Carbon Nanotube/Silica Coaxial Nanocable as a Three-Dimensional Support for Loading Diverse Ultra-High-Density Metal Nanostructures: Facile Preparation and Use as Enhanced Materials for Electrochemical Devices and SERS

In this paper, we have reported a very simple strategy (combined sonication with sol-gel techniques) for synthesizing well-defined silica-coated carbon nanotube (CNT) coaxial nanocable without prior CNT functionalization. After functionalization with NH2 group, the CNT/silica coaxial nanocable has been employed as a three-dimensional support for loading ultra-high-density metal or hybrid nanoparticles (NPs) such as gold NPs, Au/Pt hybrid NPs, Pt hollow NPs, and Au/Ag core/shell NPs. Most importantly, it is found that the ultra-high-density Au/Pt NPs supported on coaxial nanocables (UASCN) could be used as enhanced materials for constructing electrochemical devices with high performance. Four model probe molecules (O-2, CH3OH, H2O2, and NH2NH2) have been investigated on UASCN-modified glassy carbon electrode (GCE). It was observed that the present UASCN exhibited high electrocatalytic activity toward diverse molecules and was a promising electrocatalyst for constructing electrochemical devices with high performance. For instance, the detection limit for H2O2 with a signal-to-noise ratio of 3 was found to be 0.3 mu M, which was lower than certain enzyme-based biosensors.

A general route to prepare one- and three-dimensional carbon nanotube/metal nanoparticle composite nanostructures

Adsorption of polyethyleneimine (PEI)-metal ion complexes onto the surfaces of carbon nanotubes (CNTs) and subsequent reduction of the metal ion leads to the fabrication of one-dimensional CNT/metal nanoparticle (CNT/M NP) heterogeneous nanostructures. Alternating adsorption of PEI-metal ion complexes and CNTs on substrates results in the formation of multilayered CNT films. After exposing the films to NaBH4, three-dimensional CNT composite films embedded with metal nanoparticles (NPs) are obtained. UV-visible spectroscopy, scanning electron microscopy, and X-ray photoelectron spectroscopy are used to characterize the film assembly. The resulting (CNT/M NP)(n) films inherit the properties from both the metal NPs and CNTs that exhibit unique performance in surface-enhanced Raman scattering (SERS) and electrocatalytic activities to the reduction of O-2; as a result, they are more attractive compared to (CNT/polyelectrolyte)(n) and (NP/polyelectrolyte)(n) films because of their multifunctionality.

Self-assembly of ionic liquids-stabilized Pt nanoparticles into two-dimensional patterned nanostructures at the air-water interface

In this paper, we demonstrate the self-assembly of ionic liquids (ILs)-stabilized Pt nanoparticles into two-dimensional (2D) patterned nanostructures at the air-water interface under ambient conditions. Here, ILs are not used as solvents but as mediators by virtue of their pronounced self-organization ability in synthesis of self-assembled, highly organized hybrid Pt nanostructures. It is also found that the morphologies of the 2D patterned nanostructures are directly connected with the quantities of ILs. Due to the special structures of ILs-stabilized Pt nanoparticles, 2D patterned Pt nanostructures can be formed through the pi-pi stack interactions and hydrogen bonds. The resulting 2D patterned Pt nanostructures exhibit good electrocatalytic activity toward oxygen reduction.

Alternate assemblies of polyelectrolyte functionalized carbon nanotubes and platinum nanoparticles as tunable electrocatalysts for dioxygen reduction

A novel method based on electrostatic layer-by-layer self-assembly (LBL) technique for alternate assemblies of polyelectrolyte functionalized multi-walled carbon nanotubes (MWNTs) and platinum nanoparticles (PtNPs) is proposed. The shortened MWNTs can be functionalized with positively charged poly(diallyldimethylammonium chloride) (PDDA) based on electrostatic interaction. Through electrostatic layer-by-layer assembly, the positively charged PDDA functionalized MWNTs (PDWNTs) and negatively charged citrate-stabilized PtNPs were alternately assembled on a 3-mercaptopropanesulfonic sodium (NIPS) modified gold electrode and also on other negatively charged surface, e.g. quartz slide and indium-tin-oxide (ITO) plate, directly forming the three-dimensional (3D) nanostructured materials. This is a very general and powerful technique for the assembling three-dimensional nanostructured materials containing carbon nanotubes (CNTs) and nanoparticles. Thus prepared multilayer films were characterized by ultraviolet-visiblenear-infrared spectroscopy (UV-vis-NIR), scanning electron microscopy (SEM) and cyclic voltammetry (CV). Regular growth of the mutilayer films is monitored by UV-vis-NIR.

Two- and three-dimensional Au nanoparticle/CoTMPyP self-assembled nanotructured materials: Film structure, tunable electrocatalytic activity, and plasmonic properties

Two- and three-dimensional Au nanoparticle/[tetrakis(N-methylpyridyl)porphyrinato]cobalt (CoTMPyP) nanostructured materials were prepared by "bottom-up" self-assembly. The electrocatalytic and plasmonic properties of the Au nanoparticle/CoTMPyP self-assembled nanostructured materials (abbreviated as Au/CoTMPyP SANMs) are tunable by controlled self-assembly of the An nanoparticles and CoTMPyP on indium tin oxide (ITO) electrode. The electrocatalytic activity of the Au/CoTMPyP SANMs can be tuned in two ways. One way is that citrate-stabilized An nanoparticles are positioned first on ITO surface with tunable number density, and then positively charged CoTMPyP ions are planted selectively on these gold sites. The other way is that An nanoparticles and CoTMPyP are deposited by virtue of layer-by-layer assembly, which can also tune the amount of the as-deposited electrocatalysts. FE-SEM studies showed that three-dimensional SANMs grow in the lateral expansion mode, and thermal annealing resulted in both surface diffusion of nanoparticles and atomic rearrangement to generate larger gold nanostructures with predominant (I 11) facets.

Hydrothermal synthesis and crystal structure of a sulfur-bridged binuclear copper(II) coordination polymer generated by a spontaneous reduction

A simultaneous reduction SO42- to S2- by 2,5-pyridinedicarboxylate under hydrothermal conditions produced a new binuclear copper(II) coordination polymer [CuS(4,4'-bipy)](n) (4,4-bipy = 4,4'-bipyridine) (1). Single crystal X-ray analysis revealed that compound I consisted of sulfur-bridged binuclear copper(II) units with Cu-Cu bonding which were combined with 4,4-bipy to generate a three-dimensional network constructed from mutual interpenetration of two-dimensional (6,3) nets. Crystal data for 1:C10H8CuN2S, tetragonal 14(1)/acd, a = 14.0686(5) Angstrom, b = 14.0686(5) Angstrom, c = 38.759(2) Angstrom, Z = 32. Other characterizations by elemental analysis, IR, EPR and TGA analysis were also described in this paper.

Assemble of poly(aniline-co-o-aminobenzenesulfonic acid) three-dimensional tubal net-works onto ITO electrode and its application for the direct electrochemistry and electrocatalytic behavior of cytochrome c

Stable electroactive film of poly(aniline-co-o-aminobenzenesulfonic acid) three-dimensional tubal net-works was assembled on indium oxide glass (ITO) successfully, and the cytochrome c was immobilized on the matrix by the electrostatic interactions. The adsorbed cytochrome c showed a good electrochemical activity with a pair of well-defined redox waves in pH 6.2 phosphate buffer solution, and the adsorbed protein showed more faster electron transfer rate (12.9 s(-1)) on the net-works matrix than those of on inorganic porous or even nano-materials reported recently. The immobilized cytochrome c exhibited a good electrocatalytic activity and amperometric response (2 s) for the reduction of hydrogen peroxide (H2O2). The detection limit for H2O2 was 1.5 mu M, and the linear range was from 3 mu M to 1 mM. Poly(aniline-co-o-aminobenzenesulfonic acid) three-dimensional tubal net-works was proved to be a good matrix for protein immobilization and biosensor preparation.

Preparation of multilayer films containing pt nanoparticles on a glassy carbon electrode and application as an electrocatalyst for dioxygen reduction

In this paper, a simple route for the preparation of Pt nanoparticles is described. PtCl62- and [tetrakis-(N-methylpyridyl)porphyrinato] cobalt (CoTMPyP) were assembled on a 4-aminobenzoic acid modified glassy carbon electrode through the layer-by-layer method. The three-dimensional Pt nanoparticle films are directly formed on an electrode surface by electrochemical reduction of PtCl62- sandwiched between CoTMPyP layers. Regular growth of the multilayer films is monitored by UV-vis spectroscopy. X-ray photoelectron spectroscopy verifies the constant composition of the multilayer films containing Pt nanoparticles. Atomic force microscopy proves that the as-prepared Pt nanoparticles are uniformily distributed with average particle diameters of 6-10 nm. The resulting multilayer films containing Pt nanoparticles on the modified electrode possess catalytic activity for the reduction of dissolved oxygen. Rotating disk electrode voltammetry and rotating ring-disk electrode voltammetry confirm that Pt nanoparticle containing films can catalyze an almost four-electron reduction of O-2 to water in 0.5 M H2SO4 solution.

Simple preparation method of Pd nanoparticles on an Au electrode and its catalysis for dioxygen reduction

A simple method for the fabrication of Pd nanoparticles is described. The three-dimensional Pd nanoparticle films are directly formed on a gold electrode surface by simple electrodeposition at -200 mV from a solution of 1 M H2SO4+0.01 mM K2PdCl4. X-Ray photoelectron spectroscopy verifies the constant composition of the Pd nanoparticle films. Atomic force microscopy proves that the as-prepared Pd nanoparticles are uniformly distributed with an average particle diameter of 45-60 nm. It is confirmed that the morphology of the Pd nanoparticle films are correlated with the electrodeposition time and the state of the Au substrate. The resulting Pd-nanoparticle-film-modified electrode possesses high catalytic activity for the reduction of dissolved oxygen in 0.1 M KCl solution. Freshly prepared Pd nanoparticles can catalyze the reduction of O-2 by a 4-electron process at -200 mV in 0.1 M KCl, but this system is not very stable. The cathodic peaks corresponding to the reduction of O-2 gradually decrease with potential cycling and at last reach a steady state. Then two well-defined reduction peaks are observed at -390 and -600 mV vs. Ag/AgCl/KCl (sat.). Those two peaks correspond to a 2-step process for the 4-electron reduction pathway of O-2 in this neutral medium.

A method to construct a third-generation horseradish peroxidase biosensor: Self-assembling gold nanoparticles to three-dimensional sol-gel network

A novel method for fabrication of horseradish peroxidase biosensor has been developed by self-assembling gold nanoparticles to a thiol-containing sol-gel network. A cleaned gold electrode was first immersed in a hydrolyzed (3-mercaptopropyl)-trimethoxysilane (MPS) sol-gel solution to assemble three-dimensional silica gel, and then gold nanoparticles were chemisorbed onto the thiol groups of the sol-gel network. Finally, horseradish peroxidase (HRP) was adsorbed onto the surface of the gold nanoparticles. The distribution of gold nanoparticles and HRP was examined by atomic force microscopy (AFM). The immobilized horseradish peroxidase exhibited direct electrochemical behavior toward the reduction of hydrogen peroxide. The performance and factors influencing the performance of the resulting biosensor were studied in detail. The resulting biosensor exhibited fast amperometric response (2.5 s) to H2O2. The detection limit of the biosensor was 2.0 mumol L-1, and the linear range was from 5.0 mumol L-1 to 10.0 mmol L-1. Moreover, the studied biosensor exhibited high sensitivity, good reproducibility, and long-term stability.

Restructuring Sparse High Dimensional Data for Effective Retrieval

The task in text retrieval is to find the subset of a collection of documents relevant to a user's information request, usually expressed as a set of words. Classically, documents and queries are represented as vectors of word counts. In its simplest form, relevance is defined to be the dot product between a document and a query vector--a measure of the number of common terms. A central difficulty in text retrieval is that the presence or absence of a word is not sufficient to determine relevance to a query. Linear dimensionality reduction has been proposed as a technique for extracting underlying structure from the document collection. In some domains (such as vision) dimensionality reduction reduces computational complexity. In text retrieval it is more often used to improve retrieval performance. We propose an alternative and novel technique that produces sparse representations constructed from sets of highly-related words. Documents and queries are represented by their distance to these sets. and relevance is measured by the number of common clusters. This technique significantly improves retrieval performance, is efficient to compute and shares properties with the optimal linear projection operator and the independent components of documents.

Fuzzy-Rough Attribute Reduction with Application to Web Categorization.

R. Jensen and Q. Shen, 'Fuzzy-Rough Attribute Reduction with Application to Web Categorization,' Fuzzy Sets and Systems, vol. 141, no. 3, pp. 469-485, 2004.

Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Simultaneous Learning of Non-Linear Manifold and Dynamical Models for High-Dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. The model structure setup and parameter learning are done using a variational Bayesian approach, which enables automatic Bayesian model structure selection, hence solving the problem of over-fitting. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.

Cyclic Pursuit: Symmetry, Reduction and Nonlinear Dynamics

In this dissertation, we explore the use of pursuit interactions as a building block for collective behavior, primarily in the context of constant bearing (CB) cyclic pursuit. Pursuit phenomena are observed throughout the natural environment and also play an important role in technological contexts, such as missile-aircraft encounters and interactions between unmanned vehicles. While pursuit is typically regarded as adversarial, we demonstrate that pursuit interactions within a cyclic pursuit framework give rise to seemingly coordinated group maneuvers. We model a system of agents (e.g. birds, vehicles) as particles tracing out curves in the plane, and illustrate reduction to the shape space of relative positions and velocities. Introducing the CB pursuit strategy and associated pursuit law, we consider the case for which agent i pursues agent i+1 (modulo n) with the CB pursuit law. After deriving closed-loop cyclic pursuit dynamics, we demonstrate asymptotic convergence to an invariant submanifold (corresponding to each agent attaining the CB pursuit strategy), and proceed by analysis of the reduced dynamics restricted to the submanifold. For the general setting, we derive existence conditions for relative equilibria (circling and rectilinear) as well as for system trajectories which preserve the shape of the collective (up to similarity), which we refer to as pure shape equilibria. For two illustrative low-dimensional cases, we provide a more comprehensive analysis, deriving explicit trajectory solutions for the two-particle "mutual pursuit" case, and detailing the stability properties of three-particle relative equilibria and pure shape equilibria. For the three-particle case, we show that a particular choice of CB pursuit parameters gives rise to remarkable almost-periodic trajectories in the physical space. We also extend our study to consider CB pursuit in three dimensions, deriving a feedback law for executing the CB pursuit strategy, and providing a detailed analysis of the two-particle mutual pursuit case. We complete the work by considering evasive strategies to counter the motion camouflage (MC) pursuit law. After demonstrating that a stochastically steering evader is unable to thwart the MC pursuit strategy, we propose a (deterministic) feedback law for the evader and demonstrate the existence of circling equilibria for the closed-loop pursuer-evader dynamics.