More recent attention has been focused on the utilization of Jatropha curcas in the field of water treatment. The potential of Jatropha oil in the synthesis of membrane for water filtration had been explored, its performance compared to the addition of graphene oxide (GO) in the polymer matrix. Jatropha oil was modified in a two-step method to produce Jatropha oil-based polyol (JOL) and was blended with hexamethylene diisocyanate (HDI) to produce Jatropha polyurethane membrane (JPU). JPU was synthesized in different conditions to obtain the optimized membrane and was blended with different GO loading to form Jatropha/graphene oxide composite membrane (JPU/GO) for performance improvement. The synthesized pristine JPU and JPU/GO were evaluated and the materials were analyzed using fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), contact angle, water flux, and field emission scanning electron microscopy (FESEM). Results showed that the ratio of HDI to JOL for optimized JPU was obtained at 5:5 (v/v) with the cross-linking temperature at 90 °C and curing temperature at 150 °C. As GO was added into JPU, several changes were observed. The glass transition temperature (Tg) and onset temperature (To) increased from 58 °C to 69 °C and from 170 °C to 202 °C, respectively. The contact angle, however, decreased from 88.8° to 52.1° while the water flux improved from 223.33 L/m2·h to 523.33 L/m2·h, and the pore distribution in JPU/GO became more orderly. Filtration of copper ions using the synthesized membrane was performed to give rejection percentages between 33.51% and 71.60%. The results indicated that GO had a significant impact on JPU. Taken together, these results have suggested that JPU/GO has the potential for use in water filtration.

This study aimed to optimize the removal of Cu(II) ions from an aqueous solution using a Jatropha oil bio-based membrane blended with 0.50 wt% graphene oxide (JPU/GO 0.50 wt%) using a central composite model (CCD) design using response surface methodology. The input factors were the feed concentration (60–140) ppm, pressure (1.5–2.5) bar, and solution pH value (3–5). An optimum Cu(II) ions removal of 87% was predicted at 116 ppm feed concentration, 1.5 bar pressure, and pH 3.7, while the validated experimental result recorded 80% Cu(II) ions removal, with 95% of prediction intervals. A statistically non-significant term was removed from the analysis by the backward elimination method to improve the model’s accuracy. Using the reduction method, the predicted R2 value was increased from −0.16 (−16%) to 0.88 (88%), suggesting that the reduced model had a good predictive ability. The quadratic regression model was significant (R2 = 0.98) for the optimization prediction. Therefore, the results from the reduction model implied acceptable membrane performance, offering a better process optimization for Cu(II) ions removal.

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