Photoreactive azido-containing silica nanoparticle/polycation multilayers : durable superhydrophobic coating on cotton fabrics

In this study, we report the functionalization of silica nanoparticles with highly photoreactive phenyl azido groups and their utility as a negatively charged building block for layer-by-layer (LbL) electrostatic assembly to produce a stable silica nanoparticle coating. Azido-terminated silica nanoparticles were prepared by the functionalization of bare silica nanoparticles with 3-aminopropyltrimethoxysilane followed by the reaction with 4-azidobenzoic acid. The azido functionalization was confirmed by FTIR and XPS. Poly(allylamine hydrochloride) was also grafted with phenyl azido groups and used as photoreactive polycations for LbL assembly. For the photoreactive silica nanoparticle/polycation multilayers, UV irradiation can induce the covalent cross-linking within the multilayers as well as the anchoring of the multilayer film onto the organic substrate, through azido photochemical reactions including C–H insertion/abstraction reactions with surrounding molecules and dimerization of azido groups. Our results show that the stability of the silica nanoparticle/polycation multilayer film was greatly improved after UV irradiation. Combined with a fluoroalkylsilane post-treatment, the photoreactive LbL multilayers were used as a coating for superhydrophobic modification of cotton fabrics. Herein the LbL assembly method enables us to tailor the number of the coated silica nanoparticles through the assembly cycles. The superhydrophobicity of cotton fabrics was durable against acids, bases, and organic solvents, as well as repeated machine wash. Because of the unique azido photochemistry, the approach used here to anchor silica nanoparticles is applicable to almost any organic substrate.

Wireless instantaneous neurotransmitter concentration system-based amperometric detection of dopamine, adenosine, and glutamate for intraoperative neurochemical monitoring - laboratory investigation

Object  In a companion study, the authors describe the development of a new instrument named the Wireless Instantaneous Neurotransmitter Concentration System (WINCS), which couples digital telemetry with fast-scan cyclic voltammetry (FSCV) to measure extracellular concentrations of dopamine. In the present study, the authors describe the extended capability of the WINCS to use fixed potential amperometry (FPA) to measure extracellular concentrations of dopamine, as well as glutamate and adenosine. Compared with other electrochemical techniques such as FSCV or high-speed chronoamperometry, FPA offers superior temporal resolution and, in combination with enzyme-linked biosensors, the potential to monitor nonelectroactive analytes in real time.

Methods  The WINCS design incorporated a transimpedance amplifier with associated analog circuitry for FPA; a microprocessor; a Bluetooth transceiver; and a single, battery-powered, multilayer, printed circuit board. The WINCS was tested with 3 distinct recording electrodes: 1) a carbon-fiber microelectrode (CFM) to measure dopamine; 2) a glutamate oxidase enzyme–linked electrode to measure glutamate; and 3) a multiple enzyme–linked electrode (adenosine deaminase, nucleoside phosphorylase, and xanthine oxidase) to measure adenosine. Proof-of-principle analyses included noise assessments and in vitro and in vivo measurements that were compared with similar analyses by using a commercial hardwired electrochemical system (EA161 Picostat, eDAQ; Pty Ltd). In urethane-anesthetized rats, dopamine release was monitored in the striatum following deep brain stimulation (DBS) of ascending dopaminergic fibers in the medial forebrain bundle (MFB). In separate rat experiments, DBS-evoked adenosine release was monitored in the ventrolateral thalamus. To test the WINCS in an operating room setting resembling human neurosurgery, cortical glutamate release in response to motor cortex stimulation (MCS) was monitored using a large-mammal animal model, the pig.

Results   The WINCS, which is designed in compliance with FDA-recognized consensus standards for medical electrical device safety, successfully measured dopamine, glutamate, and adenosine, both in vitro and in vivo. The WINCS detected striatal dopamine release at the implanted CFM during DBS of the MFB. The DBS-evoked adenosine release in the rat thalamus and MCS-evoked glutamate release in the pig cortex were also successfully measured. Overall, in vitro and in vivo testing demonstrated signals comparable to a commercial hardwired electrochemical system for FPA.

Conclusions  By incorporating FPA, the chemical repertoire of WINCS-measurable neurotransmitters is expanded to include glutamate and other nonelectroactive species for which the evolving field of enzyme-linked biosensors exists. Because many neurotransmitters are not electrochemically active, FPA in combination with enzyme-linked microelectrodes represents a powerful intraoperative tool for rapid and selective neurochemical sampling in important anatomical targets during functional neurosurgery.

Sensitivity analysis of a sub-wavelength localized surface plasmon resonance graphene biosensor

Localized surface plasmon resonance (LSPR) is a promising detection method for label-free sensing of biomolecules. In this paper, a multilayer design for a LSPR biosensor is presented. In the proposed design, a periodic array of dielectric grating is incorporated on top of a graphene layer in the biosensor. The aim is to improve sensitivity of the LSPR biosensor through monitoring biomolecular interactions of biotin-streptavidin. Sensitivity improvement is obtained for the proposed LSPR biosensor compared with conventional SPR counterparts. In addition, to optimize the design, we have investigated grating geometry including volume factor and grating depth. The outcome of this investigation identifies ideal functioning conditions corresponding to the best design parameters.

Prediction of Drug Dissolution Profiles Using Artificial Neural Networks

This paper investigates the efficacy and reliability of Artificial Neural Networks (ANNs) as an intelligent decision support tool for pharmaceutical product formulation. Two case studies have been employed to evaluate capabilities of the Multilayer Perceptron network in predicting drug dissolution/release profiles. Performances of the network were evaluated using similarity factor (&fnof[sub 2]) — an index recommended by the United States Food and Drug Administration for profile comparison in pharmaceutical research. In addition, the bootstrap method was applied to assess the network prediction reliability by estimating confidence intervals associated with the results. The Multilayer Perceptron network also demonstrated a superior performance in comparison with multiple regression models. The results reveal that the ANN system has potentials to be a decision support tool for profile prediction in pharmaceutical experimentation, and the bootstrap method could be used as a means to assess reliability of the network prediction. [ABSTRACT FROM AUTHOR].

Development of an intelligent system for the solder paste printing process

A neurogenetic-based hybrid framework is developed where the main components within the framework are artificial neural networks (ANNs) and genetic algorithms (GAs). The investigation covers a mode of combination or hybridisation between the two components that is called task hybridisation. The combination between ANNs and GAs using task hybridisation leads to the development of a hybrid multilayer feedforward network, trained using supervised learning. This paper discusses the GA method used to optimize the process parameters, using the ANN developed as the process mode, in a solder paste printing process, which is part of the process in the surface mount technology (SMT) method. The results obtained showed that the GA-based optimization method works well under various optimization criteria

Use of circle-segments as a data visualization technique for feature selection in pattern classification

One of the issues associated with pattern classification using data based machine learning systems is the “curse of dimensionality”. In this paper, the circle-segments method is proposed as a feature selection method to identify important input features before the entire data set is provided for learning with machine learning systems. Specifically, four machine learning systems are deployed for classification, viz. Multilayer Perceptron (MLP), Support Vector Machine (SVM), Fuzzy ARTMAP (FAM), and k-Nearest Neighbour (kNN). The integration between the circle-segments method and the machine learning systems has been applied to two case studies comprising one benchmark and one real data sets. Overall, the results after feature selection using the circle segments method demonstrate improvements in performance even with more than 50% of the input features eliminated from the original data sets.

Variable incidence angle subwavelength grating SPR graphene biosensor

This paper investigates the sensitivity enhancement of a variable incidence angle subwavelength grating based multilayer surface plasmon resonance biosensor (SPRB). In the proposed design, a periodic array of subwavelength grating is integrated on top of a layer of graphene sheet in the multilayer SPR biosensor. The performance of the biosensor is investigated through monitoring the biomolecular interactions of cDNA-ssDNA interactions on its surface. The sensitivity improvement is indicated by the shift of the resonance peak angle.

Effects of mechanical deformation on electric performance of rechargeable batteries embedded in load carrying composite structures

Integrating rechargeable battery cells with fibre reinforced polymer matrix composites is a promising technology to enable composite structures to concurrently carry load and store electric energy, thus significantly reducing weight at the system level. To develop a design criterion for structural battery composites, rechargeable lithium polymer battery cells were embedded into carbon fibre/epoxy matrix composite laminates, which were then subjected to tensile, flexural and compressive loading. The electric charging/discharging properties were measured at varying levels of applied loads. The results showed that degradation in battery performance, such as voltagea and energy storage capacity, correlated well with the applied strain under three different loading conditions. Under compressive loading, battery cells, due to their multilayer construction, were unable to prevent buckling of composite face sheets due to the low lateral stiffness, leading to lower compressive strength that sandwich panels with foam core.

Mechanical Behavior of Nano-crystalline Metallic Thin Films and Multilayers Under Microcompression

Microcompression tests were performed to determine the mechanical behavior of nano-crystalline Cu/Fe and Fe/Cu multilayers, as well as monolithic Cu and Fe thin films. The results show that the micropillars of pure Cu thin film bulge out under large compressive strains without failure, while those of pure Fe thin film crack near the top at low compressive strains followed by shear failure. For Cu/Fe and Fe/Cu multilayers, the Cu layers accommodate the majority of plastic deformation, and the geometry constraints imposed by Fe layers exaggerates the bulging in the Cu layers. However, the existence of ductile Cu layers does not improve the overall ductility of Cu/Fe and Fe/Cu multilayers. Cracking in the Fe layers directly lead to the failure of the multilayer micropillars, although the Cu layers have very good ductility. The results imply that suppressing the cracking of brittle layers is more important than simply adding ductile layers for improving the overall ductility of metallic multilayers.

An accelerated particle swarm optimization based levenberg marquardt back propagation algorithm

The Levenberg Marquardt (LM) algorithm is one of the most effective algorithms in speeding up the convergence rate of the Artificial Neural Networks (ANN) with Multilayer Perceptron (MLP) architectures. However, the LM algorithm suffers the problem of local minimum entrapment. Therefore, we introduce several improvements to the Levenberg Marquardt algorithm by training the ANNs with meta-heuristic nature inspired algorithm. This paper proposes a hybrid technique Accelerated Particle Swarm Optimization using Levenberg Marquardt (APSO_LM) to achieve faster convergence rate and to avoid local minima problem. These techniques are chosen since they provide faster training for solving pattern recognition problems using the numerical optimization technique.The performances of the proposed algorithm is evaluated using some bench mark of classification’s datasets. The results are compared with Artificial Bee Colony (ABC) Algorithm using Back Propagation Neural Network (BPNN) algorithm and other hybrid variants. Based on the experimental result, the proposed algorithms APSO_LM successfully demonstrated better performance as compared to other existing algorithms in terms of convergence speed and Mean Squared Error (MSE) by introducing the error and accuracy in network convergence.

Efficient design of polarization insensitive polymer optical waveguide devices considering stress-induced effects

We present an approach for the efficient design of polarization insensitive polymeric optical waveguide devices considering stress-induced effects. In this approach, the stresses induced in the waveguide during the fabrication process are estimated first using a more realistic model in the finite element analysis. Then we determine the perturbations in the material refractive indices caused by the stress-optic effect. It is observed that the stresses cause non-uniform optical anisotropy in the waveguide materials, which is then incorporated in the modal analysis considering a multilayer structure of waveguide. The approach is exploited in the design of a Bragg grating on strip waveguide. Excellent agreement between calculated and published experimental results confirms the feasibility of our approach in the accurate design of polarization insensitive polymer waveguide devices.

Experimental studies and modelling of the deformation behaviour of multiple nanolayers

Microcompression tests were performed on monolithic Cu and Fe thin films and a Cu/Fe multilayer that had each individual layer of 200 nm thick, to understand the mechanical behaviour of multiple nanolayers. The micron-sized pillars were prepared by focused-ion beam (FIB) technique and compressed with a flat punch in a nanoindenter. The flow curves of the monolithic Cu and Fe thin films and Cu/Fe multilayer were extracted from the microcompression tests. The monolithic Cu thin film bulges in the top region of the pillar, while the Fe thin film cracks due to its columnar grain structure. For the Cu/Fe multilayer, the ductile Cu layers accommodate the majority of plastic deformation upon compression, while cracking in the Fe layers leads to the failure of the multilayer. Finite element models of microcompression tests were also developed to provide insights into the deformation behaviours of the multilayer and monolithic thin films. © 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

An improved Gbest guided artificial bee colony (IGGABC) algorithm for classification and prediction tasks

Artificial Neural Networks (ANN) performance depends on network topology, activation function, behaviors of data, suitable synapse's values and learning algorithms. Many existing works used different learning algorithms to train ANN for getting high performance. Artificial Bee Colony (ABC) algorithm is one of the latest successfully Swarm Intelligence based technique for training Multilayer Perceptron (MLP). Normally Gbest Guided Artificial Bee Colony (GGABC) algorithm has strong exploitation process for solving mathematical problems, however the poor exploration creates problems like slow convergence and trapping in local minima. In this paper, the Improved Gbest Guided Artificial Bee Colony (IGGABC) algorithm is proposed for finding global optima. The proposed IGGABC algorithm has strong exploitation and exploration processes. The experimental results show that IGGABC algorithm performs better than that standard GGABC, BP and ABC algorithms for Boolean data classification and time-series prediction tasks.