Structure-performance relationships between amino acid-functionalized graphene quantum dots and self-cleaning nanofiltration membranes

To develop self-cleaning nanofiltration (NF) membranes with excellent desalination performance, five kinds of amino acid-functionalized graphene quantum dots (AA-fGQDs) were synthesized and introduced into the active layers of the thin-film nanocomposite (TFN) membranes via the interfacial polymerization (IP) method. The synthesized AA-fGQDs and the resulting TFN NF membranes were systematically characterized to investigate the structure-performance relationships between the AA-fGQDs and the NF membranes. The results showed that the desalination and photocatalytic self-cleaning performance of the fabricated TFN membranes were closely related to the types and structures of AA-fGQDs. The TFN membranes incorporating AA-fGQDs with the polar amino acids exhibit better desalination performance than those incorporating AA-fGQDs with the non-polar amino acids, overcoming the trade-off effect between water flux and salt rejection. The TFN-Asp-GQDs membrane exhibits superior water permeability of 27.35 LMH·bar−1 while maintaining high salt rejection (97.95% for Na2SO4), equal to 4.41 times of the TFC-blank membrane (6.20 LMH·bar−1) and 1.85 times of the TFN-GQDs membrane (14.78 LMH·bar−1). Moreover, the TFN membranes incorporating AA-fGQDs with low bandgap energies exhibit better self-cleaning performance than those incorporating AA-fGQDs with high bandgap energies. The TFN-Cys-GQDs membrane shows the best self-cleaning performance with the highest water flux recovery (96.95%), mainly ascribed to the low bandgap energy of Cys-GQDs. Therefore, the desalination and self-cleaning performance of NF membranes can be effectively regulated by the types of AA-fGQDs. It also suggests that the utilization of suitable AA-fGQDs presents a novel and promising strategy to develop self-cleaning NF membranes with excellent desalination performance.