Recent progress on the smart membranes based on two-dimensional materials

The intelligent prediction of membrane fouling during membrane filtration by mathematical models and artificial intelligence models

Recently, membrane separation technology has been widely utilized in filtration process intensification due to its efficient performance and unique advantages, but membrane fouling limits its development and application. Therefore, the research on membrane fouling prediction and control technology is crucial to effectively reduce membrane fouling and improve separation performance. This review first introduces the main factors (operating condition, material characteristics, and membrane structure properties) and the corresponding principles that affect membrane fouling. In addition, mathematical models (Hermia model and Tandem resistance model), artificial intelligence (AI) models (Artificial neural networks model and fuzzy control model), and AI optimization methods (genetic algorithm and particle swarm algorithm), which are widely used for the prediction of membrane fouling, are summarized and analyzed for comparison. The AI models are usually significantly better than the mathematical models in terms of prediction accuracy and applicability of membrane fouling and can monitor membrane fouling in real-time by working in concert with image processing technology, which is crucial for membrane fouling prediction and mechanism studies. Meanwhile, AI models for membrane fouling prediction in the separation process have shown good potential and are expected to be further applied in large-scale industrial applications for separation and filtration process intensification. This review will help researchers understand the challenges and future research directions in membrane fouling prediction, which is expected to provide an effective method to reduce or even solve the bottleneck problem of membrane fouling, and to promote the further application of AI modeling in environmental and food fields.

Electrically Redox-Active Membrane with Switchable Selectivity to Contaminants for Water Purification

Membrane technology provides an attractive approach for water purification but faces significant challenges in separating small molecules due to its lack of satisfactory permselectivity. In this study, a polypyrrole-based active membrane with a switchable multi-affinity that simultaneously separates small ionic and organic contaminants from water was created. Unlike conventional passive membranes, the designed membrane exhibits a good single-pass filtration efficiency (>99%, taking 1-naphthylamine and Pb2+ as examples) and high permeability (227 L/m2/h). Applying a reversible potential can release the captured substances from the membrane, thus enabling membrane regeneration and self-cleaning without the need for additives. Advanced characterizations reveal that potential switching alters the orientation of the doped amphipathic molecules with the self-alignment of the hydrophobic alkyl chains or the disordered sulfonate anions to capture the target organic molecules or ions via hydrophobic or electrostatic interactions, respectively. The designed smart membrane holds great promise for controllable molecular separation and water purification.

Two-Dimensional Graphene-Based Potassium Channels Built at an Oil/Water Interface

Graphene-based laminar membranes exhibit remarkable ion sieving properties, but their monovalent ion selectivity is still low and much less than the natural ion channels. Inspired by the elementary structure/function relationships of biological ion channels embedded in biomembranes, a new strategy is proposed herein to mimic biological K+ channels by using the graphene laminar membrane (GLM) composed of two-dimensional (2D) angstrom(Å)-scale channels to support a simple model of semi-biomembrane, namely oil/water (O/W) interface. It is found that K+ is strongly preferred over Na+ and Li+ for transferring across the GLM-supported water/1,2-dichloroethane (W/DCE) interface within the same potential window (-0.1-0.6 V), although the monovalent ion selectivity of GLM under the aqueous solution is still low (K+/Na+~1.11 and K+/Li+~1.35). Moreover, the voltammetric responses corresponding to the ion transfer of NH4+ observed at the GLM-supported W/DCE interface also show that NH4+ can often pass through the biological K+ channels due to their comparable hydration-free energies and cation-π interactions. The underlying mechanism of as-observed K+ selective voltammetric responses is discussed and found to be consistent with the energy balance of cationic partial-dehydration (energetic costs) and cation-π interaction (energetic gains) as involved in biological K+ channels.

Electrically Modulated Nanofiltration Membrane Based on an Arch-Bridged Graphene Structure for Multicomponent Molecular Separation

Tunable regulation of molecular penetration through porous membranes is highly desirable for membrane applications in the pharmaceutical and medical fields. However, in most previous reports additional reagents or components are usually needed to provide the graphene-based membranes with responsiveness. Herein, we report tunable arch-bridged reduced graphene oxide (rGO) nanofiltration membranes modulated by the applied voltage. Under a finite voltage of 5 V, the rGO membrane could completely reject organic/anionic molecules. With assistance of the voltage, the positive-charge-modified rGO membrane realized the universal rejection of both cationic and anionic dyes, also showing the valid modulation in harsh organic solvents. The efficient electrical modulation depended on the synergetic effects of Donnan repulsion and size exclusion, benefiting from the electric field enhancement in arch-bridged rGO structures. Furthermore, multicomponent separation was achieved by our electrically modulated rGO-based membranes, demonstrating their potential in practical applications such as pharmaceutical industries.