25 research outputs found
New electrospun polystyrene/Al2O3 nanocomposite superhydrophobic coatings; Synthesis, characterization, and application
The effect of electrospinning operational parameters on the morphology, surface roughness, and wettability of different compositions of electrospun polystyrene (PS)-aluminum oxide (Al2O3) nanocomposite coatings was investigated using different techniques. For example, a scanning electron microscope (SEM) coupled with an energy dispersive X-ray (EDX) unit, a Fourier transform infrared (FTIR) spectrometer, an atomic force microscope (AFM), and water contact angle (WCA), and contact angle hysteresis (CAH) measurements using the sessile droplet method, were used. The latter used 4 μL of distilled water at room temperature. PS/Al2O3 nanocomposite coatings exhibited different morphologies, such as beaded fibers and microfibers, depending on the concentration ratio between the PS and Al2O3 nanoparticles and the operational parameters of the electrospinning process. The optimum conditions to produce a nanocomposite coating with the highest roughness and superhydrophobic properties (155° ± 1.9° for WCA and 3° ± 4.2° for CAH) are 2.5 and 0.25 wt % of PS and Al2O3, respectively, 25 kV for the applied potential and 1.5 mL·h-1 for the solution flow rate at 35 °C. The corrosion resistance of the as-prepared coatings was investigated using the electrochemical impedance spectroscopy (EIS) technique. The results have revealed that the highly porous superhydrophobic nanocomposite coatings (SHCs) possess a superior corrosion resistance that is higher than the uncoated Al alloy by three orders of magnitude. © 2018 by the authors.Acknowledgments: This publication was supported by Qatar University Internal Grant No. GCC-2017-012. The findings achieved herein are solely the responsibility of the authors. Thanks are also due to Anton Popelka for assistance with the AFM analysis.Scopu
Recycled polyethylene/paraffin wax/expanded graphite based heat absorbers for thermal energy storage: An artificial aging study
An artificial aging study of novel heat absorbers based on phase change materials (PCMs) prepared from recycled high-density polyethylene (HDPE), paraffin wax (PW), and expanded graphite (EG) was investigated. The optimal composition of PCMs contained 40 wt% HDPE, whereas the paraffin wax content ranged from 40 to 60 wt% and the expanded graphite content ranged from 5 to 15 wt%. PCMs were artificially aged through exposure to UV irradiation, enhanced temperature, and humidity. It was clearly demonstrated that the addition of EG to PCMs led to the suppression of PW leakage and improved the photooxidation stability of the PCMs during the aging process. The best performance was achieved by adding 15 wt% of EG to the PCMs. The sample shows a leakage of paraffin wax below 10%, retaining a melting enthalpy of PW within PCMs of 54.8 J/g, a thermal conductivity of 1.64 W/mK and the lowest photooxidation, characterized by an increase in the concentration of carbonyl groups from all investigated materials after artificial aging. Furthermore, PCMs mixed with EG exhibited good mechanical properties, even after 100 days of exposure to artificial aging. Finally, this work demonstrates a justification for the use of recycled plastics in the formation of PCMs.This research was funded by an NPRP grant No: NPRP10-0205-170349 from the Qatar National Research Fund (a member of the Qatar Foundation)
Some theoretical aspects of tertiary treatment of water/oil emulsions by adsorption and coalescence mechanisms: A review
The massive increase in the volumes of oily contaminated produced waters associated with various industrial sectors has initiated considerable technological and scientific efforts related to the development of new cleaning strategies. The petrochemical industry (oil and gas production and processing) contributes to those volumes by approximately 340 billion barrels per year. The removal of emulsified oily components is a matter of particular interest because the high emulsion stability necessitates sophisticated technological approaches as well as a deep theoretical understanding of key mechanisms of oil/water separation. This review deals with the theoretical aspects of the treatment of emulsified oil/water mixtures and is particularly focused on tertiary treatment, which means the reduction of the oil content from 70-100 ppm to below 10 ppm, depending on national regulations for water discharge. The review concerns the mechanisms of oil/water separation and it covers the (i) adsorption isotherms, (ii) kinetics of adsorption, (iii) interfacial interactions between oil/water mixtures and solid surfaces, and (iv) oil/water separation techniques based on the wettability of solid/oil/water interfaces. The advantages and drawbacks of commonly used as well as newly proposed kinetic and adsorption models are reviewed, and their applicability for the characterization of oil/water separation is discussed. The lack of suitable adsorption isotherms that can be correctly applied for a description of oil adsorption at external and internal solid surfaces of both nonporous and porous structures is pointed out. The direct using of common isotherms, which were originally developed for gas adsorption, often leads to the incorrect data description because the adsorption of oily components at solid surfaces does not fit the assumptions from which these models were originally derived. Particularly, it results in problematic calculations of the thermodynamic parameters of sorption. The importance of nonlinear analysis of data is discussed, since recent studies have indicated that the error structure of experimental data is usually changed if the original nonlinear adsorption isotherms are transformed into their linearized forms. The comparison between the pseudo-first-order and pseudo-second-order kinetic models was performed. It was shown that the correlation between data and models strongly depends on the selection of data, particularly on the frequency of collected data in time scale. The wettability of solid surfaces by oil in air and under water is discussed, regarding the surface morphology of surfaces. We demonstrate that the combination of surface chemistry and topology strongly influences the separation of oil/water emulsions.This work was made possible by a grant from the Qatar National Research Fund under its National Priorities Research Program (award number NPRP12S-0311-190299) and by financial support from the ConocoPhillips Global Water Sustainability Center (GWSC). The paper?s content is solely the responsibility of the authors and does not necessarily represent the official views of the Qatar National Research Fund or ConocoPhillips. This research was also funded by Qatar University through Qatar University Collaborative Grant QUCGCAM- 20/21-4.Scopu
A novel Conceptual Machine Learning Method using Random Conceptual Decomposition
Formal Concept Analysis (FCA) is emerging in Data Science because of its generality, simplicity, and powerful mathematical foundation. It enabled a uniform data clustering methods into structured space of formal concepts. Several FCA based machine learning (ML) methods gave competitive results compared to classical methods. In another side, ensemble approach proved to be effective by aggregating different basic ML methods. Randomness improved other ML approaches. In this paper, we propose a new conceptual ML method by using random conceptual decomposition. This method integrated and experimented in the context of ensemble learning methods, gave encouraging good results, in general.ACKNOWLEDGMENT This contribution was made possible by NPRP grant #07-794-1-145 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu
Graphene oxide as antimicrobial against two gram-positive and two gram-negative bacteria in addition to one fungus
Graphene based materials have wide potential applications in biology, biomedical, agriculture environmental and biotechnology. Graphene Oxide (GO) is one of those materials and has a promising substance as antimicrobial agents. GO in this study was prepared by a modified Hummers method and was characterized by different techniques for confirmation of formation of GO. To study the antimicrobial activities of GO, it was tested against these microorganisms, one eukaryotic fungus (Candida albicans, C. albicans) two Gram negative bacteria (Escherichia coli (E. coli) ATCC 41570 and Pseudomonas aeruginosa (P. aeruginosa) ATCC 25619) and two Gram positive bacteria (Streptococcus faecalis (S. faecalis) ATCC 19433 and Staphylococcus aureus (S. aureus) ATCC 11632). Anti-microbial activity of GO was detected by spectrophotometer as indirect method to measure the growth and viable cell count as direct method. Readings were taken at successive incubated times. Results revealed that GO has antibacterial and anti-fungal activity against microorganisms used in this study. In conculosion the developed GO exhibit excellent antimicrobial property and GO affects more on Gram positive bacteria than Gram negative bacteria and fungi.Scopu
Effects of aniline concentrations on the electrical and mechanical properties of polyaniline polyvinyl alcohol blends
AbstractIn this work, we present an exclusive study on the effect of the feeding ratio of the monomer (aniline) on the structural, thermal, mechanical and electrical properties of polyaniline (PANI) polyvinyl alcohol (PVA) blends. The films obtained from the blends are characterised to determine their surface properties and structural morphology (elemental analysis, SEM and FTIR), thermal properties (TGA and DSC) and optical properties (UV–Vis spectroscopy). We study the effects of aniline on the mechanical and electrical properties of the composites by performing tensile, four probe and A.C. conductivity measurements, respectively. The SEM images reveal a heterogeneous distribution of conductive PANI particles in the continuous PVA matrix. During this experiment, the tensile strength of the blend films is maintained with an increase in the amount of aniline (up to 25wt%), and this behaviour is attributed to intermolecular hydrogen bonding between PANI and PVA in the presence of the surfactant DBSA. The potential attraction of the experiment lies in the nature of the conductivity (of the blend films), which is found to increase from 10−8 to 10−3S/cm with a percolation threshold of 0.78wt%
A Deep Learning Based Automatic Severity Detector for Diabetic Retinopathy
Automated Diabetic Retinopathy (DR) screening methods with high accuracy have the strong potential to assist doctors in evaluating more patients and quickly routing those who need help to a specialist. In this work, we used Deep Convolutional Neural Network architecture to diagnosing DR from digital fundus images and accurately classifying its severity. We train this network using a graphics processor unit (GPU) on the publicly available Kaggle dataset. We used Theano, Lasagne, and cuDNN libraries on two Amazon EC2 p2.xlarge instances and demonstrated impressive results, particularly for a high-level classification task. On the dataset of 30,262 training images and 4864 testing images, our model achieves an accuracy of 72%. Our experimental results showed that increasing the batch size does not necessarily speed up the convergence of the gradient computations. Also, it demonstrated that the number and size of fully connected layers do not have a significant impact on the performance of the model.This work was supported by Sidra Medicine (authors RA and SB), as well as a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 6-249-1-053 (authors SA and MA).Scopu
Processing, characterization and modeling of recycled polypropylene/glass fibre/wood flour composites
Polypropylene (PP) is one of the most common thermoplastic materials in the world. There is a need to recycle the large amount of this used material. To overcome the environmental problems, related to the polymer waste, PP was recycled and used as a matrix material in different composites that can be used in high value applications. In this paper, composites made of recycled polypropylene (RPP) reinforced by glass fibres and/or wood flour of the palm tree were prepared, characterized and modeled. The mechanical and thermal properties of these recycled polymer matrix composites (RPMCs) were measured experimentally and modeled theoretically. The mechanical properties included tensile modulus, tensile strength and hardness, whereas thermal properties included thermal stability, melting and crystallinity percentage content were studied. In addition we applied the functionally graded materials concept, the elastic finite element analysis of a layered functionally graded pressurized pipe, which is one of the practical industrial applications, was accomplished in order to have some insight on the performance of such RPMCs. The results reveal that the desired mechanical and thermal properties met the requirements of a wide range of practical applications which can be attained by adding the considered fillers. Also, the proper selection of the layers of the pressurized pipe, which was made of RPMCs, led to decrease of the induced stresses and accordingly increased the operational safety.Qatar Science and Technology Park (QSTP), Center for Advanced MaterialsScopu
Round-Robin sequential forward selection algorithm for prostate cancer classification and diagnosis using multispectral imagery
This paper proposes an automatic classification system for the use in prostate cancer diagnosis. The system aims to detect and classify prostatic tissue textures captured from microscopic samples taken from needle biopsies. Biopsies are usually analyzed by a trained pathologist with different grades of malignancy typically corresponding to different structural patterns as well as apparent textures. In the context of prostate cancer diagnosis, four major groups have to be accurately recognized: stroma, benign prostatic hyperplasia, prostatic intraepithelial neoplasia, and prostatic carcinoma. Recently, multispectral imagery has been proposed as a new image acquisition modality which unlike conventional RGB-based light microscopy allows the acquisition of a large number of spectral bands within the visible spectrum, resulting in a large feature vector size. Many features in the initial feature set are irrelevant to the classification task and are correlated with each other, resulting in an increase in the computational complexity and a reduction in the recognition rate. In this paper, a Round-Robin (RR) sequential forward selection RR-SFS is used to address these problems. RR is a technique for handling multi-class problems with binary classifiers by training one classifier for each pair of classes. The experimental results demonstrate this finding when compared with classical method based on the multiclass SFS and other ensemble methods such as bagging/boosting with decision tree (C4.5) classifier where it is shown that RR-SFS method achieves the best results with a classification accuracy of 99.9%.Scopu