YBACUO PRBACUO MULTILAYERS BY EXCIMER LASER ABLATION

YBaCuO films with (001) orientation have been deposited on MgO by laser ablation at 248 and 193 nm wavelengths. Transitions to zero resistance at 87 K and 90 K have been reproducibly achieved in the respective cases. Optical spectroscopic studies of the plume show the importance of molecular species in the ablation if good superconducting films are to be formed. The substrate position in the plume and substrate temperature are important in determining film quality. The influence of oxygen gas pressure can be significant. SEM studies show the occurrence of second-phase outcrops with a needle-like morphology aligned over the whole area of the film along two mutually perpendicular directions on the film surface. Film orientation is determined by XRD and R against T is measured down to 80 K in a hydrogen exchange gas cryostat. Characterization studies of device-related multilayer YBaCuO/PrBaCuO structures by XRD are presented.

A multi-fibre multi-layer representative volume element (M2RVE) for prediction of matrix and interfacial damage in composite laminates

Multiscale micro-mechanics theory is extensively used for the prediction of the material response and damage analysis of unidirectional lamina using a representative volume element (RVE). Th is paper presents a RVE-based approach to characterize the materi al response of a multi-fibre cross-ply laminate considering the effect of matrix damage and fibre-matrix interfacial strength. The framework of the homogenization theory for periodic media has been used for the analysis of a 'multi-fibre multi-layer representative volume element' (M2 RVE) representing cross-ply laminate. The non-homogeneous stress-strain fields within the M2RVE are related to the average stresses and strains by using Gauss theorem and the Hill-Mandal strain energy equivalence principle. The interfacial bonding strength affects the in-plane shear stress-strain response significantl y. The material response predicted by M2 RVE is in good agreement with the experimental results available in the literature. The maximum difference between the shear stress predicted using M2 RVE and the experimental results is ~15% for the bonding strength of 30MPa at the strain value of 1.1%

Realizing a high magnetic moment in Gd/Cr/FeCo: The role of the rare earth

The search for materials or systems exhibiting a high magnetic saturation has been of longstanding importance. It has been suggested that increased saturation could be achieved by coupling a transition metal via a spacer to a rare earth. We report Gd/Cr/Fe₇₀Co₃₀ multilayer stacks and find reduced yet modulating magnetic moment as a function of Cr thickness. Through a micro structural analysis the lowered moment is indicated by the nucleation of the ultrathin Gd films into an fcc phase. We discuss the possible solution in terms of quasi-perfect lattice match seed material to promote growth of hcp Gd.

Mimicking the colourful wing scale structure of the Papilio blumei butterfly

The brightest and most vivid colours in nature arise from the interaction of light with surfaces that exhibit periodic structure on the micro- and nanoscale. In the wings of butterflies, for example, a combination of multilayer interference, optical gratings, photonic crystals and other optical structures gives rise to complex colour mixing. Although the physics of structural colours is well understood, it remains a challenge to create artificial replicas of natural photonic structures(1-3). Here we use a combination of layer deposition techniques, including colloidal self-assembly, sputtering and atomic layer deposition, to fabricate photonic structures that mimic the colour mixing effect found on the wings of the Indonesian butterfly Papilio blumei. We also show that a conceptual variation to the natural structure leads to enhanced optical properties. Our approach offers improved efficiency, versatility and scalability compared with previous approaches(4-6).

Influence of solution chemistry on Cr(VI) reduction and complexation onto date-pits/tea-waste biomaterials

Tea waste (TW) and Date pits (DP) were investigated for their potential to remove toxic Cr(VI) ions from aqueous solution. Investigations showed that the majority of the bound Cr(VI) ions were reduced to Cr(III) after biosorption at acidic conditions. The electrons for the reduction of Cr(VI) may have been donated from the TW and DP biomasses. The experimental data obtained for Cr(VI)-TW and Cr(VI)-DP at different solution temperatures indicate a multilayer type biosorption, which explains why the Sips isotherm accurately represents the experimental data obtained in this study. The Sips maximum biosorption capacities of Cr(VI) onto TW and DP were 5.768 and 3.199 mmol/g at 333 K, respectively, which is comparatively superior to most other low-cost biomaterials. Fourier transform infrared spectroscopic analysis of the metal loaded biosorbents confirmed the participation of -COOH, -NH and O-CH groups in the reduction and complexation of chromium. Thermodynamic parameters demonstrated that the biosorption of Cr(VI) onto TW and DP biomass was endothermic, spontaneous and feasible at 303-333 K. The results evidently indicated that tea waste and date pits would be suitable biosorbents for Cr(VI) in wastewater under specific conditions.

The effect of photo-oxidation on the sticking and reactivity of Ag on amorphous GeS2

Photo-oxidation of amorphous GeS2 films illuminated by band-gap radiation drastically alters the growth mode and reactivity of subsequently deposited Ag. In the former case (monolayer/simultaneous multilayer growth) the Ag reacts with both Ge and S sites. In the latter case (Stranski-Krastanov growth) Ge sites are selectively oxidized and film growth proceeds by Ag nucleation at the unoxidized S sites. The behaviour is very different from that reported earlier for Zn deposition on GeS2, where photo-oxidation results in very large changes in metal sticking probability. XPS, XAES and EXAFS data provide the basis for understanding both this phenomenon and the very different photodiffusion behaviour of Zn and Ag in GeS2.

Fourier Transform using a Rotman Lens

In this paper, the design constraints that are required for a Rotman lens to realize discrete Fourier transform (DFT) amplitude and phase functionality are derived. A Fourier Rotman lens has been designed, fabricated and validated. The amplitude and phase response and the array pattern based on the CST™ results are validated with the theoretical DFT results. To the best of the authors' knowledge, this is the first Fourier Rotman lens to be fabricated and validated. The solution provided replaces multilayer Butler matrix solutions with a simple single layer microstrip technology.

Multi-Attribute SCADA-Specific Intrusion Detection System for Power Networks

The increased interconnectivity and complexity of supervisory control and data acquisition (SCADA) systems in power system networks has exposed the systems to a multitude of potential vulnerabilities. In this paper, we present a novel approach for a next-generation SCADA-specific intrusion detection system (IDS). The proposed system analyzes multiple attributes in order to provide a comprehensive solution that is able to mitigate varied cyber-attack threats. The multiattribute IDS comprises a heterogeneous white list and behavior-based concept in order to make SCADA cybersystems more secure. This paper also proposes a multilayer cyber-security framework based on IDS for protecting SCADA cybersecurity in smart grids without compromising the availability of normal data. In addition, this paper presents a SCADA-specific cybersecurity testbed to investigate simulated attacks, which has been used in this paper to validate the proposed approach.

Semi batch learning with store management using enhanced conjugate gradient

This paper explores the performance of sliding-window based training, termed as semi batch, using multilayer perceptron (MLP) neural network in the presence of correlated data. The sliding window training is a form of higher order instantaneous learning strategy without the need of covariance matrix, usually employed for modeling and tracking purposes. Sliding-window framework is implemented to combine the robustness of offline learning algorithms with the ability to track online the underlying process of a function. This paper adopted sliding window training with recent advances in conjugate gradient direction with application of data store management e.g. simple distance measure, angle evaluation and the novel prediction error test. The simulation results show the best convergence performance is gained by using store management techniques. © 2012 Springer-Verlag.

Bond quality of cross-laminated timber from Irish Sitka spruce

The potential use of Irish-grown Sitka spruce for cross-laminated timber (CLT) manufacture is investigated as this would present new opportunities and novel products for Irish timber in the home and export markets. CLT is a prefabricated multilayer engineered wood product made of at least three orthogonally bonded layers of timber. In order to increase rigidity and stability, successive layers of boards are placed cross-wise to form a solid timber panel. Load-bearing CLT wall and floor panels are easily assembled on site to form multi-storey buildings. This improves construction and project delivery time, reduces costs,
and maximises efficiency on all levels.

The paper addresses the quality of the interface bond between the laminations making up the panels, which is of fundamental importance to the load bearing capacity. Therefore, shear tests were carried out on nine test bars of three glue lines each. Moreover, delamination tests were performed on samples subjected to accelerated aging, in order to assess the durability of bonds subjected to severe environmental conditions. In addition, this paper gives an indication on thickness tolerances of planed Irish Sitka spruce lamellas, which is likely to be a critical factor for bonding quality and adhesive selection. The test results of bond quality presented in this study were within requirements of prEN 16351:2013.

Intelligent potentiostat for identification of heavy metals in situ

This article presents a low-cost portable electrochemical instrument capable of on-site identification of heavy metals. The instrument acquires metal-specific voltage and current signals by the application of differential pulse anodic stripping voltammetry. This technique enhances the analytical current and rejects the background current, resulting in a higher signal-to-noise ratio for a better detection limit. The identification of heavy metals is based on an intelligent machine-based method using a multilayer perceptron neural network consisting of three layers of neurons. The neural network is implemented using a 16 bit microcontroller. The system is developed for use in the field in order to avoid expensive and time-consuming procedures and can be used in a variety of situations to help environmental assessment and control. 

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

High Strength Thermoplastic Bonding for Multi-channel, Multi-layer Lab-on-Chip Devices for Ocean and Environmental applications

A solvent-vapour thermoplastic bonding process is reported which provides high strength bonding of PMMA over a large area for multi-channel and multi-layer microfluidic devices with shallow high resolution channel features. The bond process utilises a low temperature vacuum thermal fusion step with prior exposure of the substrate to chloroform (CHCl3) vapour to reduce bond temperature to below the PMMA glass transition temperature. Peak tensile and shear bond strengths greater than 3 MPa were achieved for a typical channel depth reduction of 25 µm. The device-equivalent bond performance was evaluated for multiple layers and high resolution channel features using double-side and single-side exposure of the bonding pieces. A single-sided exposure process was achieved which is suited to multi-layer bonding with channel alignment at the expense of greater depth loss and a reduction in peak bond strength. However, leak and burst tests demonstrate bond integrity up to at least 10 bar channel pressure over the full substrate area of 100 mm x 100 mm. The inclusion of metal tracks within the bond resulted in no loss of performance. The vertical wall integrity between channels was found to be compromised by solvent permeation for wall thicknesses of 100 µm which has implications for high resolution serpentine structures. Bond strength is reduced considerably for multi-layer patterned substrates where features on each layer are not aligned, despite the presence of an intermediate blank substrate. Overall a high performance bond process has been developed that has the potential to meet the stringent specifications for lab-on-chip deployment in harsh environmental conditions for applications such as deep ocean profiling.