Mohamed Khalid AlOmar

Novel deep eutectic solvent-functionalized carbon nanotubes adsorbent for mercury removal from water

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 Hg2+ 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, Hg2+ adsorption conditions were optimized using response surface methodology (RSM). A pseudo-second order model accurately described the adsorption of Hg2+. The Langmuir and Freundlich isotherm models described the absorption of Hg2+ on the novel adsorbent with acceptable accuracy. The maximum adsorption capacity was found to be 177.76mg/g.

Optimization of the Synthesis of Superhydrophobic Carbon Nanomaterials by Chemical Vapor Deposition

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.

The modeling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach

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.

Computational investigation of the microstructural characteristics and physical properties of glycerol-based deep eutectic solvents

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.