This project sought to fabricate a flexible gas sensor based on a short functionalized multi-walled carbon nanotubes (f-MWCNTs) network for nitrogen dioxide gas detection. The network was prepared by filtration from the suspension (FFS) method and modified by coating with a layer of polypyrrole conductive polymer (PPy) prepared by the oxidative chemical polymerization to improve the properties of the network. The structural, optical, and morphological properties of the f-MWCNTs and f-MWCNTs/PPy network were studied using X-ray diffraction (XRD), Fourie-transform infrared (FTIR), with an AFM (atomic force microscopy). XRD proved that the structure of f-MWCNTs is unaffected by the synthesis procedure. The FTIR spectra verified the existence of the functional groups and bonding for the used materials. AFM images reflect coating the network with conductive polymer on the surface parameters and granularity distribution. The sensitivity of the fabricated sensor was measured after exposure the network to 𝑁𝑂2 gas at concentrations of 20 ppm with different operating temperatures using a homemade gas sensor system. The fabricated sensor works at room temperature with a sensitivity of about 56.17% while coating the sensor surface with conductive polymer improves the sensitivity at all operating temperatures.
One of the most important parameters determining structural members' durability and strength is the fire flame's influence and hazard. Some engineers have advocated using advanced analytical models to predict fire spread impact within a compartment and considering finite element models of structural components to estimate the temperatures within a component using heat transfer analysis. This paper presented a numerical simulation for a reinforced concrete beam’s structural response in a case containing Water Absorbing Polymer Spheres (WAPS) subjected to fire flame effect. The commercial finite element package ABAQUS was considered. The relevant geometrical and material parameters of the reinforced concrete beam model at elevated t
... Show MoreOne of the most important parameters determining structural members' durability and strength is the fire flame's influence and hazard. Some engineers have advocated using advanced analytical models to predict fire spread impact within a compartment and considering finite element models of structural components to estimate the temperatures within a component using heat transfer analysis. This paper presented a numerical simulation for a reinforced concrete beam’s structural response in a case containing Water Absorbing Polymer Spheres (WAPS) subjected to fire flame effect. The commercial finite element package ABAQUS was considered. The relevant geometrical and material parameters of the reinforced concrete beam model at elevated t
... Show MoreOne of the most important parameters determining structural members' durability and strength is the fire flame's influence and hazard. Some engineers have advocated using advanced analytical models to predict fire spread impact within a compartment and considering finite element models of structural components to estimate the temperatures within a component using heat transfer analysis. This paper presented a numerical simulation for a reinforced concrete beam’s structural response in a case containing Water Absorbing Polymer Spheres (WAPS) subjected to fire flame effect. The commercial finite element package ABAQUS was considered. The relevant geometrical and material parameters of the reinforced concrete beam model a
... Show MoreWater quality sensors have recently received a lot of attention due to their impact on human health. Due to their distinct features, environmental sensors are based on carbon quantum dots (CQDs). In this study, CQDs were prepared using the electro-chemical method, where the structural and optical properties were studied. These quantum dots were used in the environmental sensor application after mixing them with three different materials: CQDs, Alq3 polymer and CQDs and Alq3 solutions using two different methods: drop casting and spin coating, and depositing them on silicon. The sensitivity of the water pollutants was studied for each case of the prepared samples after measuring the change in resistance of the samples at a temperature of
... Show MoreHalf of the oil production of the worldwide is a result of the water flooding project. But the main concern of this process is mobility control of the injected fluid, because the unfavorable mobility ratio leads to fingering effect. Adding polymer to the injection water increase the water viscosity, therefore, the displacement will be more stable and have a greater sweep efficiency.
Using of polymer flooding has received more attention these days. Polymer has great potential in the Middle East region, especially in reservoir with high temperature and salinity.
The main objective of this work is to show the effect of shear rate, salinity, temperature, polymer concentration on polymer v
... Show MoreSynthetic polymers such as polyurethane are used widely in the field of biomedical applications such as implants or part of implant systems.
This paper focuses on the preparation of base polymer matrix composite materials by (Hand Lay-Up) method, and studying the effect of selected grain size (32, 53, 63, 75, and 90) µm of (Reenia) particles on some properties of the prepared composite.
Mechanical tests were used to evaluate the prepared system (Tensile, Compression, Impact, and Hardness) tests, and a physical test of (Water absorption %), and all tests were accomplished at room temperature.
Where results showed tensile test (maximum tensile strength and modulus of elasticity) high at small grain size while
... Show MoreDuring COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
... Show MoreDuring COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
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