeO is square, and Cu2O is ovoid. For the heat transfer experiment,
the experimental temperature was set at ambient temperature of 30 °C, the average heat transfer coefficient is 0.708 W/m °C, which is 16.3% higher than that of deionized water. The magnetism test shows that the magnetic retentivity of the fluid is 47.27 (Oe), which makes it a soft
magnetic fluid.
Nanostructured Hydroxyapatite by Microwave Sintering
Authors: Seo Dong Seok , Hwang Kyu Hong , Lee Jong Kook
Published: March 2008

In this work, nanostructured HA ceramics with dense microstructure were prepared by microwave sintering process and their microstructures were compared with the case of conventional sintering. Commercially obtained HA powder with Ca/P molar ratio of 1.67 was used as a starting material.
The powder of granular type consists of nanocrystalline particles of 20–30 nm in size. The as-received HA powder or the powder calcined at 800 °C, followed by ball-milling was used for the preparation of HA disks. Microwave sintering was conducted at 1200 °C for 5 min with a
heating rate of 50 °C/min. HA ceramics with the sintered densities of approximately 96–97% of the theoretical were obtained. XRD analysis showed that all detectable peaks are identical to pure hydroxyapatite. The HA sintered body made of calcined and ball-milled powder showed uniform
microstructure with grain size of 300–400 nm and with finer sub-grains of 30–40 nm.
Submerged Arc Spray Synthesis of TiO2 Nanoparticles with Desired Form Sphericity Using Process Characterization and Optimization
Authors: Chen Liang-Chia , Ho Chie-Chan
Published: March 2008

This article presents a study on process characterization and optimization of the metal nanoparticle fabrication process known as the submerged arc spray nanoparticle synthesis system (SANSS) for obtaining desired geometric sphericity of nanoparticles. The geometric shape characteristics
of nanoparticles pose significant impact on innovative product and process design. The sphericity and surface roughness of prepared TiO2 nanoparticles can vary widely and are influenced by the process parameters being employed in the SANSS. To improve this, an in-situ nanofluid
sampling and measurement approach was developed to analyze the particle shape characteristics and characterize the nanoparticle synthesis process. The particle shape contours obtained from FE-SEM and TEM were employed to quantify the TiO2 nanoparticle sphericity and analyze the
effect of process parameters on particle roundness. The optimized process parameters were identified using the Taguchi method. Our results proved that the average sphericity of TiO2 particles prepared using the optimized process parameters was effectively improved up to three folds.
Parent—Child Conflicts, School Troubles, and Differences in Delinquency Across Immigration Generations
Authors: Bui Hoan N.
Published: March 2008

This study examines delinquent behavior among schoolchildren in a nationally representative sample from the United States and seeks an understanding of the factors contributing to variances in delinquency across immigration generations. Data analysis indicates that the levels of self-reported substance use, property delinquency, and violent delinquency among first-generation students are significantly lower than those among students from later immigration generations. These differences are explained in part by family relationships and school bonding, particularly parent—child conflicts and school troubles that increase with later immigration generations. These findings suggest that there are negative effects of acculturation on family and school processes, which in turn affect delinquency.
Conviction Offense and Prison Violence
Authors: Sorensen Jon , Cunningham Mark D.
Published: March 2008

The characteristics of, and 2003 disciplinary data on, 51,527 inmates in the Florida Department of Corrections, including 9,586 inmates who had been convicted of some degree of homicide, were examined for rates and correlates of prison misconduct and violence. Disciplinary misconduct and institutional acts of violence committed by an admissions cohort ( N = 14,088) and a subset of Close custody inmates ( N = 4,113) also were considered. Regardless of conviction offense, the prevalence and rate of violent prison misconduct fell markedly as the severity of assault increased. Comparative data showed that convicted murderers did not account for a disproportionate share of prison violence, however defined. Furthermore, negative binomial regression models revealed that convicted murderers were not significantly more likely to engage in disciplinary misconduct or commit acts of institutional violence than were inmates serving time for other offenses.