In the recent decade, injection of nanoparticles (NPs) into underground formation as liquid nanodispersions has been suggested as a smart alternative for conventional methods in tertiary oil recovery projects from mature oil reservoirs. Such reservoirs, however, are strong candidates for carbon geo-sequestration (CGS) projects, and the presence of nanoparticles (NPs) after nanofluid-flooding can add more complexity to carbon geo-storage projects. Despite studies investigating CO2 injection and nanofluid-flooding for EOR projects, no information was reported about the potential synergistic effects of CO2 and NPs on enhanced oil recovery (EOR) and CGS concerning the interfacial tension (γ) of CO2-oil system. This study thus extensively investigates the effect of silica NPs on the γ of CO2/decane system at elevated pressure and temperature to recognise the potential impact of NPs-injection on the future CGS projects. To achieve this, a wide-ranging series of tests have been conducted to reveal the role of hydrophilic and hydrophobic silica NPs on γ of the CO2/oil system. n-decane was utilized as model oil and different amounts of NPs were mixed with the oil phase. Oil-NPs dispersions were formulated using an ultrasonic homogenizer. The γ of the CO2/oil system was measured at different pressures (0.1 to 20 MPa) and temperatures (25 to 70 °C) using a high-pressure temperature optical cell. The γ data were measured using the pendant drop technique via axisymmetric drop shape analysis (ADSA). The results showed that, generally, CO2/oil γ subjected mainly to pressure, temperature, and with less extent to NPs load in the oil phase. γ decreases with increased pressure until reaching a plateau where no more significant decrease in γ was observed. The γ trend with increased temperature, on the other hand, was more completed. No significant impact of temperature on γ was recorded with low pressure (≤ 5 MPa). Similarly, at relatively high pressure (≥ 25 MPa), only a slight variation of IFT with temperature change was recorded. However, for the pressure range from 5 – 25 MPa, IFT was increased remarkably with temperature. Furthermore, NPs in the oil phase exhibit a remarkable influence on IFT. In this context, the presence of hydrophilic silica NPs in the oil phase can significantly reduce the γ of the CO2/decane system. However, hydrophobic silica NPs showed less influence on IFT reduction. The outcomes of this work afford good understandings into applications of NP for EOR and CGS applications and help to de-risk CO2-geological storage projects.
During 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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In this work, multilayer nanostructures were prepared from two metal oxide thin films by dc reactive magnetron sputtering technique. These metal oxide were nickel oxide (NiO) and titanium dioxide (TiO2). The prepared nanostructures showed high structural purity as confirmed by the spectroscopic and structural characterization tests, mainly FTIR, XRD and EDX. This feature may be attributed to the fine control of operation parameters of dc reactive magnetron sputtering system as well as the preparation conditions using the same system. The nanostructures prepared in this work can be successfully used for the fabrication of nanodevices for photonics and optoelectronics requiring highly-pure nanomaterials.
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Verbal and omnipotent differences in the oud of conscience, which are comprehensive reasons for language in all
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Video streaming is widely available nowadays. Moreover, since the pandemic hit all across the globe, many people stayed home and used streaming services for news, education, and entertainment. However, when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices. Users are often irritated by unpredictable video quality format displays on their smartphone devices. In this paper, we proposed a framework video selection scheme that targets to increase QoE user satisfaction. We used a video content selection algorithm to map the video selection that satisfies the user the most regarding streaming quality. Video Content Selection (VCS) are classified in
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This paper presents the non-linear finite element method to study the behavior of four reinforced rectangular concrete MD beams with web circular openings tested under two-point load. The numerical finite elements methods have been used in a much more practical way to achieve approximate solutions for more complex problems. The ABAQUS /CAE is chosen to explore the behavior of MD beams. This paper also studies, the effect of both size and shape of the circular apertures of MD beams. The strengthening technique that used in this paper is externally strengthening using CFRP around the opening in the MD beams. The numerical results were compared to the experimental results in terms of ultimate load failure and displace
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