Mechanical, electrical and electro-mechanical properties of thermoplastic elastomer styrene–butadiene–styrene/multiwall carbon nanotubes composites

Composites of styrene–butadiene–styrene (SBS) block copolymer with multiwall carbon nanotubes were processed by solution casting to investigate the influence of filler content, the different ratios of styrene/butadiene in the copolymer and the architecture of the SBS matrix on the electrical, mechanical and electro-mechanical properties of the composites. It was found that filler content and elastomer matrix architecture influence the percolation threshold and consequently the overall composite electrical conductivity. The mechanical properties are mainly affected by the styrene and filler content. Hopping between nearest fillers is proposed as the main mechanism for the composite conduction. The variation of the electrical resistivity is linear with the deformation. This fact, together with the gauge factor values in the range of 2–18, results in appropriate composites to be used as (large) deformation sensors.

On the origin of the electrical response of vapor grown carbon nanofiber + epoxy composites

The origin of the electrical response of vapor grown carbon nanofiber (VGCNF) + epoxy composites is investigated by studying the electrical behavior of VGCNF with resin, VGCNF with hardener and cured composites, separately. It is demonstrated that the onset of the conductivity is associated to the emergence of a weak disorder regime. It is also shown that the weak disorder regime is related to a hopping depending on the physical properties of the polymer matrix.

Temperature dependence of the electrical conductivity of vapor grown carbon nanofiber/epoxy composites with different filler dispersion levels

The influence of the dispersion of vapor grown carbon nanofibers (VGCNF) on the electrical properties of VGCNF/epoxy composites has been studied. A homogeneous dispersion of the VGCNF does not imply better electrical properties. The presence of well distributed clusters appears to be a key factor for increasing composite conductivity. It is also shown that the main conduction mechanism has an ionic nature for concentrations below the percolation threshold, while above the percolation threshold it is dominated by hopping between the fillers. Finally, using the granular system theory it is possible to explain the origin of conduction at low temperatures.

The role of disorder on the AC and DC electrical conductivity of vapour grown carbon nanofibre/epoxy composites

Four dispersion methods were used for the preparation of vapour grown carbon nanofibre (VGCNF)/epoxy composites. It is shown that each method induces certain levels of VGCNF dispersion and distribution within the matrix, and that these have a strong influence on the composite electrical properties. A homogenous VGCNF dispersion does not necessarily imply higher electrical conductivity. In fact, it is concluded that the presence of well distributed clusters, rather than a fine dispersion, is more important for achieving larger conductivities for a given VGCNF concentration. It is also found that the conductivity can be described by a weak disorder regime.

Modeling Carbon Nanotube Electrical Properties in CNT/Polymer Composites

In this work it is demonstrated that the capacitance between two cylinders increases with the rotation angle and it has a fundamental influence on the composite dielectric constant. The dielectric constant is lower for nematic materials than for isotropic ones and this can be attributed to the effect of the filler alignment in the capacitance. The effect of aspect ratio in the conductivity is also studied in this work. Finally, based on previous work and by comparing to results from the literature it is found that the electrical conductivity in this type of composites is due to hopping between nearest fillers resulting in a weak disorder regime that is similar to the single junction expression.

Coupling Mechanical and Electrical Behavior in the Multi-scale Simulation of Polymer-based CNT Nanocomposites

A numeric model has been proposed to investigate the mechanical and electrical properties of a polymeric/carbon nanotube (CNT) composite material subjected to a deformation force. The reinforcing phase affects the behavior of the polymeric matrix and depends on the nanofiber aspect ratio and preferential orientation. The simulations show that the mechanical behavior of a computer generated material (CGM) depends on fiber length and initial orientation in the polymeric matrix. It is also shown how the conductivity of the polymer/CNT composite can be calculated for each time step of applied stress, effectively providing the ability to simulate and predict strain-dependent electrical behavior of CNT nanocomposites.

The influence of matrix mediated hopping conductivity, filler concentration, aspect ratio and orientation on the electrical response of carbon nanotube/polymer nanocomposites

A model to simulate the conductivity of carbon nanotube/polymer nanocomposites is presented. The proposed model is based on hopping between the fillers. A parameter related to the influence of the matrix in the overall composite conductivity is defined. It is demonstrated that increasing the aspect ratio of the fillers will increase the conductivity. Finally, it is demonstrated that the alignment of the filler rods parallel to the measurement direction results in higher conductivity values, in agreement with results from recent experimental work.

The influence of the dispersion method on the electrical properties of vapor-grown carbon nanofiber/epoxy composites

The influence of the dispersion of vapor-grown carbon nanofibers (VGCNF) on the electrical properties of VGCNF/ Epoxy composites has been studied. A homogenous dispersion of the VGCNF does not imply better electrical properties. In fact, it is demonstrated that the most simple of the tested dispersion methods results in higher conductivity, since the presence of well-distributed nanofiber clusters appears to be a key factor for increasing composite conductivity.

Effect of cylindrical filler aggregation on the electrical conductivity of composites

This work reports on the effect of carbon nanotube aggregation on the electrical conductivity and other network properties of polymer/carbon nanotube composites by modeling the carbon nanotubes as hard-core cylinders. It is shown that the conductivity decreases for increasing filler aggregation, and that this effect is more significant for higher cylinder volume fractions. It is also demonstrated, for volume fractions at which the giant component is present, that increasing the fraction of cylinders within clusters leads to a break of the giant component and the formation of a set of finite clusters. The decrease of the giant component with the increase of the fraction of cylinders within the cluster can be related to a decrease of the spanning probability due to a decrease of the number of cylinders between the clusters. Finally, it is demonstrated that the effect of aggregation can be understood by employing the network theory.

Electrical transport properties of CuS single crystals

Electrical resistivity, transverse magnetoresistance and thermoelectric power measurements were performed on CuS high quality single crystals in the range 1.2-300 K and under fields of up to 16 T. The zero field resistivity data are well described below 55 K by a quasi-2D model, consistent with a carrier confinement at lower temperatures, before the transition to the superconducting state. The transverse magnetoresistance develops mainly below 30 K and attains values as large as 470% for a 16 T field at 5 K, this behaviour being ascribed to a band effect mechanism, with a possible magnetic field induced DOS change at the Fermi level. The transverse magnetoresistance shows no signs of saturation, following a power law with field Delta rho/rho(0) proportional to H(1.4), suggesting the existence of open orbits for carriers at the Fermi surface. The thermoelectric power shows an unusual temperature dependence, probably as a result of the complex band structure of CuS.

Lighting conditions in assembling electrical industry

Proper lighting is a prerequisite for obtaining a good working environment. Good lighting includes quantity and quality requirements, and should necessarily be appropriate to the activity/task being carried out, bearing in mind the comfort and visual efficiency of the worker. Apart from the advantages in the health and welfare for the workers, good lighting also leads to better job performance (faster), less errors, better safety, fewer accidents and less absenteeism. The overall effect is: better productivity.

TINSEL – a web-based intelligent tutoring system on electrical installation design

This paper presents a project consisting on the development of an Intelligent Tutoring System, for training and support concerning the development of electrical installation projects to be used by electrical engineers, technicians and students. One of the major goals of this project is to devise a teaching model based on Intelligent Tutoring techniques, considering not only academic knowledge but also other types of more empirical knowledge, able to achieve successfully the training of electrical installation design.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Mobi_System: A Personal Travel Assistance for Electrical Vehicles in Smart Cities

In this work it is proposed the design of a mobile system to assist car drivers in a smart city environment oriented to the upcoming reality of Electric Vehicles (EV). Taking into account the new reality of smart cites, EV introduction, Smart Grids (SG), Electrical Markets (EM), with deregulation of electricity production and use, drivers will need more information for decision and mobility purposes. A mobile application to recommend useful related information will help drivers to deal with this new reality, giving guidance towards traffic, batteries charging process, and city mobility infrastructures (e. g. public transportation information, parking places availability and car & bike sharing systems). Since this is an upcoming reality with possible process changes, development must be based on agile process approaches (Web services).

Knowledge extraction from medium voltage load diagrams to support the definition of electrical tariffs

With the electricity market liberalization, distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. In this environment all consumers are free to choose their electricity supplier. A fair insight on the customer´s behaviour will permit the definition of specific contract aspects based on the different consumption patterns. In this paper Data Mining (DM) techniques are applied to electricity consumption data from a utility client’s database. To form the different customer´s classes, and find a set of representative consumption patterns, we have used the Two-Step algorithm which is a hierarchical clustering algorithm. Each consumer class will be represented by its load profile resulting from the clustering operation. Next, to characterize each consumer class a classification model will be constructed with the C5.0 classification algorithm.