Simulation and characterization of novel reverse osmosis membrane prepared by blending polypyrrole coated multiwalled carbon nanotubes for brackish water desalination and antifouling properties using artificial neural networks

Microplastics and dye removal from textile wastewater using MIL-53 (Fe) metal-organic framework-based ultrafiltration membranes

Microplastics (MPs) and other organic matters in textile wastewater have posed a formidable challenge for treatment processes, particularly in the primary stages such as ultrafiltration (UF). UF plays a crucial role in preventing the entry of pollutants into subsequent treatment steps. However, the performance efficiency of UF membranes is compromised by the potential fouling of membrane pores by MPs, dyes and other organic pollutants such as bovine serum albumin (BSA). This study focuses on enhancing UF membrane performance, specifically its antifouling properties, through the development of high-performance membranes using MIL-53(Fe) metal-organic framework (MOF) particles (noted as MIL-53 here). Various concentrations of the MIL-53 (0.05, 0.1, 0.2, and 0.5 wt%) were integrated into the membrane structure through phase inversion process. Streaming zeta potential results confirmed the negatively charged surface of the membranes and their high hydrophilicity was validated through contact angle analysis. FTIR, SEM, EDS, and XRD confirmed the presence of MIL-53 particles on the surface of membranes. The developed membranes were tested for 24 h to assess their antifouling properties, with a subsequent 30-min hydraulic flush to measure their flux recovery ratios. Methylene Blue (MB) dye was used as a cationic dye present in textile wastewater to evaluate the efficiency of the developed membranes in dye removal and the synergistic effects of dye rejection in the presence of organic matters (i.e., MPs and BSA). Since previous studies have not fully addressed the combination of dyes and organic matter, this study thoroughly investigated the effect of particle-type foulants (MPs) and their interactions with dye (MB), as well as water soluble protein-type foulants (BSA) and their interaction with MB. The results indicated that the developed membranes exhibited higher MB rejection when the dye was present with either MP or BSA, along with improved antifouling properties. The optimised UF membrane integrated with 0.1 wt% MIL-53 demonstrated nearly 96% BSA rejection and around 86% MB rejection in the mixed foulant case (BSA-MB). The modified membrane exhibited a substantial increase in water flux from 176 L m-2.h-1 to 327 L m-2.h-1. The findings of this research show the potential of iron-based MOFs in improving the performance of UF membranes and provide a platform for future studies on significant areas such as long-term stability studies and testing with other pollutants found in textile wastewater.

Dual modification of reverse osmosis membranes with NH2-MIL-125 and functionalised multiwalled carbon nanotubes for enhanced nanoplastic removal

The present study describes a novel double-modified strategy for developing high-performance thin-film composite reverse osmosis (TFC-RO) membranes by incorporating titanium-based metal organic frameworks (NH2-MIL-125) and functionalised multiwalled carbon nanotubes (MWCNTs) into the support layer and selective layer, respectively. Initially, the support layer was subjected to successive modifications using NH2-MIL-125 mixed with polysulfone (PSF) in dimethylformamide DMF solution to investigate their impact on the performance and properties of the support layer and resultant TFC-RO membranes. Results indicated that the new structure of the modified support layer had significant influences on the developed TFC-RO membranes. Notably, the pristine PSF support exhibited a large surface pore size, medium porosity, and strong hydrophobicity, resulting in a low-flux TFC-RO membrane. However, after modification with NH2-MIL-125, the optimal blend support demonstrated a small surface pore size, high porosity, and improved hydrophilicity, favouring the formation of a high performance TFC-RO membrane. The incorporation of functionalised MWCNTs nanochannels into the selective layer, using the optimal NH2-MIL-125-PSF blended support, resulted in a smoother and more hydrophilic TFC-RO membrane with enhanced negative charge to improve antifouling properties against negative foulants (i.e., nanoplastics (NPs) and bovine serum albumin (BSA)). The double-modified membrane (TFC-RO-DM) exhibited superior performance over the conventional PSF-TFC-RO membrane. Notably, the maximum water flux reached 39 L m-2.h-1 with 98.4% NaCl rejection. The membrane exhibited a high flux recovery rate of 92% following a 30-min physical cleaning process. Additionally, the TFC-RO-DM membrane displayed reduced fouling against NPs suggesting the great promise of this innovative double-modification approach for the advancement of high-performance TFC-RO membranes.

Artificial neural network modeling for the prediction, estimation, and treatment of diverse wastewaters: A comprehensive review and future perspective

The application of artificial neural networks (ANNs) in the treatment of wastewater has achieved increasing attention, as it enhances the efficiency and sustainability of wastewater treatment plants (WWTPs). This paper explores the application of ANN-based models in WWTPs, focusing on the latest published research work, by presenting the effectiveness of ANNs in predicting, estimating, and treatment of diverse types of wastewater. Furthermore, this review comprehensively examines the applicability of the ANNs in various processes and methods used for wastewater treatment, including membrane and membrane bioreactors, coagulation/flocculation, UV-disinfection processes, and biological treatment systems. Additionally, it provides a detailed analysis of pollutants viz organic and inorganic substances, nutrients, pharmaceuticals, drugs, pesticides, dyes, etc., from wastewater, utilizing both ANN and ANN-based models. Moreover, it assesses the techno-economic value of ANNs, provides cost estimation and energy analysis, and outlines promising future research directions of ANNs in wastewater treatment. AI-based techniques are used to predict parameters such as chemical oxygen demand (COD) and biological oxygen demand (BOD) in WWTP influent. ANNs have been formed for the estimation of the removal efficiency of pollutants such as total nitrogen (TN), total phosphorus (TP), BOD, and total suspended solids (TSS) in the effluent of WWTPs. The literature also discloses the use of AI techniques in WWT is an economical and energy-effective method. AI enhances the efficiency of the pumping system, leading to energy conservation with an impressive average savings of approximately 10%. The system can achieve a maximum energy savings state of 25%, accompanied by a notable reduction in costs of up to 30%.

Bridging Nanomanufacturing and Artificial Intelligence-A Comprehensive Review

Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution-Industry 4.0-as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed.

Recent advances in surface tailoring of thin film forward osmosis membranes: A review

The recent advancements in fabricating forward osmosis (FO) membranes have shown promising results in desalination and water treatment. Different methods have been applied to improve FO performance, such as using mixed or new draw solutions, enhancing the recovery of draw solutions, membrane modification, and developing FO-hybrid systems. However, reliable methods to address the current issues, including reverse salt flux, fouling, and antibacterial activities, are still in progress. In recent decades, surface modification has been applied to different membrane processes, including FO membranes. Introducing nanochannels, bioparticles, new monomers, and hydrophilic-based materials to the surface layer of FO membranes has significantly impacted their performance and efficiency and resulted in better control over fouling and concentration polarization (CP) in these membranes. This review critically investigates the recent developments in FO membrane processes and fabrication techniques for FO surface-layer modification. In addition, this study focuses on the latest materials and structures used for the surface modification of FO membranes. Finally, the current challenges, gaps, and suggestions for future studies in this field have been discussed in detail.

MOF@Polydopamine-incorporated membrane with high permeability and mechanical property for efficient fouling-resistant and oil/water separation

Metal organic frameworks (MOFs) have demonstrated great potential for their favorable impacts on the performance of water treatment membranes. Herein, the novel nanoparticles based on both nanoporous MOFs and organic PDA layer was exploited as a novel dopant for the fabrication of PES ultrafiltration (UF) membranes. The PDA was synthesized via oxidative self-polymerization under alkaline conditions and formed adhesive coatings on dispersed MOF. The properties of resulting membranes on the porosity, membrane morphology, hydrophilicity, permeability and anti-fouling performance were adequately investigated. The membranes incorporated with MOF@PDA exhibited exceptionally high permeability (209.02 L m-2·h-1), which is approximately 6 times higher than that of the pure PES membrane, and high BSA rejection (99.12%). Notably, the mechanical property and hydrophilicity of the PES membrane were both enhanced by MOF@PDA, and it has been demonstrated that greater hydrophilicity prevents fouling under practical conditions, which results in significant improvements in flux recovery ratio (FRR) (82%). In addition, the modified PES membranes were used to purify the oil/water emulsion, and the results indicates that the membranes have high permeability and rejection of oil/water emulsion, showing its great promise in practical oily sewage remediation.

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.

Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art

Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system's properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted.

Proteoliposome-Incorporated Seawater Reverse Osmosis Polyamide Membrane: Is the Aquaporin Water Channel Effect in Improving Membrane Performance Overestimated?

The water channel feature of the aquaporin (AQP) is considered to be the key in improving the permselectivity of AQP-based thin-film composite (TFC) polyamide (PA) membranes, yet much less attention has been paid to the physicochemical property changes of the PA layer induced by AQP-reconstituted proteoliposomes. This study systematically investigated the roles of proteoliposome constituents (liposome/detergent/AQP) in affecting the physicochemical properties and performance of the membranes. For the first time, we demonstrated that the constituents in the proteoliposome could facilitate the formation of a PA layer with enlarged protuberances and thinner crumples, resulting in a 79% increase in effective surface area and lowering of hydraulic resistance for filtration. These PA structural changes of the AQP-based membrane were found to contribute over 70% to the water permeability increase via comparing the separation performance of the membranes prepared with liposome, detergent, and proteoliposome, respectively, and one proteoliposome-ruptured membrane. The contribution from the AQP water channel feature was about 27% of water permeability increase in the current study, attributed to only ∼20% vesicle coverage in the PA matrix, and this contribution may be easily lost as a result of vesicle rupture during the real seawater reverse osmosis process. This study reveals that the changed morphology dominates the performance improvement of the AQP-based PA membrane and well explains why the actual AQP-based PA membranes cannot acquire the theoretical water/salt selectivity of a biomimetic AQP membrane, deepening our understanding of the AQP-based membranes.

A Neural Network Based Superstructure Optimization Approach to Reverse Osmosis Desalination Plants

An ever-growing population together with globally depleting water resources pose immense stresses for water supply systems. Desalination technologies can reduce these stresses by generating fresh water from saline water sources. Reverse osmosis (RO), as the industry leading desalination technology, typically involves a complex network of membrane modules that separate unwanted particles from water. The optimal design and operation of these complex RO systems can be computationally expensive. In this work, we present a modeling and optimization strategy for addressing the optimal operation of an industrial-scale RO plant. We employ a feed-forward artificial neural network (ANN) surrogate modeling representation with rectified linear units as activation functions to capture the membrane behavior accurately. Several ANN set-ups and surrogate models are presented and evaluated, based on collected data from the H2Oaks RO desalination plant in South-Central Texas. The developed ANN is then transformed into a mixed-integer linear programming formulation for the purpose of minimizing energy consumption while maximizing water utilization. Trade-offs between the two competing objectives are visualized in a Pareto front, where indirect savings can be uncovered by comparing energy consumption for an array of water recoveries and feed flows.

Performance evaluation of whey flux in dead-end and cross-flow modes via convolutional neural networks

Effluent originating from cheese production puts pressure onto environment due to its high organic load. Therefore, the main objective of this work was to compare the influence of different process variables (transmembrane pressure (TMP), Reynolds number and feed pH) on whey protein recovery from synthetic and industrial cheese whey using polyethersulfone (PES 30 kDa) membrane in dead-end and cross-flow modes. Analysis on the fouling mechanistic model indicates that cake layer formation is dominant as compared to other pore blocking phenomena evaluated. Among the input variables, pH of whey protein solution has the biggest influence towards membrane flux and protein rejection performances. At pH 4, electrostatic attraction experienced by whey protein molecules prompted a decline in flux. Cross-flow filtration system exhibited a whey rejection value of 0.97 with an average flux of 69.40 L/m²h and at an experimental condition of 250 kPa and 8 for TMP and pH, respectively. The dynamic behavior of whey effluent flux was modeled using machine learning (ML) tool convolutional neural networks (CNN) and recursive one-step prediction scheme was utilized. Linear and non-linear correlation indicated that CNN model (R² - 0.99) correlated well with the dynamic flux experimental data. PES 30 kDa membrane displayed a total protein rejection coefficient of 0.96 with 55% of water recovery for the industrial cheese whey effluent. Overall, these filtration studies revealed that this dynamic whey flux data studies using the CNN modeling also has a wider scope as it can be applied in sensor tuning to monitor flux online by means of enhancing whey recovery efficiency.

Recent Achievements and Future Challenges in Nanoscience and Nanotechnology

The article presents the investigation results of the formation and synthesis of nanosized materials which were obtained at the Institute of Combustion Problems, many works have been brought to practical use. Investigations of low-temperature soot formation become the basis of nanomaterial synthesis methods, developed at the Institute for Combustion Problems since 1985. Flame can be considered as a chemical reactor to produce target products. The main feature of the processes based on technological combustion is that the target product is formed as a result of the combustion reaction, occurring spontaneously at high temperatures with a high speed without consuming external energy, i.e. due to its own heat. With the development of nanotechnology, new challenges have emerged in the synthesis of nanomaterials under combustion synthesis conditions. Below is a list of works on nanomaterials synthesis carried out at the Institute: complete scheme of soot formation; energy intensive nanocarbon materials; development and study of perovskite photocatalysts for hydrogen evolution; obtaining carbon fibers by the method of electrospinning; obtaining of biologically soluble membranes based on polymeric nanofibres and hydroxyapatite of calcium; synthesis of nanocarbon sorbents for purification of water from heavy metal ions.

Recent Advances in Applications of Carbon Nanotubes for Desalination: A Review

As a sustainable, cost-effective and energy-efficient method, membranes are becoming a progressively vital technique to solve the problem of the scarcity of freshwater resources. With these critical advantages, carbon nanotubes (CNTs) have great potential for membrane desalination given their high aspect ratio, large surface area, high mechanical strength and chemical robustness. In recent years, the CNT membrane field has progressed enormously with applications in water desalination. The latest theoretical and experimental developments on the desalination of CNT membranes, including vertically aligned CNT (VACNT) membranes, composited CNT membranes, and their applications are timely and comprehensively reviewed in this manuscript. The mechanisms and effects of CNT membranes used in water desalination where they offer the advantages are also examined. Finally, a summary and outlook are further put forward on the scientific opportunities and major technological challenges in this field.