Selective removal or enrichment of targeted solutes including micropollutants, valuable elements, and mineral scalants from complex aqueous matrices is both challenging and pivotal to the success of water purification and resource recovery from unconventional water resources. Membrane separation with precision at the subnanometer or even subangstrom scale is of paramount importance to address those challenges via enabling “fit-for-purpose” water and wastewater treatment. So far, researchers have attempted to develop novel membrane materials with precise and tailored selectivity by tuning membrane structure and chemistry. In this critical review, we first present the environmental challenges and opportunities that necessitate improved solute–solute selectivity in membrane separation. We then discuss the mechanisms and desired membrane properties required for better membrane selectivity. On the basis of the most recent progress reported in the literature, we examine the key principles of material design and fabrication, which create membranes with enhanced and more targeted selectivity. We highlight the important roles of surface engineering, nanotechnology, and molecular-level design in improving membrane selectivity. Finally, we discuss the challenges and prospects of highly selective NF membranes for practical environmental applications, identifying knowledge gaps that will guide future research to promote environmental sustainability through more precise and tunable membrane separation.

Enhancing the chemical and physical properties of the polyamide active layer of thin-film composite (TFC) membranes by surface coating is a goal long-pursued. Atomic layer deposition (ALD) has been proposed as an innovative approach to deposit chemically robust metal oxides onto membrane surfaces due to its unique capability to control coating conformality and thickness with atomic scale precision. This study examined the potential to coat the surface of TFC reverse osmosis (RO) and nanofiltration (NF) membranes via ALD of TiO2. Our results suggest that the optimal ALD conditions, the film growth kinetics, and the depth of deposition are different for RO and NF membranes due to the different diffusive transport of ALD precursors through the membrane pores. The TiO2 coating mainly located at the surface of the RO membrane; in contrast, the TiO2 coating extended to the depth of the NF membrane. The TiO2 coating degraded membrane water permeability and salt rejection beyond 10 cycles of ALD, the condition commonly employed in previous ALD-based membrane modification studies. Instead, this study showed that with fewer than 10 cycles, the TiO2 coating of RO membrane increased the membrane surface charge without negatively impacting water permeability and salt rejection. For the NF membranes, the coating of TiO2 inside their pores led to the tuning of pore sizes and increased the rejection of selected solutes.

Polymeric membrane design is a multidimensional process involving selection of membrane materials and optimization of fabrication conditions from an infinite candidate space. It is impossible to explore the entire space by trial-and-error experimentation. Here, we present a membrane design strategy utilizing machine learning-based Bayesian optimization to precisely identify the optimal combinations of unexplored monomers and their fabrication conditions from an infinite space. We developed ML models to accurately predict water permeability and salt rejection from membrane monomer types (represented by the Morgan fingerprint) and fabrication conditions. We applied Bayesian optimization on the built ML model to inversely identify sets of monomer/fabrication condition combinations with the potential to break the upper bound for water/salt selectivity and permeability. We fabricated eight membranes under the identified combinations and found that they exceeded the present upper bound. Our findings demonstrate that ML-based Bayesian optimization represents a paradigm shift for next-generation separation membrane design.