Electrical Stimulation of Myoblast Proliferation and Differentiation on Aligned Nanostructured Conductive Polymer Platforms

In this study, nanostructured conductive platforms synthesized from aligned multiwalled carbon nanotubes and polypyrrole are investigated as myo-regenerative scaffolds. Myotube formation follows a linear path on the platforms coinciding with extent of nanotopography. In addition, electrical stimulation enhances myo-nuclear number and differentiation. These studies demonstrate that conductive polymer platforms can be used to influence muscle cell behaviour through nanostructure and electrical stimulation.

One-Step Wet-Spinning Process of Poly(3,4-ethylenedioxythiophene):Poly(styrenesulfonate) Fibers and the Origin of Higher Electrical Conductivity

A simplified wet-spinning process for the production of continuous poly (3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) fibers is reported. Conductivity enhancement of PEDOT:PSS fibers up to 223 S cm−1 has been demonstrated when these fibers are exposed to ethylene glycol as a post-synthesis processing step. In a new spinning approach it is shown that by employing a spinning formulation consisting of an aqueous blend of PEDOT:PSS and poly(ethlylene glycol), the need for post-spinning treatment with ethylene glycol is eliminated. With this approach, 30-fold conductivity enhancements from 9 to 264 S cm−1 are achieved with respect to an untreated fiber. This one-step approach also demonstrates a significant enhancement in the redox properties of the fibers. These improvements are attributed to an improved molecular ordering of the PEDOT chains in the direction of the fiber axis and the consequential enrichment of linear (or expanded-coil like) conformation to preference bipolaronic electronic structures as evidenced by Raman spectroscopy, solid-state electron spin resonance (ESR) and in situ electrochemical ESR studies.

Construction of neural network-based prediction intervals for short-term electrical load forecasting

Short-term load forecasting (STLF) is of great importance for control and scheduling of electrical power systems. The uncertainty of power systems increases due to the random nature of climate and the penetration of the renewable energies such as wind and solar power. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in datasets. To quantify these potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for construction of prediction intervals (PIs). A newly proposed method, called lower upper bound estimation (LUBE), is applied to develop PIs using NN models. The primary multi-objective problem is firstly transformed into a constrained single-objective problem. This new problem formulation is closer to the original problem and has fewer parameters than the cost function. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Two case studies from Singapore and New South Wales (Australia) historical load datasets are used to validate the PSO-based LUBE method. Demonstrated results show that the proposed method can construct high quality PIs for load forecasting applications.

Anodal-tDCS applied during unilateral strength training increases strength and corticospinal excitability in the untrained homologous muscle

Evidence suggests that the cross-transfer of strength following unilateral training may be modulated by increased corticospinal excitability of the ipsilateral primary motor cortex, due to cross-activation. Anodal-tDCS (a-tDCS) has been shown to acutely increase corticospinal excitability and motor performance, which may enhance this process. Therefore, we sought to examine changes in neural activation and strength of the untrained limb following the application of a-tDCS during a single unilateral strength training session. Ten participants underwent three conditions in a randomized, double-blinded crossover design: (1) strength training + a-tDCS, (2) strength training + sham-tDCS and (3) a-tDCS alone. a-tDCS was applied for 20 min at 2 mA over the right motor cortex. Unilateral strength training of the right wrist involved 4 × 6 wrist extensions at 70 % of maximum. Outcome measures included maximal voluntary strength, corticospinal excitability, short-interval intracortical inhibition, and cross-activation. We observed a significant increase in strength of the untrained wrist (5.27 %), a decrease in short-interval intracortical inhibition (−13.49 %), and an increase in cross-activation (15.71 %) when strength training was performed with a-tDCS, but not following strength training with sham-tDCS, or tDCS alone. Corticospinal excitability of the untrained wrist increased significantly following both strength training with a-tDCS (17.29 %), and a-tDCS alone (15.15 %), but not following strength training with sham-tDCS. These findings suggest that a single session of a-tDCS combined with unilateral strength training of the right limb increases maximal strength and cross-activation to the contralateral untrained limb.

Porous carbon nanotube/polyvinylidene fluoride composite material: Superhydrophobicity/superoleophilicity and tunability of electrical conductivity

Porous carbon nanotube/polyvinylidene fluoride (CNT/PVDF) composite material can be fabricated via formation and freeze-drying of a gel. The field emission scanning electron microscopy, nitrogen adsorption-desorption and pore size distribution analysis reveal that the introduction of a small amount of carbon nanotubes (CNTs) can effectively increase the surface roughness and porosity of polyvinylidene fluoride (PVDF). Contact angle measurements of water and oil indicate that the as-obtained composite material is superhydrophobic and superoleophilic. Further experiments demonstrate that these composite material can be efficiently used to separate/absorb the insoluble oil from oil polluted water as membrane/absorbent. Most importantly, the electrical conductivity of such porous CNT/PVDF composite material can be tuned by adjusting the mass ratio of CNT to PVDF without obviously changing the superhydrophobicity or superoleophilicity. The unique properties of the porous CNT/PVDF composite material make it a promising candidate for oil-polluted water treatment as well as water-repellent catalyst-supporting electrode material.

Structure of the electrical double layer at aqueous gold and silver interfaces for saline solutions

We report the structure of the electrical double layer, determined from molecular dynamics simulations, for a range of saline solutions (NaCl, KCl, MgCl2 and CaCl2) at both 0.16 and 0.60molkg(-1) on different facets of the gold and silver aqueous interfaces. We consider the Au/Ag(111), native Au/Ag(100) and reconstructed Au(100)(5×1) facets. For a given combination of metallic surface and facet, some variations in density profile are apparent across the different cations in solution, with the corresponding chloride counterion profiles remaining broadly invariant. All density profiles at the higher concentration are predicted to be very similar to their low-concentration counterparts. We find that each electrolyte responds differently to the different metallic surface and facets, particularly those of the divalent metal ions. Our findings reveal marked differences in density profiles between facets for a given metallic interface for both Mg(2+) and Ca(2+), with Na(+) and K(+) showing much less distinction. Mg(2+) was the only ion for which we find evidence of materials-dependent differences in interfacial solution structuring between the Ag and Au.

Understanding the effects of electrical interference signals and the environment on the effectiveness of cathodic protection

Cathodic protection (CP) failure due to excursions from safe CP levels is a challenge for the protection and maintenance of buried energy pipelines. Although research shows that stray current is a major factor contributing to CP failure, there is little consensus on how 'big' the excursions (either in magnitude, length or frequency) need to be in order to cause pipeline corrosion problems. This uncertainty has caused difficulties in selecting suitable parameters in relevant industry standards. This paper provides a brief review of past research on different factors affecting CP efficiency. Preliminary results from new electrochemical cells designed to develop an understanding of how CP excursions away from the 'safe' level can lead to corrosion problems are also presented.

Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.

Strain-responsive polyurethane/PEDOT:PSS elastomeric composite fibers with high electrical conductivity

It is a challenge to retain the high stretchability of an elastomer when used in polymer composites. Likewise, the high conductivity of organic conductors is typically compromised when used as filler in composite systems. Here, it is possible to achieve elastomeric fiber composites with high electrical conductivity at relatively low loading of the conductor and, more importantly, to attain mechanical properties that are useful in strain-sensing applications. The preparation of homogenous composite formulations from polyurethane (PU) and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) that are also processable by fiber wet-spinning techniques are systematically evaluated. With increasing PEDOT:PSS loading in the fiber composites, the Young's modulus increases exponentially and the yield stress increases linearly. A model describing the effects of the reversible and irreversible deformations as a result of the re-arrangement of PEDOT:PSS filler networks within PU and how this relates to the electromechanical properties of the fibers during the tensile and cyclic stretching is presented. Conducting elastomeric fibers based on a composite of polyurethane (PU) and PEDOT:PSS, produced by a wet-spinning method, have high electrical conductivity and stretchability. These fibers can sense large strains by changes in resistance. The PU/PEDOT:PSS fiber is optimized to achieve the best strain sensing. PU/PEDOT:PSS fibers can be produced on a large scale and integrated into conventional textiles by weaving or knitting. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The influence of graphene on the electrical communication through organic layers on graphite and gold electrodes

The influence of graphene on the electrical communication through organic layers fabricated on graphite and gold electrodes is investigated. These layers were prepared by in situ reductive adsorption of 4-aminobenzoic acid in the presence of NaNO2 and HCl to have surface bound carboxylic acid functionalities, followed by covalent attachment of 1-aminopyrene via an amide coupling reaction to have surface bound pyrene groups for graphene immobilization via noncovalent π-π stacking interaction. The coverage of the layers created via reductive adsorption on graphite electrodes was found to be much higher than that on gold electrodes. It was revealed that graphene significantly enhances the electrical communication through the layers on graphite electrodes but on gold electrodes the enhancement effect through the layers was only minor. However, when gold electrodes were modified with a self-assembled monolayer (SAM) of propanethiol the subsequent immobilization of graphene resulted in a significant enhancement of the electrical communication. It is also found that immobilization of graphene could affect the electron transfer between the redox probe, pyrene and the underlying electrodes. Atomic force microscopy (AFM) and scanning electron microscopy (SEM) were used to characterize the graphene sheets. Cyclic voltammetry, electrochemical impedance spectroscopy (EIS), and X-ray photoelectron spectroscopy (XPS) were also used to characterize the stepwise modified electrodes. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.