Displaying all 11 publications

Abstract:
Sort:
  1. Dehyab AS, Bakar MFA, AlOmar MK, Sabran SF
    Saudi J Biol Sci, 2020 Sep;27(9):2457-2478.
    PMID: 32884430 DOI: 10.1016/j.sjbs.2020.07.007
    Tuberculosis (TB) is a disease that affects one-third of the world's population. Although currently available TB drugs have many side effects, such as nausea, headache and gastrointestinal discomfort, no new anti-TB drugs have been produced in the past 30 years. Therefore, the discovery of a new anti-TB agent with minimal or no side effects is urgently needed. Many previous works have reported the effects of medicinal plants against Mycobacterium tuberculosis (MTB). However, none have focused on medicinal plants from the Middle Eastern and North African (MENA) region. This review highlights the effects of medicinal plants from the MENA region on TB. Medicinal plants from the MENA region have been successfully used as traditional medicine and first aid against TB related problems. A total of 184 plants species representing 73 families were studied. Amongst these species, 93 species contained more active compounds with strong anti-MTB activity (crude extracts and/or bioactive compounds with activities of 0-100 µg/ml). The extract of Inula helenium, Khaya senegalensis, Premna odorata and Rosmarinus officinalis presented the strongest anti-MTB activity. In addition, Boswellia papyrifera (Del) Hochst olibanum, Eucalyptus camaldulensis Dehnh leaves (river red gum), Nigella sativa (black cumin) seeds and genus Cymbopogon exhibited anti-TB activity. The most potent bioactive compounds included alantolactone, octyl acetate, 1,8-cineole, thymoquinone, piperitone, α- verbenol, citral b and α-pinene. These compounds affect the permeability of microbial plasma membranes, thus kill the mycobacterium spp. As a conclusion, plant species collected from the MENA region are potential sources of novel drugs against TB.
  2. Aissaoui T, Benguerba Y, AlOmar MK, AlNashef IM
    J Mol Model, 2017 Sep 14;23(10):277.
    PMID: 28913646 DOI: 10.1007/s00894-017-3450-5
    Recently, there has been significant interest in the possibility of using deep eutectic solvents (DESs) as novel green media and alternatives to conventional solvents and ionic liquids (ILs) in many applications. Due to their attractive properties, such as their biodegradability, low cost, easy preparation, and nontoxicity, DESs appear to be very promising solvents for use in the field of green chemistry. This computational study investigated six glycerol-based DESs: DES1(glycerol:methyl triphenyl phosphonium bromide), DES2(glycerol:benzyl triphenyl phosphonium chloride), DES3(glycerol:allyl triphenyl phosphonium bromide), DES4(glycerol:choline chloride), DES5(glycerol:N,N-diethylethanolammonium chloride), and DES6(glycerol:tetra-n-butylammonium bromide). The chemical structures and combination mechanisms as well as the sigma profiles and sigma potentials of the studied DESs were explored in detail. Moreover, density, viscosity, vapor pressure, and IR analytical data were predicted and compared with the corresponding experimental values reported in the literature for these DESs. To achieve these goals, the conductor-like screening model for realistic solvents (COSMO-RS) and the Amsterdam Density Functional (ADF) software package were used. The predicted results were found to be in good agreement with the corresponding experimental values reported in the literature. Further theoretical investigations are needed to confirm the experimental results-regarding both properties and applications-reported for these DESs.
  3. Alayan HM, Alsaadi MA, AlOmar MK, Hashim MA
    Environ Technol, 2019 Jul;40(18):2400-2415.
    PMID: 29451094 DOI: 10.1080/09593330.2018.1441911
    This work demonstrated the synthesis of carbon nanotubes (CNTs) on powder activated carbon (PAC) impregnated with Ni-catalyst through chemical vapour deposition. The optimized effects of reaction temperature, time and feedstock flow rates on CNT growth were examined. Potassium permanganate (KMnO4) and potassium permanganate in acidic solution (KMnO4/H2SO4) were used to functionalize CNTs samples. A primary screening of methylene blue (MB) adsorption was conducted. The chemical, physical and morphological properties of the adsorbent with the highest removal efficiency were investigated using FESEM, EDX, TEM, BET surface area, RAMAN, TGA, FTIR, and zeta potential. The resulting carbon nanotube-loaded activated carbons possessed abundant pore structure and large surface area. The MB removal by the as-synthesized CNTs was more remarkable than that by the modified samples. Adsorption studies were carried out to evaluate the optimum conditions, kinetics and isotherms for MB adsorption process. The response surface methodology-central composite design (RSM-CCD) was used to optimize the adsorption process parameters, including pH, adsorbent dosage and contact time. The investigation of the adsorption behaviour demonstrated that the adsorption was well fitted with the pseudo-second-order model and Langmuir isotherm with the maximum monolayer adsorption capacity of 174.5 mg/g. Meanwhile, the adsorption of MB onto adsorbent was driven by the electrostatic attraction and π-π interaction. Moreover, the as-obtained CNT-PAC exhibited good reusability after four repeated operations. In view of these empirical findings, the low-cost CNT-PAC has potential for removal of MB from aqueous solution.
  4. AlOmar MK, Alsaadi MA, Hayyan M, Akib S, Ibrahim M, Hashim MA
    Chemosphere, 2017 Jan;167:44-52.
    PMID: 27710842 DOI: 10.1016/j.chemosphere.2016.09.133
    Recently, deep eutectic solvents (DESs) have shown their new and interesting ability for chemistry through their involvement in variety of applications. This study introduces carbon nanotubes (CNTs) functionalized with DES as a novel adsorbent for Hg(2+) from water. Allyl triphenyl phosphonium bromide (ATPB) was combined with glycerol as the hydrogen bond donor (HBD) to form DES, which can act as a novel CNTs functionalization agent. The novel adsorbent was characterized using Raman, FTIR, XRD, FESEM, EDX, BET surface area, TGA, TEM and Zeta potential. Response surface methodology was used to optimize the removal conditions for Hg(2+). The optimum removal conditions were found to be pH 5.5, contact time 28 min, and an adsorbent dosage of 5 mg. Freundlich isotherm model described the adsorption isotherm of the novel adsorbent, and the maximum adsorption capacity obtained from the experimental data was 186.97 mg g(-1). Pseudo-second order kinetics describes the adsorption rate order.
  5. AlOmar MK, Alsaadi MA, Jassam TM, Akib S, Ali Hashim M
    J Colloid Interface Sci, 2017 07 01;497:413-421.
    PMID: 28314146 DOI: 10.1016/j.jcis.2017.03.014
    Due to the interestingly tolerated physicochemical properties of deep eutectic solvents (DESs), they are currently in the process of becoming widely used in many fields of science. Herein, we present a novel Hg(2+) adsorbent that is based on carbon nanotubes (CNTs) functionalized by DESs. A DES formed from tetra-n-butyl ammonium bromide (TBAB) and glycerol (Gly) was used as a functionalization agent for CNTs. This novel adsorbent was characterized using Raman spectroscopy, Fourier transform infrared (FTIR) spectroscopy, XRD, FESEM, EDX, BET surface area, and Zeta potential. Later, Hg(2+) adsorption conditions were optimized using response surface methodology (RSM). A pseudo-second order model accurately described the adsorption of Hg(2+). The Langmuir and Freundlich isotherm models described the absorption of Hg(2+) on the novel adsorbent with acceptable accuracy. The maximum adsorption capacity was found to be 177.76mg/g.
  6. Alayan HM, Alsaadi MA, Das R, Abo-Hamad A, Ibrahim RK, AlOmar MK, et al.
    Water Sci Technol, 2018 Mar;77(5-6):1714-1723.
    PMID: 29595174 DOI: 10.2166/wst.2018.057
    In this study, carbon species were grown on the surface of Ni-impregnated powder activated carbon to form a novel hybrid carbon nanomaterial by chemical vapor deposition. The carbon nanomaterial was obtained by the precipitation of the methane elemental carbon atoms on the surface of the Ni catalyst. The physiochemical properties of the hybrid material were characterized to illustrate the successful growth of carbon species on the carbon substrate. The response surface methodology was used for the evaluation of adsorption parameters effect such as pH, adsorbent dose and contact time on the percentage removal of MB dye from aqueous solution. The optimum conditions were found to be pH = 11, adsorbent dose = 15 mg and contact time of 120 min. The material we prepared showed excellent removal efficiency of 96% for initial MB concentration of 50 mg/L. The adsorption of MB was described accurately by the pseudo-second-order model with R2 of 0.998 and qe of 163.93 (mg/g). The adsorption system showed the best agreement with Langmuir model with R2 of 0.989 and maximum adsorption capacity (Qm) of 250 mg/g.
  7. Fiyadh SS, AlSaadi MA, AlOmar MK, Fayaed SS, Hama AR, Bee S, et al.
    Water Sci Technol, 2017 Nov;76(9-10):2413-2426.
    PMID: 29144299 DOI: 10.2166/wst.2017.393
    The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb2+. Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R2) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R2 of 0.9956 with MSE of 1.66 × 10-4. The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.
  8. Alomar MK, Khaleel F, Aljumaily MM, Masood A, Razali SFM, AlSaadi MA, et al.
    PLoS One, 2022;17(11):e0277079.
    PMID: 36327280 DOI: 10.1371/journal.pone.0277079
    Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels' U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models' efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies.
  9. Fiyadh SS, AlOmar MK, Binti Jaafar WZ, AlSaadi MA, Fayaed SS, Binti Koting S, et al.
    Int J Mol Sci, 2019 Aug 28;20(17).
    PMID: 31466219 DOI: 10.3390/ijms20174206
    Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 × 10-3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10-3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10-3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.
  10. Aljumaily MM, Alsaadi MA, Das R, Hamid SBA, Hashim NA, AlOmar MK, et al.
    Sci Rep, 2018 02 09;8(1):2778.
    PMID: 29426860 DOI: 10.1038/s41598-018-21051-3
    Demand is increasing for superhydrophobic materials in many applications, such as membrane distillation, separation and special coating technologies. In this study, we report a chemical vapor deposition (CVD) process to fabricate superhydrophobic carbon nanomaterials (CNM) on nickel (Ni)-doped powder activated carbon (PAC). The reaction temperature, reaction time and H2/C2H2 gas ratio were optimized to achieve the optimum contact angle (CA) and carbon yield (CY). For the highest CY (380%) and CA (177°), the optimal reaction temperatures were 702 °C and 687 °C, respectively. However, both the reaction time (40 min) and gas ratio (1.0) were found to have similar effects on CY and CA. Based on the Field emission scanning electron microscopy and transmission electron microscopy images, the CNM could be categorized into two main groups: a) carbon spheres (CS) free carbon nanofibers (CNFs) and b) CS mixed with CNFs, which were formed at 650 and 750 °C, respectively. Raman spectroscopy and thermogravimetric analysis also support this finding. The hydrophobicity of the CNM, expressed by the CA, follows the trend of CS-mixed CNFs (CA: 177°) > CS-free CNFs (CA: 167°) > PAC/Ni (CA: 65°). This paves the way for future applications of synthesized CNM to fabricate water-repellent industrial-grade technologies.
  11. Ismael BH, Khaleel F, Ibrahim SS, Khaleel SR, AlOmar MK, Masood A, et al.
    Membranes (Basel), 2023 Dec 05;13(12).
    PMID: 38132904 DOI: 10.3390/membranes13120900
    Vacuum membrane distillation (VMD) has attracted increasing interest for various applications besides seawater desalination. Experimental testing of membrane technologies such as VMD on a pilot or large scale can be laborious and costly. Machine learning techniques can be a valuable tool for predicting membrane performance on such scales. In this work, a novel hybrid model was developed based on incorporating a spotted hyena optimizer (SHO) with support vector machine (SVR) to predict the flux pressure in VMD. The SVR-SHO hybrid model was validated with experimental data and benchmarked against other machine learning tools such as artificial neural networks (ANNs), classical SVR, and multiple linear regression (MLR). The results show that the SVR-SHO predicted flux pressure with high accuracy with a correlation coefficient (R) of 0.94. However, other models showed a lower prediction accuracy than SVR-SHO with R-values ranging from 0.801 to 0.902. Global sensitivity analysis was applied to interpret the obtained result, revealing that feed temperature was the most influential operating parameter on flux, with a relative importance score of 52.71 compared to 17.69, 17.16, and 14.44 for feed flowrate, vacuum pressure intensity, and feed concentration, respectively.
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links