Retraction notice to “Microstructural image analysis and structure–electrical conductivity relationship of single- and multiple-filler conductive composites” [Compos. Sci. Technol. 68 (2008) 1679–1687]

2010 ◽  
Vol 70 (7) ◽  
pp. 1196
Author(s):  
Radwan Dweiri ◽  
Jaafar Sahari
Keyword(s):  
Electrical Conductivity ◽  
Image Analysis ◽  
Conductive Composites ◽  
Retraction Notice ◽  
Relationship Of

scholarly journals Prediction of Reproductive Success in Multiparous First Service Dairy Cows by Parameters from In-Line Sensors

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 334
Author(s):  
Ramūnas Antanaitis ◽  
Vida Juozaitienė ◽  
Dovilė Malašauskienė ◽  
Mindaugas Televičius ◽  
Mingaudas Urbutis ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Blood Parameters ◽  
Negative Energy ◽  
Hydroxybutyric Acid ◽  
Pregnancy Status ◽  
Group A ◽  
Biochemical Indices ◽  
Relationship Of ◽  
The Relationship

The aim of the current study was to evaluate the relationship of different parameters from an automatic milking system (AMS) with the pregnancy status of multiparous cows at first service and to assess the accuracy of such a follow-up with regard to blood parameters. Before the insemination of cows, blood samples for measuring biochemical indices were taken from the coccygeal vessels and the concentrations of blood serum albumin (ALB), cortisol, non-esterified fatty acids (NEFA) and the activities of aspartate aminotransferase (AST) and gamma glutamyltransferase (GGT) were determined. From oestrus day to seven days after oestrus, the following parameters were registered: milk yield (MY), electric milk conductivity, lactate dehydrogenase (LDH) and β-hydroxybutyric acid (BHB). The pregnancy status was evaluated using ultrasound “Easy scan” 30–35 days after insemination. Cows were grouped by reproductive status: PG− (non-pregnant; n = 48) and PG+ (pregnant; n = 44). The BHB level in PG− cows was 1.2 times higher (p < 0.005). The electrical conductivity of milk was statistically significantly higher in all quarters of PG− cows (1.07 times) than of PG+ cows (p < 0.05). The arithmetic mean of blood GGT was 1.61 times higher in PG− cows and the NEFA value 1.23 times higher (p < 0.05) compared with the PG+ group. The liver function was affected, the average ALB of PG− cows was 1.19 times lower (p < 0.05) and the AST activity was 1.16 times lower (p < 0.05) compared with PG+ cows. The non-pregnant group had a negative energy balance demonstrated by high in-line milk BHB and high blood NEFA concentrations. We found a greater number of cows with cortisol >0.0.75 mg/dL in the non-pregnant group. A higher milk electrical conductivity in the non-pregnant cows pointed towards a greater risk of mastitis while higher GGT activities together with lower albumin concentrations indicated that the cows were more affected by oxidative stress.

Structure-Electrical Transport Property Relationship of Anisotropic iPP/CNT Films

2013 ◽  
Vol 1499 ◽  
Author(s):  
Parvathalu Kalakonda ◽  
Michael Daly ◽  
Kaikai Xu ◽  
Yaniel Cabrera ◽  
Robert Judith ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Electrical Transport ◽  
Preparation Method ◽  
Weight Percent ◽  
Electrical Transport Properties ◽  
Oriented Polymer ◽  
Nano Composite ◽  
Nano Structure ◽  
Relationship Of ◽  
Fine Tune

ABSTRACTThe internal micro/nano-structure of anisotropically oriented polymer/CNTs composites determines their macroscopic properties. However, the connections between the two are not fully understood. The varying of CNT concentration, preparation method, and a thermodynamic parameter (e.g. temperature) can all play interconnected role. In this work, the macroscopic electrical conductivity was measured perpendicular to the film thickness of an insulating polymer (isotactic PolyPropylene, iPP) and a nano-composite of iPP with 5 weight percent of CNT. The thin films studied were sheared (anisotropically nano-structured) and non-sheared (with random internal structure). In general the effect of melt shearing induces anisotropy on the electrical transport properties of the iPP/CNT films in directions parallel and perpendicular to the direction of orientation. Our results show that for the pure iPP, resistivity slightly increases with shear at higher temperatures. When CNTs are introduced, there is a large difference between the resistivity of the sheared and non-sheared nanocomposite. The sheared PNCs when the CNTs are aligned parallel to each other, have higher resistivity, which is possibly due to the higher concentration at which the percolation threshold occurs in this arrangement. The resistivity decreases overall, as the temperature increases from 0 to 50 °C. These results show that CNTs can be used to control and fine tune the desired macroscopic physical properties of nanocomposites, by concentration and orientation, such as electrical conductivity, for applications where such properties are necessary.

An improvement to the ANN-enhanced flow rating method

2021 ◽  
Author(s):  
Bernhard Schmid
Keyword(s):  
Neural Network ◽  
Electrical Conductivity ◽  
Water Resources ◽  
Flow Rate ◽  
Major Ions ◽  
Rate Of Change ◽  
Data Set ◽  
Input Variables ◽  
Relationship Of ◽  
The Relationship

&lt;p&gt;The work reported here builds upon a previous pilot study by the author on ANN-enhanced flow rating (Schmid, 2020), which explored the use of electrical conductivity (EC) in addition to stage to obtain &amp;#8216;better&amp;#8217;, i.e. more accurate and robust, estimates of streamflow. The inclusion of EC has an advantage, when the relationship of EC versus flow rate is not chemostatic in character. In the majority of cases, EC is, indeed, not chemostatic, but tends to decrease with increasing discharge (so-called dilution behaviour), as reported by e.g. Moatar et al. (2017), Weijs et al. (2013) and Tunqui Neira et al.(2020). This is also in line with this author&amp;#8217;s experience.&lt;/p&gt;&lt;p&gt;The research presented here takes the neural network based approach one major step further and incorporates the temporal rate of change in stage and the direction of change in EC among the input variables (which, thus, comprise stage, EC, change in stage and direction of change in EC). Consequently, there are now 4 input variables in total employed as predictors of flow rate. Information on the temporal changes in both flow rate and EC helps the Artificial Neural Network (ANN) characterize hysteretic behaviour, with EC assuming different values for falling and rising flow rate, respectively, as described, for instance, by Singley et al. (2017).&lt;/p&gt;&lt;p&gt;The ANN employed is of the Multilayer Perceptron (MLP) type, with stage, EC, change in stage and direction of change in EC of the M&amp;#246;dling data set (Schmid, 2020) as input variables. Summarising the stream characteristics, the M&amp;#246;dling brook can be described as a small Austrian stream with a catchment of fairly mixed composition (forests, agricultural and urbanized areas). The relationship of EC versus flow reflects dilution behaviour. Neural network configuration 4-5-1 (the 4 input variables mentioned above, 5 hidden nodes and discharge as the single output) with learning rate 0.05 and momentum 0.15 was found to perform best, with testing average RMSE (root mean square error) of the scaled output after 100,000 epochs amounting to 0.0138 as compared to 0.0216 for the (best performing) 2-5-1 MLP with stage and EC as inputs only. &amp;#160;&amp;#160;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References&lt;/p&gt;&lt;p&gt;Moatar, F., Abbott, B.W., Minaudo, C., Curie, F. and Pinay, G.: Elemental properties, hydrology, and biology interact to shape concentration-discharge curves for carbon, nutrients, sediment and major ions. Water Resources Res., 53, 1270-1287, 2017.&lt;/p&gt;&lt;p&gt;Schmid, B.H.: Enhanced flow rating using neural networks with water stage and electrical conductivity as predictors. EGU2020-1804, EGU General Assembly 2020.&lt;/p&gt;&lt;p&gt;Singley, J.G., Wlostowski, A.N., Bergstrom, A.J., Sokol, E.R., Torrens, C.L., Jaros, C., Wilson, C.,E., Hendrickson, P.J. and Gooseff, M.N.: Characterizing hyporheic exchange processes using high-frequency electrical conductivity-discharge relationships on subhourly to interannual timescales. Water Resources Res. 53, 4124-4141, 2017.&lt;/p&gt;&lt;p&gt;Tunqui Neira, J.M., Andr&amp;#233;assian, V., Tallec, G. and Mouchel, J.-M.: A two-sided affine power scaling relationship to represent the concentration-discharge relationship. Hydrol. Earth Syst. Sci. 24, 1823-1830, 2020.&lt;/p&gt;&lt;p&gt;Weijs, S.V., Mutzner, R. and Parlange, M.B.: Could electrical conductivity replace water level in rating curves for alpine streams? Water Resources Research 49, 343-351, 2013.&lt;/p&gt;

Correlation between Curing Temperature and the Properties of Conductive Composites

2013 ◽  
Vol 750-752 ◽  
pp. 119-122 ◽  
Author(s):  
Xiao Ya Wang ◽  
Zhi Dong Xia ◽  
Zhe Li
Keyword(s):  
Mechanical Properties ◽  
Electrical Conductivity ◽  
Crosslink Density ◽  
Fracture Morphology ◽  
Curing Temperature ◽  
Tensile Fracture ◽  
Elongation At Break ◽  
Conductive Composites ◽  
The Stability ◽  
Better Than

This study was carried out to discuss the influence of curing temperature on the performance of conductive composites filled with nickel-coated graphite (NCG). The electrical conductivity, crosslink density, mechanical properties and tensile fracture morphology have been investigated. The results indicated that curing temperature had great impact on the electrical conductivity and mechanical properties. Voluem resistivity decreased from 43.1 to 0.08 ohm-cm at 125°C-205°C, and the reason was discussed in light of formation and break of the conductive network in the composites. The stability of SR-NCG cured at 165°C-205°C were also better than those cured at other curing temperature. Besides, tensile strength increased from 2.41 to 7.19Mpa at 125°C-225°C, elongation at break have a 56% increase, and Shore A hardness also incresed from 74 to 82.

Experimental Investigations of Ni Nanoparticle-Polyurethane Acrylic Composite for Electrical Conductivity Enhancement

MRS Advances ◽  
2019 ◽  
Vol 4 (43) ◽  
pp. 2337-2344
Author(s):  
Adrian Goodwin ◽  
Ajit D. Kelkar ◽  
Ram V. Mohan
Keyword(s):  
Electrical Conductivity ◽  
Nickel Nanoparticles ◽  
Surface Concentration ◽  
Nano Particles ◽  
Experimental Investigations ◽  
Conductivity Measurements ◽  
Conductivity Enhancement ◽  
Conductive Composites ◽  
Maximum Conductivity ◽  
Number Of Particles

ABSTRACTConductive composites are being considered for use in applications such as electromagnetic shielding. Prior work has shown correlation of electrical conductivity to the microstructure of corresponding composite. In the present paper, composites consisting of polyurethane acrylic and dispersed nickel nanoparticles were fabricated, and tested for their electrical conductivity. In the fabrication process, half of the suspensions were agitated by sonication and half were not. Correlations between electrical conductivity and composite microstructural details are presented. These correlations show an optimum concentration of nickel nanoparticles that result in maximum conductivity enhancement. In addition, sonicating the suspensions increased conductivity of resulting nanocomposites. Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS) images were used to estimate surface concentration and distribution of Nickel nanoparticles, and were correlated to electrical conductivity measurements. Parameters such as number of particles in contact and junction distance between the nano particles in the composites are suggested as a way of enhancing electrical conductivity.

scholarly journals Segregated network polymer/carbon nanotubes composites

2004 ◽  
Vol 2 (2) ◽  
pp. 363-370 ◽  
Author(s):  
A. Mierczynska ◽  
J. Friedrich ◽  
H. Maneck ◽  
G. Boiteux ◽  
J. Jeszka
Keyword(s):  
Carbon Nanotubes ◽  
Electrical Conductivity ◽  
Carbon Nanotube ◽  
Percolation Threshold ◽  
Network Formation ◽  
Processing Parameters ◽  
Low Grade ◽  
Single Walled Carbon ◽  
Conductive Composites ◽  
Grade Material

AbstractIn this work we present the preparation of conductive polyethylene/carbon nanotube composites based on the segregated network concept. Attention has been focused on the effect of decreasing the amount of filler necessary to achieve low resistivity. Using high- and low-grade single-walled carbon nanotube materials we obtained conductive composites with a low percolation threshold of 0.5 wt.% for high-grade nanotubes, about 1 wt% for commercial nanotubes and 1.5 wt% for low-grade material. The higher percolation threshold for low-grade material is related to low effectiveness of other carbon fractions in the network formation. The electrical conductivity was measured as a function of the single-walled carbon nanotubes content in the polymer matrix and as a function of temperature. It was also found that processing parameters significantly influenced the electrical conductivity of the composites. Raman spectroscopy was applied to study single wall nanotubes in the conductive composites.

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