Understanding and Designing a High-Performance Ultrafiltration Membrane Using Machine Learning

Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives

Membrane technologies have become proficient alternatives for advanced wastewater treatment, ensuring high contaminant removal and sustainable resource recovery. Despite significant progress, ongoing research efforts aim to further optimize treatment performance. Among the challenges faced, membrane fouling persists as a relevant obstacle in membrane technologies, necessitating the development of more effective mitigation strategies. Mathematical models, widely employed for predicting treatment performance, generally exhibit low accuracy and suffer from uncertainties due to the complex and variable nature of wastewater. To overcome these limitations, numerous studies have proposed artificial intelligence (AI) modeling to accurately predict membrane technologies' performance and fouling mechanisms. This approach aims to provide advanced simulations and predictions, thereby enhancing process control, optimization, and intensification. This literature review explores recent advancements in modeling membrane-based wastewater treatment processes through AI models. The analysis highlights the enormous potential of this research field in enhancing the efficiency of membrane technologies. The role of AI modeling in defining optimal operating conditions, developing effective strategies for membrane fouling mitigation, enhancing the performance of novel membrane-based technologies, and improving membrane fabrication techniques is discussed. These enhanced process optimization and control strategies driven by AI modeling ensure improved effluent quality, optimized resource consumption, and minimized operating costs. The potential contribution of this cutting-edge approach to a paradigm shift toward sustainable wastewater treatment is examined. Finally, this review outlines future perspectives, emphasizing the research challenges that require attention to overcome the current limitations hindering the integration of AI modeling in wastewater treatment plants.

In situ acid production by organic matter induced with trace homogeneous Fenton reagent for membrane fouling control

The homogeneous Fenton process involves both coagulation and oxidation, but it requires added acidity, so it is rarely used to control membrane fouling. This work found that the pH of neutral simulated wastewater sharply declined to 4.1 after pre-treatment with 0.1 mM Fenton reagent (Fe2+:H2O2=1:1) without added acidity. This occurred mainly because the trace homogeneous Fenton reagent induced in situ acid production by organic matter in the wastewater, which supplied the acidic conditions required for the Fenton reaction and ensured that the reaction could proceed continuously. Then, oxidation during the pre-Fenton process enhanced the electrostatic repulsion forces and effectively weakened the hydrogen bonds of organic matter at the membrane surface by altering the net charge and hydroxyl content of organic matter, while coagulation caused the foulants to gather and form large aggregates. These changes diminished the deposition of foulants onto the membrane surface and resulted in a looser fouling layer, which eventually caused the membrane fouling rate to decline from 83 % to 24 % and the flux recovery rate to increase from 44 % to 98 % during 2 h of filtration. This membrane fouling mitigation ability is much superior to that of pre-H2O2, pre-Fe2+ or pre-Fe3+ processes with equivalent doses.

The mitigation potential of synergistic quorum quenching and antibacterial properties for biofilm proliferation and membrane biofouling

Biofouling has been a persistent problem hindering the application of membranes in water treatment, and quorum quenching has been identified as an effective method for mitigating biofouling, but surface accumulation of live bacteria still induces biofilm secretion, which poses a significant challenge for sustained prevention of membrane biofouling. In this study, we utilized quercetin, a typical flavonoid with the dual functions of quorum quenching and bacterial inactivation, to evaluate its role in preventing biofilm proliferation and against biofouling. Quercetin exhibited excellent antibacterial activity against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), and the decreased bioactivity was positively correlated with the quercetin concentration, with inhibition rates of 53.1 % and 57.4 %, respectively, at the experimental concentrations. The RT-qPCR results demonstrated that quercetin inhibited AI-2 of E. coli and AGR of S. aureus mediated quorum sensing system, and reduced the expression of genes such as adhesion, virulence, biofilm secretion, and key regulatory proteases. As a result, the bacterial growth cycle was retarded and the biomass and biofilm maturation cycles were alleviated with the synergistic effect of quorum quenching and antibacterial activity. In addition, membrane biofouling was significantly declined in the dynamic operation experiments, dead cells in the biofilm overwhelmingly dominated, and the final normalized water fluxes were increased by more than 49.9 % and 34.5 % for E. coli and S. aureus, respectively. This work demonstrates the potential for mitigating biofouling using protocols that quorum quenching and inactivate bacteria, also provides a unique and long-lasting strategy to alleviate membrane fouling.

Design and Machine Learning Prediction of In Situ Grown PDA-Stabilized MOF (UiO-66-NH2) Membrane for Low-Pressure Separation of Emulsified Oily Wastewater

Significant progress has been made in designing advanced membranes; however, persistent challenges remain due to their reduced permeation rates and a propensity for substantial fouling. These factors continue to pose significant barriers to the effective utilization of membranes in the separation of oil-in-water emulsions. Metal-organic frameworks (MOFs) are considered promising materials for such applications; however, they encounter three key challenges when applied to the separation of oil from water: (a) lack of water stability; (b) difficulty in producing defect-free membranes; and (c) unresolved issue of stabilizing the MOF separating layer on the ceramic membrane (CM) support. In this study, a defect-free hydrolytically stable zirconium-based MOF separating layer was formed through a two-step method: first, by in situ growth of UiO-66-NH2 MOF into the voids of polydopamine (PDA)-functionalized CM during the solvothermal process, and then by facilitating the self-assembly of UiO-66-NH2 with PDA using a pressurized dead-end assembly. A stable MOF separating layer was attained by enriching the ceramic support with amines and hydroxyl groups using PDA, which assisted in the assembly and stabilization of UiO-66-NH2. The PDA-s-UiO-66-NH2-CM membrane displayed air superhydrophilicity and underwater superoleophobicity, demonstrating its oil resistance and high antifouling behavior. The PDA-s-UiO-66-NH2-CM membrane has shown exceptionally high permeability and separation capacity for challenging oil-in-water emulsions. This is attributed to numerous nanochannels from the membrane and its high resistance to oil adhesion. The membranes showed excellent stability over 15 continuous test cycles, which indicates that the developed MOFs separating layers have a low tendency to be clogged by oil droplets during separation. Machine learning-based Gaussian process regression (GPR) models as nonparametric kernel-based probabilistic models were employed to predict the performance efficiency of the PDA-s-UiO-66-NH2-CM membrane in oil-in-water separation. The outcomes were compared with the support vector machine (SVM) and decision tree (DT) algorithm. This efficiency includes various metrics related to its separation accuracy, and the models were developed through feature engineering to identify and utilize the most significant factors affecting the membrane's performance. The results proved the reliability of GPR optimization with the highest prediction accuracy in the validation phase. The average percentage increase of the GPR model compared to the SVM and DT model was 6.11 and 42.94%, respectively.

A Meta-Analysis to Revisit the Property-Aggregation Relationships of Carbon Nanomaterials: Experimental Observations versus Predictions of the DLVO Theory

Contradicting relationships between physicochemical properties of nanomaterials (e.g., size and ζ-potential) and their aggregation behavior have been constantly reported in previous literature, and such contradictions deviate from the predictions of the classic Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. To resolve such controversies, in this work, we employed a meta-analytic approach to synthesize the data from 46 individual studies reporting the critical coagulation concentration (CCC) of two carbon nanomaterials, namely, graphene oxide (GO) and carbon nanotube (CNT). The correlations between CCC and material physicochemical properties (i.e., size, ζ-potential, and surface functionalities) were examined and compared to the theoretical predictions. Results showed that the CCC of electrostatically stabilized carbon nanomaterials increased with decreasing nanomaterial size when their hydrodynamic sizes were smaller than ca. 200 nm. This is qualitatively consistent with the prediction of the DLVO theory but with a smaller threshold size than the predicted 2 μm. Above the threshold size, the material ζ-potential can be correlated to CCC for nanomaterials with moderate/low surface charge, in agreement with the DLVO theory. The correlation was not observed for highly charged nanomaterials because of their underestimated surface potential by the ζ-potential. Furthermore, a correlation between the C/O ratio and CCC was observed, where a lower C/O ratio resulted in a higher CCC. Overall, our findings rationalized the inconsistency between experimental observation and theoretical prediction and provided essential insights into the aggregation behavior of nanomaterials in water, which could facilitate their rational design.

Machine Learning in Membrane Design: From Property Prediction to AI-Guided Optimization

Porous membranes, either polymeric or two-dimensional materials, have been extensively studied because of their outstanding performance in many applications such as water filtration. Recently, inspired by the significant success of machine learning (ML) in many areas of scientific discovery, researchers have started to tackle the problem in the field of membrane design using data-driven ML tools. In this Mini Review, we summarize research efforts on three types of applications of machine learning in membrane design, including (1) membrane property prediction using ML, (2) gaining physical insight and drawing quantitative relationships between membrane properties and performance using explainable artificial intelligence, and (3) ML-guided design, optimization, or virtual screening of membranes. On top of the review of previous research, we discuss the challenges associated with applying ML for membrane design and potential future directions.

Aquatic Functional Liquid Crystals: Design, Functionalization, and Molecular Simulation

Aquatic functional liquid crystals, which are ordered molecular assemblies that work in water environment, are described in this review. Aquatic functional liquid crystals are liquid-crystalline (LC) materials interacting water molecules or aquatic environment. They include aquatic lyotropic liquid crystals and LC based materials that have aquatic interfaces, for example, nanoporous water treatment membranes that are solids preserving LC order. They can remove ions and viruses with nano- and subnano-porous structures. Columnar, smectic, bicontinuous LC structures are used for fabrication of these 1D, 2D, 3D materials. Design and functionalization of aquatic LC sensors based on aqueous/LC interfaces are also described. The ordering transitions of liquid crystals induced by molecular recognition at the aqueous interfaces provide distinct optical responses. Molecular orientation and dynamic behavior of these aquatic functional LC materials are studied by molecular dynamics simulations. The molecular interactions of LC materials and water are key of these investigations. New insights into aquatic functional LC materials contribute to the fields of environment, healthcare, and biotechnology.

Role of exosomes in metastasis and therapeutic resistance in esophageal cancer

Esophageal cancer (EC) has a high incidence and mortality rate and is emerging as one of the most common health problems globally. Owing to the lack of sensitive detection methods, uncontrollable rapid metastasis, and pervasive treatment resistance, EC is often diagnosed in advanced stages and is susceptible to local recurrence. Exosomes are important components of intercellular communication and the exosome-mediated crosstalk between the cancer and surrounding cells within the tumor microenvironment plays a crucial role in the metastasis, progression, and therapeutic resistance of EC. Considering the critical role of exosomes in tumor pathogenesis, this review focused on elucidating the impact of exosomes on EC metastasis and therapeutic resistance. Here, we summarized the relevant signaling pathways involved in these processes. In addition, we discussed the potential clinical applications of exosomes for the early diagnosis, prognosis, and treatment of EC.

Non-Equilibrium Block Copolymer Self-Assembly Based Porous Membrane Formation Processes Employing Multicomponent Systems

Porous polymer-derived membranes are useful for applications ranging from filtration and separation technologies to energy storage and conversion. Combining block copolymer (BCP) self-assembly with the industrially scalable, non-equilibrium phase inversion technique (SNIPS) yields membranes comprising periodically ordered top surface structures supported by asymmetric, hierarchical substructures that together overcome performance tradeoffs typically faced by materials derived from equilibrium approaches. This review first reports on recent advances in understanding the top surface structural evolution of a model SNIPS-derived system during standard membrane formation. Subsequently, the application of SNIPS to multicomponent systems is described, enabling pore size modulation, chemical modification, and transformation to non-polymeric materials classes without compromising the structural features that define SNIPS membranes. Perspectives on future directions of both single-component and multicomponent membrane materials are provided. This points to a rich and fertile ground for the study of fundamental as well as applied problems using non-equilibrium-derived asymmetric porous materials with tunable chemistry, composition, and structure.