Ion transport and selectivity in thin film composite membranes in pressure-driven and electrochemical processes

Exploring the Knowledge Attained by Machine Learning on Ion Transport across Polyamide Membranes Using Explainable Artificial Intelligence

Recent studies have increasingly applied machine learning (ML) to aid in performance and material design associated with membrane separation. However, whether the knowledge attained by ML with a limited number of available data is enough to capture and validate the fundamental principles of membrane science remains elusive. Herein, we applied explainable artificial intelligence (XAI) to thoroughly investigate the knowledge learned by ML on the mechanisms of ion transport across polyamide reverse osmosis (RO) and nanofiltration (NF) membranes by leveraging 1,585 data from 26 membrane types. The Shapley additive explanation method based on cooperative game theory was used to unveil the influences of various ion and membrane properties on the model predictions. XAI shows that the ML can capture the important roles of size exclusion and electrostatic interaction in regulating membrane separation properly. XAI also identifies that the mechanisms governing ion transport possess different relative importance to cation and anion rejections during RO and NF filtration. Overall, we provide a framework to evaluate the knowledge underlying the ML model prediction and demonstrate that ML is able to learn fundamental mechanisms of ion transport across polyamide membranes, highlighting the importance of elucidating model interpretability for more reliable and explainable ML applications to membrane selection and design.

Understanding Selectivity in Solute-Solute Separation: Definitions, Measurements, and Comparability

The development of membranes capable of precise solute-solute separation is still in its burgeoning stage without a standardized protocol for evaluating selectivity. Three types of membrane processes with different driving forces, including pressure-driven filtration, concentration difference-driven diffusion, and electric field-driven ion migration, have been applied in this study to characterize solute-solute selectivity of a commercial nanofiltration membrane. Our results demonstrated that selectivity values measured using different methods, or even different conditions with the same method, are generally not comparable. The cross-method incomparability is true for both apparent selectivity, defined as the ratio between concentration-normalized fluxes, and the more intrinsic selectivity, defined as the ratio between the permeabilities of solutes through the active separation layer. The difference in selectivity measured using different methods possibly stems from the fundamental differences in the driving force of ion transport, the effect of water transport, and the interaction between cations and anions. We further demonstrated the difference in selectivity measured using feed solutions containing single-salt species and that containing mixed salts. A consistent protocol with standardized testing conditions to facilitate fair performance comparison between studies is proposed.

Enhanced Specific Mechanism of Separation by Polymeric Membrane Modification-A Short Review

Membrane technologies have found a significant application in separation processes in an exceeding range of industrial fields. The crucial part that is decided regarding the efficiency and effectivity of separation is the type of membrane. The membranes deal with separation problems, working under the various mechanisms of transportation of selected species. This review compares significant types of entrapped matter (ions, compounds, and particles) within membrane technology. The ion-exchange membranes, molecularly imprinted membranes, smart membranes, and adsorptive membranes are investigated. Here, we focus on the selective separation through the above types of membranes and detect their preparation methods. Firstly, the explanation of transportation and preparation of each type of membrane evaluated is provided. Next, the working and application phenomena are evaluated. Finally, the review discusses the membrane modification methods and briefly provides differences in the properties that occurred depending on the type of materials used and the modification protocol.

Differentiating Solutes with Precise Nanofiltration for Next Generation Environmental Separations: A Review

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.