scholarly journals Electrically Conductive Membranes Obtained by Simultaneous Electrospinning and Electrospraying Processes

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Martina Roso ◽  
Alessandra Lorenzetti ◽  
Carlo Boaretti ◽  
Michele Modesti
Keyword(s):  
Electrostatic Discharge ◽  
Multiwall Carbon Nanotubes ◽  
Volume Resistivity ◽  
Image Texture ◽  
Electrically Conductive ◽  
Image Texture Analysis ◽  
Analysis Techniques ◽  
Conductive Membranes ◽  
Nanostructured Membranes ◽  
Multiwall Carbon

Electrically conductive polyurethane nanostructured membranes have been prepared combining the electrospinning of polymer nanofibers (NFs) with the electrospraying of pristine multiwall carbon nanotubes (MWCNTs) in simultaneous processes. In order to have a better understanding of the distribution of MWCNTs on the surface of the membranes, the optimization of the electrospraying process has been carried out and the distribution of MWCNTs has been evaluated using image texture analysis techniques. Large membranes with a volume resistivity typical of electrostatic discharge materials with a MWCNTs concentration less than 0.3% wt (0.01 mg/cm2) have been obtained and characterized with morphological (SEM and TEM) and spectroscopic (UV-Vis, Raman) techniques.

scholarly journals Electrically Conductive Electrospun Polymeric Mats for Sensing Dispersed Vegetable Oil Impurities in Wastewater

Processes ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 906 ◽  
Author(s):  
Abdolali Moghaddasi ◽  
Patrik Sobolčiak ◽  
Anton Popelka ◽  
Kishor Kumar Sadasivuni ◽  
Zdeno Spitalsky ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Electrical Resistivity ◽  
Vegetable Oil ◽  
Multiwall Carbon Nanotubes ◽  
Dimethyl Formamide ◽  
Gold Electrodes ◽  
Active Sensing ◽  
Electrically Conductive ◽  
Styrene Copolymer ◽  
Multiwall Carbon

This paper addresses the preparation of electrically conductive electrospun mats on a base of styrene-isoprene-styrene copolymer (SIS) and multiwall carbon nanotubes (CNTs) and their application as active sensing elements for the detection of vegetable oil impurities dispersed within water. The most uniform mats without beads were prepared using tetrahydrofuran (THF)/dimethyl formamide (DMF) 80:20 (v/v) as the solvent and 13 wt.% of SIS. The CNT content was 10 wt.%, which had the most pronounced changes in electrical resistivity upon sorption of the oil component. The sensors were prepared by deposition of the SIS/CNT layer onto gold electrodes through electrospinning and applied for sensing of oil dispersed in water for 50, 100, and 1000 ppm.

PIO 7405 Efficacy of image texture analysis techniques in the classification of ultrasonic liver images

1997 ◽  
Vol 23 ◽  
pp. S135 ◽  
Author(s):  
P.S. Zoumpoulis ◽  
I.D. Theotokas ◽  
D. Floros ◽  
S.A. Pavlopoulos ◽  
E.K. Kyricou ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Image Texture ◽  
Image Texture Analysis ◽  
Analysis Techniques

scholarly journals CHARACTERIZATION OF HIPPOCAMPUS ON EPILEPTIC PATIENTS ON MRI USING TEXTURE ANALYSIS TECHNIQUES

2021 ◽  
Vol 9 (1) ◽  
pp. 164-168
Author(s):  
Tasneem Abdulrazig Mohamed Sayed ◽  
Fatima Yousif Mohammed ◽  
Maha Esmeal Ahmed
Keyword(s):  
Texture Analysis ◽  
Brain Mri ◽  
Score Function ◽  
Image Texture ◽  
Mr Images ◽  
Image Texture Analysis ◽  
Analysis Techniques ◽  
Classification Score ◽  
Epileptic Patients

The aim of this study was to characterize the hippocampus in Sudanese epileptic patients in MR images using image texture analysis techniques in order to differentiate hippocampus between the normal and epileptic patient. There were two groups of the patients were examined by using Signal-GE 1.5Tesla MR Scanner which was used with patients with known epilepsy and normal T1 weighted brain. MRI finding patients, 101 and 105 patients respectively examined in period from December 2017- March 2018, where the variables of the study were MRI images entered to the IDL program as input for further analysis, using window 3*3 the images texture was extracted from hippocampus (head, body and tail) that include, mean, STD, variance, energy, and entropy then the comparison was made to differentiate between the normal and abnormal hippocampus. The extracted feature classified using linear discriminate analysis. The classification score function is used to classify the hippocampus classes was as flows: Epileptic= (.271×mean) + (.026×variance) + (7.475× Part) -32.134 Normal= (.240×mean) + (.052×variance) + (2.960× Part) -13.684 The study confirmed that it’s possible to differentiate between normal and epileptic hippocampus body, head, and tail in sagittal section texturally. The result showed that the classification result is best in the tail where higher classification accuracy will be achieved followed by body and then head.

Effect of Low-Content of Graphene and Carbon Nanotubes on Dielectric Properties of Polyethylene Oxide Solid Composite Electrolyte

2016 ◽  
Vol 721 ◽  
pp. 18-22 ◽  
Author(s):  
Aleksandra Jurkane ◽  
Sergejs Gaidukov
Keyword(s):  
Carbon Nanotubes ◽  
Polyethylene Oxide ◽  
Multiwall Carbon Nanotubes ◽  
Lithium Triflate ◽  
Composite Electrolyte ◽  
Electrically Conductive ◽  
Pressing Method ◽  
Dielectric Characteristics ◽  
Controlled Structure ◽  
Multiwall Carbon

Preparation of polymeric nano-composites with finely controlled structure, especially, at nano-scale, is still one of the most perspective modification ways of the properties of polymeric composites. Paper actuality is based on growing need for non-combustible and safe battery electrolytes, which operate portable electronic devices. Polyethylene oxide solid composite electrolytes containing lithium triflate, multiwall carbon nanotubes and graphene by solution casting and hot-pressing method were prepared. Dielectric spectroscopy, surface resistivity measurements were performed to evaluate nanoparticles influence on the dielectric characteristics of the electrolyte material. Observed enhancement of dielectric conductivity is connected to the addition of the Li+ ions and incorporation of the electrically conductive nanoparticles to the polymer electrolyte

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