Response Surface Methodology Modeling Correlation of Polymer Composite Carbon Nanotubes/Chitosan Nanofiltration Membranes for Water Desalination

Abstract

Quick population growth and worldwide industrialization is creating serious issues in accessing safe drinking water, which necessitates the exploration of operative and economical water treatment methods. This study aims to develop chitosan and carbon nanotube (CNT)-incorporated nanofiltration polyethersulfone (PES) membranes via the phase inversion method that have effective salt rejection capability. Various membranes, i.e., pristine PES, PES�0.75 wt % chitosan, PES�0.1 wt % CNTs, and PES�0.1 wt % CNT/ chitosan composites, were fabricated and characterized. The composition, surface texture, and cross-sectional microstructures of the synthesized membranes were investigated by using attenuated total reflection−Fourier-transform infrared spectroscopy, atomic-force microscopy, and scanning electron microscopy, respectively. The chitosan/MWNTs containing a PES membrane showed excellent water flux and salt rejection. This composite membrane registered a maximum water flux of 80.26 L/m 2 •h and ∼95.5% salt rejection at 40 °C and 4 kg/cm 2 of feed water pressure, as validated by ANOVA analysis. Response surface methodology showed a complete fit for the experimental analysis. This study suggests that the designed membrane can be used in practice to treat brackish water.

Synthesis and Impurity Profiling of a Vital Intermediate in Gabapentin and Bispidine Derivative Pathways by Response Surface Methodology

“…In addition, Table 4 shows that the difference between Pred-R 2 (0.9572) and Adj-R 2 (0.9820) was within a certain range (<0.2), which indicates that the model is appropriate. 37 A higher level of precision was also indicated by the relatively low coefficient of variation (0.7116), which also validated the reliability of the experiments. 33 Adequate precision, which denotes the signal-to-noise ratio (a measure of the design space in predicted response relative to its error), must be greater than 4.…”

“…As a result of the strong correlation between the experimental and predicted response values demonstrated by a high R 2 , the model’s ability to predict the reaction yield had a high level of accuracy. In addition, Table shows that the difference between Pred- R 2 (0.9572) and Adj- R 2 (0.9820) was within a certain range (<0.2), which indicates that the model is appropriate . A higher level of precision was also indicated by the relatively low coefficient of variation (0.7116), which also validated the reliability of the experiments .…”