In the current work, a Gum, Arabic-modified Graphene (GGA), has been synthesized via a facile green method and employed for the first time as an additive for enhancement of the PPSU ultrafiltration membrane properties. A series of PPSU membranes containing very low (0-0.25) wt.% GGA were prepared, and their chemical structure and morphology were comprehensively investigated through atomic force microscopy (AFM), Fourier transforms infrared spectroscopy (FTIR), X-ray diffraction (XRD), and field emission scanning electron microscopy (FESEM). Besides, thermogravimetric analysis (TGA) was harnessed to measure thermal characteristics, while surface hydrophilicity was determined by the contact angle. The PPSU-GGA membrane performance was assessed through volumetric flux, solute flux, and retention of sodium alginate solution as an organic polysaccharide model. Results demonstrated that GGA structure had been successfully synthesized as confirmed XRD patterns. Besides, all membranes prepared using low GGA content could impart enhanced hydrophilic nature and permeation characteristics compared to pristine PPSU membranes. Moreover, greater thermal stability, surface roughness, and a noticeable decline in the mean pore size of the membrane were obtained.
To overcome the biofouling challenge which faces membrane water treatment processed, the novel superhydrophobic carbon nanomaterials impregnated on/powder activated carbon (CNMs/PAC) was utilized to successfully design prepare an antimicrobial membrane. The research was conducted following a systematic statistical design of experiments technique considering various parameters of composite membrane fabrication. The impact of these parameters of composite membrane on Staphylococcus aureus growth was investigated. The bacteria growth was analyzed through spectrophotometer and SEM. The effect of CNMs' hydrophobicity on the bacterial colonies revealed a decrease in the abundance of bacterial colonies and an alteration in structure with increasing the hydrophobicity. The results revealed that the optimum preparative conditions for carbon loading CNMs/PAC was 363.04 mg with a polymer concentration of 22.64 g/100 g, and a casting knife thickness of 133.91 μm. These conditions have resulted in decreasing the number of bacteria colonies to about 7.56 CFU. Our results provided a strong evidence on the antibacterial effect and consequently on the antibiofouling potential of CNMs/PAC in membrane.
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.
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.