Structural characterisation and environmental application of organoclays for the removal of phenolic compounds

Modified montmorillonite was prepared at different surfactant (HDTMA) loadings through ion exchange. The conformational arrangement of the loaded surfactants within the interlayer space of MMT was obtained by computational modelling. The conformational change of surfactant molecules enhance the visual understanding of the results obtained from characterization methods such as XRD and surface analysis of the organoclays. Batch experiments were carried out for the adsorption of p-chlorophenol (PCP) and different conditions (pH and temperature) were used in order to determine the optimum sorption. For comparison purpose, the experiments were repeated under the same conditions for p-nitrophenol (PNP). Langmuir and Freundlich equations were applied to the adsorption isotherm of PCP and PNP. The Freundlich isotherm model was found to be the best fit for both of the phenolic compounds. This involved multilayer adsorptions in the adsorption process. In particular, the binding affinity value of PNP was higher than that of PCP and this is attributable to their hydrophobicities. The adsorption of the phenolic compounds by organoclays intercalated with highly loaded surfactants was markedly improved possibly due to the fact that the intercalated surfactant molecules within the interlayer space contribute to the partition phases, which result in greater adsorption of the organic pollutants.

Spectrum sensing optimisation for dynamic primary user signal

We propose a multi-layer spectrum sensing optimisation algorithm to maximise sensing efficiency by computing the optimal sensing and transmission durations for a fast changing, dynamic primary user. Dynamic primary user traffic is modelled as a random process, where the primary user changes states during both the sensing period and transmission period to reflect a more realistic scenario. Furthermore, we formulate joint constraints to correctly reflect interference to the primary user and lost opportunity of the secondary user during the transmission period. Finally, we implement a novel duty cycle based detector that is optimised with respect to PU traffic to accurately detect primary user activity during the sensing period. Simulation results show that unlike currently used detection models, the proposed algorithm can jointly optimise the sensing and transmission durations to simultaneously satisfy the optimisation constraints for the considered primary user traffic.

The interplay between chondrocyte redifferentiation pellet size and oxygen concentration

Chondrocytes dedifferentiate during ex vivo expansion on 2-dimensional surfaces. Aggregation of the expanded cells into 3-dimensional pellets, in the presence of induction factors, facilitates their redifferentiation and restoration of the chondrogenic phenotype. Typically 1×105–5×105 chondrocytes are aggregated, resulting in “macro” pellets having diameters ranging from 1–2 mm. These macropellets are commonly used to study redifferentiation, and recently macropellets of autologous chondrocytes have been implanted directly into articular cartilage defects to facilitate their repair. However, diffusion of metabolites over the 1–2 mm pellet length-scales is inefficient, resulting in radial tissue heterogeneity. Herein we demonstrate that the aggregation of 2×105 human chondrocytes into micropellets of 166 cells each, rather than into larger single macropellets, enhances chondrogenic redifferentiation. In this study, we describe the development of a cost effective fabrication strategy to manufacture a microwell surface for the large-scale production of micropellets. The thousands of micropellets were manufactured using the microwell platform, which is an array of 360×360 µm microwells cast into polydimethylsiloxane (PDMS), that has been surface modified with an electrostatic multilayer of hyaluronic acid and chitosan to enhance micropellet formation. Such surface modification was essential to prevent chondrocyte spreading on the PDMS. Sulfated glycosaminoglycan (sGAG) production and collagen II gene expression in chondrocyte micropellets increased significantly relative to macropellet controls, and redifferentiation was enhanced in both macro and micropellets with the provision of a hypoxic atmosphere (2% O2). Once micropellet formation had been optimized, we demonstrated that micropellets could be assembled into larger cartilage tissues. Our results indicate that micropellet amalgamation efficiency is inversely related to the time cultured as discreet microtissues. In summary, we describe a micropellet production platform that represents an efficient tool for studying chondrocyte redifferentiation and demonstrate that the micropellets could be assembled into larger tissues, potentially useful in cartilage defect repair.

Electrochemically exfoliated graphene for electrode films : effect of graphene flake thickness on the sheet resistance and capacitive properties

We present an electrochemical exfoliation method to produce controlled thickness graphene flakes by ultrasound assistance. Bilayer graphene flakes are dominant in the final product by using sonication during the electrochemical exfoliation process, while without sonication the product contains a larger percentage of four-layer graphene flakes. Graphene sheets prepared by using the two procedures are processed into films to measure their respective sheet resistance and optical transmittance. Solid-state electrolyte supercapacitors are made using the two types of graphene films. Our study reveals that films with a higher content of multilayer graphene flakes are more conductive, and their resistance is more easily reduced by thermal annealing, making them suitable as transparent conducting films. The film with higher content of bilayer graphene flakes shows instead higher capacitance when used as electrode in a supercapacitor.

The role of defects, or active states, in surface electrochemistry with particular reference to gold in neutral solution

Metastable, active, or nonequilibrium states due to the presence of abnormal structures and various types of defects are well known in metallurgy. The role of such states at gold surfaces in neutral aqueous media (an important electrode system in the microsensor area) was explored using cyclic voltammetry. It was demonstrated that, as postulated in earlier work from this laboratory, there is a close relationship between premonolayer oxidation, multilayer hydrous oxide reduction and electrocatalytic behaviour in the case of this and other metal electrode systems. Some of the most active, and therefore most important, entities at surfaces (e.g., metal adatoms) are not readily imageable or detectable by high resolution surface microscopy techniques. Cyclic voltammetry, however, provides significant, though not highly specific, information about such species. The main conclusion is that further practical and theoretical work on active states of metal surfaces is highly desirable as their behaviour is not simple and is of major importance in many electrocatalytic processes.

Multiband photoluminescence from carbon nanoflakes synthesized by hot filament CVD: towards solid-state white light sources

Carbon nanoflakes (CNFLs) are synthesized on silicon substrates deposited with carbon islands in a methane environment using hot filament chemical vapor deposition. The structure and composition of the CNFLs are studied using field emission scanning electron microscopy, high-resolution transmission electron microscopy, micro-Raman spectroscopy, and X-ray photoelectron spectroscopy. The results indicate that the CNFLs are composed of multilayer graphitic sheets and the area and thickness of CNFs increase with the growth time. The photoluminescence (PL) of CNFLs excited by a 325 nm He-Cd laser exhibits three strong bands centered at 408, 526, and 699 nm, which are related to the chemical radicals terminated on the CNFLs and the associated interband transitions. The PL results indicate that the CNFLs are promising as an advanced nano-carbon material capable of generating white light emission. These outcomes are significant to control the electronic structure of CNFLs and contribute to the development of next-generation solid-state white light emission devices. © 2014 the Partner Organisations.

A high frequency, low voltage stackable full bridge module for modular multilevel cascade converter (MMCC) research

A new small full bridge module for MMCC research is presented. Each full bridge converter cell is a single small (65 × 30 mm) multilayer PCB with two low voltage high current (22 V, 40 A) integrated half bridge ICs and the necessary isolated control signals and auxiliary power supply (2500 V isolation). All devices are surface mount, minimising cell height (4 mm) and parasitic inductance. Each converter cell can be physically stacked with PCB connectors propagating the control signals and inter-cell power connections. Many cells can be trivially stacked to create a large multilevel converter leg with isolated auxiliary power and control signals. Any of the MMCC family members is then easily formed. With a change in placement of stacking connector, a parallel connection of bridges is also possible. Operation of a nine level parallel full bridge is demonstrated at 12 V and 384 kHz switching frequency delivering a 30 W 2 kHz sinewave into a resistive load. A number of new applications for this novel module aside from MMCC development are listed.

Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

An evaluation of the neocognitron

We describe a sequence of experiments investigating the strengths and limitations of Fukushima's neocognitron as a handwritten digit classifier. Using the results of these experiments as a foundation, we propose and evaluate improvements to Fukushima's original network in an effort to obtain higher recognition performance. The neocognitron's performance is shown to be strongly dependent on the choice of selectivity parameters and we present two methods to adjust these variables. Performance of the network under the more effective of the two new selectivity adjustment techniques suggests that the network fails to exploit the features that distinguish different classes of input data. To avoid this shortcoming, the network's final layer cells were replaced by a nonlinear classifier (a multilayer perceptron) to create a hybrid architecture. Tests of Fukushima's original system and the novel systems proposed in this paper suggest that it may be difficult for the neocognitron to achieve the performance of existing digit classifiers due to its reliance upon the supervisor's choice of selectivity parameters and training data. These findings pertain to Fukushima's implementation of the system and should not be seen as diminishing the practical significance of the concept of hierarchical feature extraction embodied in the neocognitron. © 1997 IEEE.

Error and variance bounds on sigmoidal neurons with weight and input errors

Bounds on the expectation and variance of errors at the output of a multilayer feedforward neural network with perturbed weights and inputs are derived. It is assumed that errors in weights and inputs to the network are statistically independent and small. The bounds obtained are applicable to both digital and analogue network implementations and are shown to be of practical value.

Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Suppressed thermal conductivity of bilayer graphene with vacancy-initiated linkages

Through larger-scale molecular dynamics simulations, we investigated the impacts from vacancy-initiated linkages on the thermal conductivity of bilayer graphene sheets (of size L × W = 24.5 nm × 3.7 nm). Three different interlayer linkages, including divacancy bridging, “spiro” interstitial bridging and Frenkel pair defects, are considered. It is found that the presence of interlayer linkages induces a significant degradation in the thermal conductivity of the bilayer graphene sheet. The degradation is strongly dependent on the interlayer linkage type, concentration and location. More importantly, the linkages that contain vacancies lead to more severe suppression of the thermal conductivity, in agreement with theoretical predictions that vacancies induce strong phonon scattering. Our finding provides useful guidelines for the application of multilayer graphene sheets in practical thermal management.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Effect of covalent functionalisation on thermal transport across graphene-polymer interfaces

This paper is concerned with the interfacial thermal resistance for polymer composites reinforced by various covalently functionalised graphene. By using molecular dynamics simulations, the obtained results show that the covalent functionalisation in graphene plays a significant role in reducing the graphene-paraffin interfacial thermal resistance. This reduction is dependent on the coverage and type of functional groups. Among the various functional groups, butyl is found to be the most effective in reducing the interfacial thermal resistance, followed by methyl, phenyl and formyl. The other functional groups under consideration such as carboxyl, hydroxyl and amines are found to produce negligible reduction in the interfacial thermal resistance. For multilayer graphene with a layer number up to four, the interfacial thermal resistance is insensitive to the layer number. The effects of the different functional groups and the layer number on the interfacial thermal resistance are also elaborated using the vibrational density of states of the graphene and the paraffin matrix. The present findings provide useful guidelines in the application of functionalised graphene for practical thermal management.

New microscale vertically oriented organic photovoltaics cells

We initially look at the changing energy environment and how that can have a dramatic change on the potential of alternative energies, in particular those of organic photovoltaicvs (OPV) cells. In looking at OPV's we also address the aspects of where we are with the current art and why we may not be getting the best from our materials. In doing so, we propose the idea of changing how we build organic photovoltaics by addressing the best method to contain light within the devices. Our initial effort is in addressing how these microscale optical concentrators work in the form of optical fibers in terms of absorption. We have derived a mathematical method which takes account of the input angle of light to achieve optimum absorption. However, in doing so we also address the complex issue how the changing refractive indices in a multilayer device can alter how we input the light. We have found that by knowing the materials refractive index our model takes into account the incident plane, meridonal plane, cross sectional are and path length to ensure optical angular input. Secondly, we also address the practicalities of making such vertical structures the greater issue of changing light intensity incident on a solar cell and how that aspects alters how we view the performance of organic solar cells.