Understanding and optimization of thin film nanocomposite membranes for reverse osmosis with machine learning

Application of machine learning in ultrasonic pretreatment of sewage sludge: Prediction and optimization

In this research, typical industrial scenarios were analyzed optimized by machine learning algorithms, which fills the gap of massive data and industrial requirements in ultrasonic sludge treatment. Principal component analysis showed that the ultrasonic density and ultrasonic time were positively correlated with soluble chemical oxygen demand (SCOD), total nitrogen (TN), and total phosphorus (TP). Within five machine learning models, the best model for SCOD prediction was XG-boost (R2 = 0.855), while RF was the best for TN and TP (R2 = 0.974 and 0.957, respectively). In addition, SHAP indicated that the importance feature for SCOD, TN, and TP was ultrasonic time, and sludge concentration, respectively. Finally, the typical industrial scenario of ultrasonic pretreatment of sludge was analyzed. In the secondary sludge, treatment volume at 0.6 L, the pH at 7.0, and the ultrasonic time at 20 min was best to improve the SCOD. In the ultrasonic pretreatment primary sludge, treatment volume of 0.3 L, pH of 7.0, and ultrasonic time of 15 min was best to improve the SCOD. Furthermore, the ultrasonic power at 700 W and ultrasonic time at 20 min were best to improve the C/N and C/P in the secondary sludge. In the primary sludge, the ultrasonic power at 600 W, and the ultrasonic time at 15 min were best to improve C/N and C/P. This study lays a foundation for the practical application of ultrasonic pretreatment of sludge and provides basic information for typical industrial scenarios.

Enhanced desalination with polyamide thin-film membranes using ensemble ML chemometric methods and SHAP analysis

Addressing global freshwater scarcity requires innovative technological solutions, among which desalination through thin-film composite polyamide membranes stands out. The performance of these membranes plays a vital role in desalination, necessitating advanced predictive modeling for optimization. This study harnesses machine learning (ML) algorithms, including support vector machine (SVM), neural networks (NN), linear regression (LR), and multivariate linear regression (MLR), alongside their ensemble techniques to predict and enhance average water flux (AWF) and average salt rejection (ASR) essential metrics of desalination efficiency. To ensure model interpretability and feature importance analysis, SHapley Additive exPlanations (SHAP) were employed, providing both global and local insights into feature contributions. Initially, the individual models were validated, with NN demonstrating superior performance for both AWF and ASR, achieving the lowest mean absolute error (MAE = 0.001) and root mean squared error (RMSE = 0.0111) for AWF and an MAE = 0.0107 and RMSE = 0.0982 for ASR. The accuracy of predictions improved significantly with ensemble models, as evidenced by the near-perfect Nash-Sutcliffe efficiency (NSE) values. Specifically, the NN ensemble (NN-E) and Linear Regression ensemble (LR-E) reached an MAE and RMSE of 0.001 and 0.0111, respectively, for AWF. For ASR, NN-E reduced the MAE to 0.0013 and the RMSE to 0.0089, while LR-E maintained competitive performance with an MAE of 0.0133 and an RMSE of 0.0936. SHAP analysis revealed that features such as MDP and TMC were critical drivers of performance, with MDP showing the most significant positive impact on ASR. These findings demonstrate the dominance of ensemble methods over individual algorithms in predicting key desalination parameters. The enhanced precision in estimating AWF and ASR offered by these neuro-intelligent ensembles, combined with the interpretability provided by SHAP analysis, can lead to significant environmental and operational improvements in membrane performance, optimizing resource usage and minimizing ecological impacts. This study paves the way for integrating intelligent ML ensembles and SHAP-based interpretability into the practical field of membrane technology, marking a step forward toward sustainable and efficient desalination processes.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Predicting maturity and identifying key factors in organic waste composting using machine learning models

The measurement of germination index (GI) in composting is a time-consuming and laborious process. This study employed four machine learning (ML) models, namely Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Decision Tree (DT), to predict GI based on key composting parameters. The prediction results showed that the coefficient of determination (R2) for RF (>0.9) and ANN (>0.9) was higher than SVR (<0.6) and DT (<0.8), suggesting that RF and ANN displayed superior predictive performance for GI. The SHapley additive exPlanations value result indicated that composting time, temperature, and pH were the important features contributing to GI. Composting time was found to have the most significant impact on GI. Overall, RF and ANN were suggested as effective tools for predicting GI in composting. This study offers the reliable approach of accurately predicting GI in composting processes, thereby enabling intelligent composting practices.

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.

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

Ultrafiltration (UF) as one of the mainstream membrane-based technologies has been widely used in water and wastewater treatment. Increasing demand for clean and safe water requires the rational design of UF membranes with antifouling potential, while maintaining high water permeability and removal efficiency. This work employed a machine learning (ML) method to establish and understand the correlation of five membrane performance indices as well as three major performance-determining membrane properties with membrane fabrication conditions. The loading of additives, specifically nanomaterials (A_wt %), at loading amounts of >1.0 wt % was found to be the most significant feature affecting all of the membrane performance indices. The polymer content (P_wt %), molecular weight of the pore maker (M_Da), and pore maker content (M_wt %) also made considerable contributions to predicting membrane performance. Notably, M_Da was more important than M_wt % for predicting membrane performance. The feature analysis of ML models in terms of membrane properties (i.e., mean pore size, overall porosity, and contact angle) provided an unequivocal explanation of the effects of fabrication conditions on membrane performance. Our approach can provide practical aid in guiding the design of fit-for-purpose separation membranes through data-driven virtual experiments.

Machine Learning Guided Polyamide Membrane with Exceptional Solute-Solute Selectivity and Permeance

Designing polymeric membranes with high solute-solute selectivity and permeance is important but technically challenging. Existing industrial interfacial polymerization (IP) process to fabricate polyamide-based polymeric membranes is largely empirical, which requires enormous trial-and-error experimentations to identify optimal fabrication conditions from a wide candidate space for separating a given solute pair. Herein, we developed a novel multitask machine learning (ML) model based on an artificial neural network (ANN) with skip connections and selectivity regularization to guide the design of polyamide membranes. We used limited sets of lab-collected data to obtain satisfactory model performance over four iterations by introducing human expert experience in the online learning process. Four membranes under fabrication conditions guided by the model exceeded the present upper bound for mono/divalent ion selectivity and permeance of the polymeric membranes. Moreover, we obtained new mechanistic insights into the membrane design through feature analysis of the model. Our work demonstrates a ML approach that represents a paradigm shift for high-performance polymeric membranes design.

Extreme Ion-Transport Inorganic 2D Membranes for Nanofluidic Applications

Inorganic 2D materials offer a new approach to controlling mass diffusion at the nanoscale. Controlling ion transport in nanofluidics is key to energy conversion, energy storage, water purification, and numerous other applications wherein persistent challenges for efficient separation must be addressed. The recent development of 2D membranes in the emerging field of energy harvesting, water desalination, and proton/Li-ion production in the context of green energy and environmental technology is herein discussed. The fundamental mechanisms, 2D membrane fabrication, and challenges toward practical applications are highlighted. Finally, the fundamental issues of thermodynamics and kinetics are outlined along with potential membrane designs that must be resolved to bridge the gap between lab-scale experiments and production levels.

Thermodynamics and kinetic analysis of membrane: Challenges and perspectives

The ultimate structure of the membrane is determined using two important effects: (i) thermodynamic effect and (ii) kinetic effect. Controlling the mechanism of kinetic and thermodynamic processes in phase separation is essential for enhancing membrane performance. However, the relationship between system parameters and the ultimate membrane morphology is still largely empirical. This review focuses on the fundamental ideas behind thermally induced phase separation (TIPS) and nonsolvent-induced phase separation (NIPS) methods, including both kinetic and thermodynamic elements. The thermodynamic approach to understanding phase separation and the effect of different interaction parameters on membrane morphology has been discussed in detail. Furthermore, this review explores the capabilities and limitations of different macroscopic transport models used for the last four decades to explore the phase inversion process. The application of molecular simulations and phase field to understand phase separation has also been briefly examined. Finally, it discusses the thermodynamic approach to understanding phase separation and the consequence of different interaction parameters on membrane morphology, as well as possible directions for artificial intelligence to fill the gaps in the literature. This review aims to provide comprehensive knowledge and motivation for future modeling work for membrane fabrication via new techniques such as nonsolvent-TIPS, complex-TIPS, non-solvent assisted TIPS, combined NIPS-TIPS method, and mixed solvent phase separation.

Smart utilisation of reverse solute diffusion in forward osmosis for water treatment: A mini review

Forward osmosis (FO) has been widely studied as a promising technology in wastewater treatment, but undesirable reverse solute diffusion (RSD) is inevitable in the FO process. The RSD is generally regarded as a negative factor for the FO process, resulting in the loss of draw solutes and reduced FO efficiency. Conventional strategies to address RSD focus on reducing the amount of reverse draw solutes by fabricating high selective FO membranes and/or selecting the draw solute with low diffusion. However, since RSD is inevitable, doubts have been raised about the strategies to cope with the already occurring reverse draw solutes in the feed solution, and the feasibility to positively utilise the RSD phenomenon to improve the FO process. Herein, we review the state-of-the-art applications of RSD and their benefits such as improving selectivity and maintaining the stability of the feed solution for both independent FO processes and FO integrated processes. We also provide an outlook and discuss important considerations, including membrane fouling, membrane development and draw/feed solution properties, in RSD utilisation for water and wastewater treatment.

Unveiling the migration of Cr and Cd to biochar from pyrolysis of manure and sludge using machine learning

Heavy metal (HM) in biochar derived from pyrolysis of sludge or manure is the main issue for its large-scale application in soils for carbon sequestration. However, there is a paucity of efficient approaches to predict and comprehend the HM migration during pyrolysis for preparing low HM-contained biochar. Herein, the data on the feedstock information (FI), additive, total concentration of feedstock (FTC) of HM Cr and Cd, and pyrolysis condition, were extracted from the literature, to predict total concentration (TC) and retention rate (RR) of Cr and Cd in sludge/manure biochar using ML for mapping their migration during pyrolysis. Two datasets for Cr and Cd were compiled with 388 and 292 data points from 48 and 37 peer-review papers. The results indicated that the TC and RR of Cr and Cd could be predicted by the Random Forest model with test R² of 0.74-0.98. Their TC and RR in biochar were dominated by the FTC and FI, respectively; while pyrolysis temperature was the most important to Cd RR. Moreover, potassium-based inorganic additives decreased the TC and RR of Cr while increased those of Cd. The predictive models and insights provided by this work could aid the understanding of HM migration during manure and sludge pyrolysis and guide the preparation of low HM-contained biochar.

Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management

The extraction of acetic acid and other carboxylic acids from water is an emerging separation need as they are increasingly produced from waste organics and CO₂ during carbon valorization. However, the traditional experimental approach can be slow and expensive, and machine learning (ML) may provide new insights and guidance in membrane development for organic acid extraction. In this study, we collected extensive literature data and developed the first ML models for predicting separation factors between acetic acid and water in pervaporation with polymers' properties, membrane morphology, fabrication parameters, and operating conditions. Importantly, we assessed seed randomness and data leakage problems during model development, which have been overlooked in ML studies but will result in over-optimistic results and misinterpreted variable importance. With proper data leakage management, we established a robust model and achieved a root-mean-square error of 0.515 using the CatBoost regression model. In addition, the prediction model was interpreted to elucidate the variables' importance, where the mass ratio was the topmost significant variable in predicting separation factors. In addition, polymers' concentration and membranes' effective area contributed to information leakage. These results demonstrate ML models' advances in membrane design and fabrication and the importance of vigorous model validation.

Machine learning-assisted exploration for carbon neutrality potential of municipal sludge recycling via hydrothermal carbonization

In the context of advocating carbon neutrality, there are new requirements for sustainable management of municipal sludge (MS). Hydrothermal carbonization (HTC) is a promising technology to deal with high-moisture MS considering its low energy consumption (without drying pretreatment) and value-added products (i.e., hydrochar). This study applied machine learning (ML) methods to conduct a holistic assessment with higher heating value (HHV) of hydrochar, carbon recovery (CR), and energy recovery (ER) as model targets, yielding accurate prediction models with R² of 0.983, 0.844 and 0.858, respectively. Furthermore, MS properties showed positive (e.g., carbon content, HHV) and negative (e.g., ash content, O/C, and N/C) influences on the hydrochar HHV. By comparison, HTC parameters play a critical role for CR (51.7%) and ER (52.5%) prediction. The primary sludge was an optimal HTC feedstock while anaerobic digestion sludge had the lowest potential. This study provided a comprehensive reference for sustainable MS treatment and industrial application.

Prediction of organic contaminant rejection by nanofiltration and reverse osmosis membranes using interpretable machine learning models

Efficiency improvement in contaminant removal by nanofiltration (NF) and reverse osmosis (RO) membranes is a multidimensional process involving membrane material selection and experimental condition optimization. It is unrealistic to explore the contributions of diverse influencing factors to the removal rate by trial-and-error experimentation. However, the advanced machine learning (ML) method is a powerful tool to simulate this complex decision-making process. Here, 4 traditional learning algorithms (MLR, SVM, ANN, kNN) and 4 ensemble learning algorithms (RF, GBDT, XGBoost, LightGBM) were applied to predict the removal efficiency of contaminants. Results reported here demonstrate that ensemble models showed significantly better predictive performance than traditional models. More importantly, this study achieved a compelling tradeoff between accuracy and interpretability for ensemble models with an effective model interpretation approach, which revealed the mutual interaction mechanism between the membrane material, contaminants and experimental conditions in membrane separation. Additionally, feature selection was for the first time achieved based on the aforementioned model interpretation method to determine the most important variable influencing the contaminant removal rate. Ultimately, the four ensemble models retrained by the selected variables achieved distinguished prediction performance (R²_adj = 92.4 %-99.5 %). MWCO (membrane molecular weight cut-off), McGowan volume of solute (V) and molecular weight (MW) of the compound were demonstrated to be the most important influencing factors in contaminant removal by the NF and RO processes. Overall, the proposed methods in this study can facilitate versatile complex decision-making processes in the environmental field, particularly in contaminant removal by advanced physicochemical separation processes.

Study of Membranes with Nanotubes to Enhance Osmosis Desalination Efficiency by Using Machine Learning towards Sustainable Water Management

Water resources management is one of the most important issues nowadays. The necessity of sustainable management of water resources, as well as finding a solution to the water shortage crisis, is a question of our survival on our planet. One of the most important ways to solve this problem is to use water purification systems for wastewater resources, and one of the most necessary reasons for the research of water desalination systems and their development is the problem related to water scarcity and the crisis in the world that has arisen because of it. The present study employs a carbon nanotube-containing nanocomposite to enhance membrane performance. Additionally, the rise in flow brought on by a reduction in the membrane's clogging surface was investigated. The filtration of brackish water using synthetic polyamide reverse osmosis nanocomposite membrane, which has an electroconductivity of 4000 Ds/cm, helped the study achieve its goal. In order to improve porosity and hydrophilicity, the modified raw, multi-walled carbon nanotube membrane was implanted using the polymerization process. Every 30 min, the rates of water flow and rejection were evaluated. The study's findings demonstrated that the membranes have soft hydrophilic surfaces, and by varying concentrations of nanocomposite materials in a prescribed way, the water flux increased up to 30.8 L/m²h, which was notable when compared to the water flux of the straightforward polyamide membranes. Our findings revealed that nanocomposite membranes significantly decreased fouling and clogging, and that the rejection rate was greater than 97 percent for all pyrrole-based membranes. Finally, an artificial neural network is utilized to propose a predictive model for predicting flux through membranes. The model benefits hyperparameter tuning, so it has the best performance among all the studied models. The model has a mean absolute error of 1.36% and an R² of 0.98.

Materials discovery of ion-selective membranes using artificial intelligence

Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering.

Accelerating Discovery of High Fractional Free Volume Polymers from a Data-Driven Approach

As a fundamental structure characteristic in polymers, fractional free volume (FFV) plays an indispensable role in governing polymer properties and performance. However, the design of new high-FFV polymers is challenging. In this study, we report a data-driven approach and aim to accelerate the discovery of high-FFV polymers. First, a computational method is proposed to calculate FFV, and a two-step fragmentation method is developed to construct a fragment library for digital representation of polymer structures. Data mining is employed to identify promising fragments for high FFV. Subsequently, machine learning (ML) models are trained using a data set with 1683 polymers and their excellent transferability is demonstrated by out-of-sample predictions in another data set with 11,479 polymers. Finally, the ML models are used to screen ∼1 million hypothetical polymers, and 29,482 polymers with FFV > 0.2 are shortlisted; representative high-FFV polymers are validated by molecular simulations, and design strategies are highlighted. To further facilitate the discovery of new high-FFV polymers, we develop an online interactive platform https://ffv-prediction.herokuapp.com, which allows for rapid FFV predictions, given polymer structures. The data-driven approach in this study might advance the development of new high-FFV polymers and further explore quantitative structure-property relationships for polymers.

Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review

Membrane fouling is one of major obstacles in the application of membrane technologies. Accurately predicting or simulating membrane fouling behaviours is of great significance to elucidate the fouling mechanisms and develop effective measures to control fouling. Although mechanistic/mathematical models have been widely used for predicting membrane fouling, they still suffer from low accuracy and poor sensitivity. To overcome the limitations of conventional mathematical models, artificial intelligence (AI)-based techniques have been proposed as powerful approaches to predict membrane filtration performance and fouling behaviour. This work aims to present a state-of-the-art review on the advances in AI algorithms (e.g., artificial neural networks, fuzzy logic, genetic programming, support vector machines and search algorithms) for prediction of membrane fouling. The working principles of different AI techniques and their applications for prediction of membrane fouling in different membrane-based processes are discussed in detail. Furthermore, comparisons of the inputs, outputs, and accuracy of different AI approaches for membrane fouling prediction have been conducted based on the literature database. Future research efforts are further highlighted for AI-based techniques aiming for a more accurate prediction of membrane fouling and the optimization of the operation in membrane-based processes.

Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization

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.

Health and environmental safety of nanomaterials: O Data, Where Art Thou?

Nanotechnology keeps drawing attention due to the great tunable properties of nanomaterials in comparison to their bulk conventional materials. The growth of nanotechnology in combination with the digitization era has led to an increased need of safety related data. In addition to safety, new data-driven paradigms on safe and sustainable by design materials are stressing the necessity of data even more. Data is a fundamental asset to the scientific community in studying and analysing the entire life-cycle of nanomaterials. Unfortunately, data exist in a scattered fashion, in different sources and formats. To our knowledge, there is no study focusing on aspects of actual data-structure knowledge that exists in literature and databases. The purpose of this review research is to transparently and comprehensively, display to the nanoscience community the datasets readily available for machine learning purposes making it convenient and more efficient for the next users such as modellers or data curators to retrieve information. We systematically recorded the features and descriptors available in the datasets and provide synopsised information on their ranges, forms and metrics in the supplementary material.

Tailored design of nanofiltration membranes for water treatment based on synthesis-property-performance relationships

Tailored design of high-performance nanofiltration (NF) membranes is desirable because the requirements for membrane performance, particularly ion/salt rejection and selectivity, differ among the various applications of NF technology ranging from drinking water production to resource mining. However, this customization greatly relies on a comprehensive understanding of the influence of membrane fabrication methods and conditions on membrane properties and the relationships between the membrane structural and physicochemical properties and membrane performance. Since the inception of NF, much progress has been made in forming the foundation of tailored design of NF membranes and the underlying governing principles. This progress includes theories regarding NF mass transfer and solute rejection, further exploitation of the classical interfacial polymerization technique, and development of novel materials and membrane fabrication methods. In this critical review, we first summarize the progress made in controllable design of NF membrane properties in recent years from the perspective of optimizing interfacial polymerization techniques and adopting new manufacturing processes and materials. We then discuss the property-performance relationships based on solvent/solute mass transfer theories and mathematical models, and draw conclusions on membrane structural and physicochemical parameter regulation by modifying the fabrication process to improve membrane separation performance. Next, existing and potential applications of these NF membranes in water treatment processes are systematically discussed according to the different separation requirements. Finally, we point out the prospects and challenges of tailored design of NF membranes for water treatment applications. This review bridges the long-existing gaps between the pressing demand for suitable NF membranes from the industrial community and the surge of publications by the scientific community in recent years.

Predicting antibiotic resistance gene abundance in activated sludge using shotgun metagenomics and machine learning

While the microbiome of activated sludge (AS) in wastewater treatment plants (WWTPs) plays a vital role in shaping the resistome, identifying the potential bacterial hosts of antibiotic resistance genes (ARGs) in WWTPs remains challenging. The objective of this study is to explore the feasibility of using a machine learning approach, random forests (RF's), to identify the strength of associations between ARGs and bacterial taxa in metagenomic datasets from the activated sludge of WWTPs. Our results show that the abundance of select ARGs can be predicted by RF's using abundant genera (Candidatus Accumulibacter, Dechloromonas, Pesudomonas, and Thauera, etc.), (opportunistic) pathogens and indicators (Bacteroides, Clostridium, and Streptococcus, etc.), and nitrifiers (Nitrosomonas and Nitrospira, etc.) as explanatory variables. The correlations between predicted and observed abundance of ARGs (erm(B), tet(O), tet(Q), etc.) ranged from medium (0.400 < R² < 0.600) to strong (R² > 0.600) when validated on testing datasets. Compared to those belonging to the other two groups, individual genera in the group of (opportunistic) pathogens and indicator bacteria had more positive functional relationships with select ARGs, suggesting genera in this group (e.g., Bacteroides, Clostridium, and Streptococcus) may be hosts of select ARGs. Furthermore, RF's with (opportunistic) pathogens and indicators as explanatory variables were used to predict the abundance of select ARGs in a full-scale WWTP successfully. Machine learning approaches such as RF's can potentially identify bacterial hosts of ARGs and reveal possible functional relationships between the ARGs and microbial community in the AS of WWTPs.

Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers

Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the properties–structure–ingredients–process relationship of the different products and the necessity to discover and develop constantly and quickly new molecules and materials with tailor-made properties. In recent years, artificial intelligence (AI) and machine learning (ML) methods have gained increasing attention due to their performance in tackling particularly complex problems in various areas, such as computer vision and natural language processing. As such, they present a specific interest in addressing the complex challenges of CPE. This article provides an updated review of the state of the art regarding the implementation of ML techniques in different types of CPE problems with a particular focus on four specific domains, namely the design and discovery of new molecules and materials, the modeling of processes, the prediction of chemical reactions/retrosynthesis and the support for sensorial analysis. This review is further completed by general guidelines for the selection of an appropriate ML technique given the characteristics of each problem and by a critical discussion of several key issues associated with the development of ML modeling approaches. Accordingly, this paper may serve both the experienced researcher in the field as well as the newcomer.

2D Metal-Organic Framework-Based Thin-Film Nanocomposite Membranes for Reverse Osmosis and Organic Solvent Nanofiltration

Metal-organic frameworks (MOFs) are promising candidates for membrane-based liquid separations due to their intrinsic microporosity, but many are limited by their insufficient stability. In this work, a copper-benzoquinoid (Cu-THQ) MOF was synthesized and demonstrated structural stability in water and organic solvents. After incorporation into the polyamide layer, the hydrophilicity of the membranes was enhanced. The resultant thin-film nanocomposite (TFN) membranes broke the permeability-selectivity tradeoff by showing 242 % increase in water permeance and slightly enhanced salt rejection at MOF loading of 0.0192 mg cm^-2 . The underlying mechanism was probed by different chemical and morphological characterizations. The membranes also showed improved tolerance to chlorine oxidation. With their excellent stability, the Cu-THQ MOF-based membranes further demonstrated impressive performance in organic solvent nanofiltration involving dimethylformamide.