Water Desalination Process Research Articles

Empirical Evaluation and Parametric Optimization of Stepped Versus Traditional Solar Stills using Taguchi’s Methodology

The escalating scarcity of potable water in remote and arid regions necessitates increased reliance on sustainable solutions, notably solar stills. The imperative need to achieve high productivity and peak hour efficiency in these devices is critical to effectively addressing water shortages. This study explored modified solar still designs aimed at improving the productivity and peak-hour efficiency of the water desalination process. The experimental investigations involved various parameters, including water depth (10, 20, and 30 mm), mass flow rate (10, 15, and 20 kg/h), and glass thickness (4, 5, and 6 mm) for both traditional and stepped solar stills. The experimental layout followed an L18 orthogonal array. It was structured with 2<sup>1</sup>×3<sup>3</sup> = 18 combinations, ensuring comprehensive coverage of the factors and levels involved, and the outputs were systematically examined using the Taguchi approach to identify the optimal parameter values. Stepped solar stills have emerged as superior, demonstrating higher peak-hour efficiency and productivity than traditional solar stills. The most influential parameters, ranked by eff ectiveness,were the type of solar still, water depth, glass thickness, and mass fl ow rate. The optimal conditions for achieving maximum productivity (3881 ml) and peak hour effi ciency (22.03%) were identifi ed, including a stepped solar still, 10 mm water depth, 15 kg/h mass fl ow rate, and 4 mm glass thickness. The experimentally measured values were closely aligned with the predicted values, verifying the accuracy of the Taguchi model with minimal error (0.81% in productivity and 0.49% in peak hour effi ciency).

Novel Co-Polyamides Containing Pendant Phenyl/Pyridinyl Groups with Potential Application in Water Desalination Processes.

This study explores the development and evaluation of a novel series of aromatic co-polyamides featuring diverse pendant groups, including phenyl and pyridinyl derivatives, designed for water desalination membrane applications. These co-polyamides, synthesized with a combination of hexafluoroisopropyl, oxyether, phenyl, and amide groups, exhibited excellent solubility in polar aprotic solvents, thermal stability exceeding 350 °C, and the ability to form robust, flexible films. Membranes prepared via phase inversion demonstrated variable water permeability and NaCl rejection rates, significantly influenced by the pendant group chemistry. Notably, pyridinyl-substituted membranes achieved water fluxes up to 17.7 L m-2 h-1 and a NaCl rejection of 37.3%, while phenyl-substituted variants provided insights into the interplay of hydrophobicity and porosity. These findings highlight the critical role of pendant group functionality in tailoring membrane performance, offering a foundation for further structural modifications to enhance efficiency in water treatment technologies.

Application of smart technologies in water management analysis

More and more regions of the world are suffering due to urbanization and emerging industries. Therefore, water extraction and water management become a priority issue globally. Scientists are beginning to implement the most modern technologies and use sustainable development and sustainable construction solutions which would help solve water management problems. The article analyses the application of the most advanced technologies in water management, which increase the efficiency of buildings in the operational phase and help to implement the goals of sustainable development and sustainable construction. The study found that AI (Artificial Intelligence) and IoT (Internet of Things) technologies can be applied in many areas of water management. For example, monitoring and prevention of engineering network accidents, water distribution in engineering networks, wastewater treatment, water desalination processes, selection of efficient water-saving engineering solutions, flood prevention and implementation of sustainable construction. The implementation of the most modern technologies benefit citizens, state institutions, water management companies and the ecosystem.

(Invited) Automation of Electrochemical Water Desalination Processes Using Artificial Intelligence Technologies

Electrochemical desalination technologies, including battery electrode deionization and capacitive deionization (CDI), are being actively studied as alternatives to membrane technology due to their relatively low energy consumption. In line with the development of technology, modeling-based research has also been actively conducted to mathematically simulate and automatically control the technology (Donnan models or Lanmuir models). However, despite the complexity of calculations, these numerical models cannot simulate the entire actual operating environment; therefore, multiple assumptions are required. In addition, even in this simplified environment, predictions for specific parameters (effluent pH, etc.) are often very inaccurate.Therefore, research on big data-based artificial intelligence technology that can overcome the shortcomings of these numerical models has been reported mainly by specific research groups in recent years. First, a study that successfully predicted effluent concentration and pH in CDI using deep learning technology was reported in 2021 yr (i.e., Convolutional Neural Network combined with Long Short-Term Memory), followed by a study based on an ensemble model (i.e., Random Forest) that can overcome the complexity of deep learning models in 2023 yr. In addition, reinforcement learning-based research that can automate processes is also being reported starting in 2022 yr.Therefore, in this presentation, artificial intelligence technologies that can predict specific output variables of electrochemical desalination technology, quantify the correlation between each input variable (xAI), and ultimately enable process automation will be discussed. The advantages and disadvantages of different artificial intelligence technologies will also be discussed from a practical/technical perspective (i.e., response time, computational cost, etc).

(Invited) Comparison of Tree-Based Model with Deep Learning Model for Predicting Effluent pH and Concentration in an Electrochemical Water Desalination Process

Capacitive deionization (CDI) is an emerging water treatment technology that offers potential advantages, including reduced environmental impact, simpler operation, and lower energy consumption in certain applications. However, for optimizing the process and ensuring high-quality treated water, accurate prediction of effluent pH and concentration in CDI systems is crucial. Various numerical models developed for simulating CDI performance often struggle to capture the complexities of real-world operating conditions. Recently, artificial intelligence (AI) techniques have been widely employed to overcome these limitations by their capability to model complex nonlinear relationships and adaptively respond to changing conditions. Therefore, this study presents a comprehensive comparison of tree-based models and a deep learning model in predicting the effluent pH and concentration in a CDI system. The study developed and evaluated the performance of a steady random forest (SRF) model, a dynamic random forest (DRF) model, and a convolutional neural network-long short-term memory (CNN-LSTM) model using the same dataset. The models were trained and tested on tabular data from constant current (CC) and constant voltage (CV) MCDI operations.The results showed that the tree-based models and the CNN-LSTM model achieved similar prediction accuracy when used on the operation mode they were trained on, with R2 values exceeding 0.98 for both effluent pH and concentration. However, in generalization tests across different operation modes, the performance of the SRF model without binary features significantly deteriorated (R2 < 0.115). To address this, binary features were incorporated to distinguish between the CC and CV operating modes, which substantially improved the SRF model's generalization ability, with R2 values (model R2 > 0.5) comparable to the CNN-LSTM model.Feature importance analysis revealed that voltage, current, and influent pH were the most influential factors in predicting both effluent pH and concentration, aligning with the characteristics of the CDI process. Notably, the tree-based models demonstrated a significant advantage in computational efficiency, requiring over 100 times less computation time per iteration than the CNN-LSTM model.These findings suggest that tree-based models, particularly the SRF model with binary features, can be a viable and computationally efficient alternative to deep learning models for modeling and optimizing CDI processes. The approach of incorporating binary features to enhance the generalization ability of tree-based models is a practical and cost-effective solution that can be applied to other complex engineering problems. Further research in this area could help reduce modeling complexity and computational costs, offering more straightforward computational modeling methods for CDI processes.

The Dynamic Diversity and Invariance of Ab Initio Water.

Comprehending water dynamics is crucial in various fields, such as water desalination, ion separation, electrocatalysis, and biochemical processes. While ab initio molecular dynamics (AIMD) accurately portray water's structure, computing its dynamic properties over nanosecond time scales proves cost-prohibitive. This study employs machine learning potentials (MLPs) to accurately determine the dynamic properties of liquid water with ab initio accuracy. Our findings reveal diversity in the calculated diffusion coefficient (D) and viscosity of water (η) across different methodologies. Specifically, while the GGA, meta-GGA, and hybrid functional methods struggle to predict dynamic properties under ambient conditions, methods on the higher level of Jacob's ladder of DFT approximation perform significantly better. Intriguingly, we discovered that both D and η adhere to the established Stokes-Einstein (SE) relation for all of the ab initio water. The diversity observed across different methods can be attributed to distinct structural entropy, affirming the applicability of excess entropy scaling relations across all functionals. The correlation between D and η provides valuable insights for identifying the ideal temperature to accurately replicate the dynamic properties of liquid water. Furthermore, our findings can validate the rationale behind employing artificially high temperatures in the simulation of water via AIMD. These outcomes not only pave the path to designing better functionals for water but also underscore the significance of water's many-body characteristics.

Resources optimization using Pareto analysis for sea water desalination plants

This study examines the potential of implementing a systematic approach to piloting desalination plants, with the objective of evaluating energy improvements in water desalination processes. The introduction of new reverse osmosis membranes is imperative at the small-scale level. The findings can be extrapolated to inform the operation of large-scale desalination plants. The objective of this approach is to achieve the optimal water quality standard at the lowest possible cost. In this manner, pilot tests are being conducted prior to the determination of whether to alter the reverse osmosis membranes, as this represents a substantial investment. The objective of these tests is to minimise the risk of making an erroneous decision and to ensure optimal results in terms of energy consumption, operating costs and reduction of environmental impact, while complying with the requisite water quality standards. In this instance, the Pareto analysis is utilised to ascertain the two or three most significant causes whose treatment affects more than 80 % of the potential energy savings to be implemented. This article introduces a novel approach to studying a total of 180 desalination plants at the territorial level in the Canaries.

A conforming mixed finite element method for a coupled Navier–Stokes/transport system modeling reverse osmosis processes

We consider the coupled Navier–Stokes/transport equations with nonlinear transmission conditions, which constitute one of the most common models utilized to simulate a reverse osmosis effect in water desalination processes when considering feed and permeate channels coupled through a semi-permeate membrane. The variational formulation consists of a set of equations where the velocities, the concentrations, along with tensors and vector fields introduced as auxiliary unknowns and two Lagrange multipliers are the main unknowns of the system. The latter are introduced to deal with the trace of functions that do not have enough regularity to be restricted to the boundary. In addition, the pressures can be recovered afterwards by a postprocessing formula. As a consequence, we obtain a nonlinear Banach spaces-based mixed formulation, which has a perturbed saddle point structure. We analyze the continuous and discrete solvability of this problem by linearizing the perturbation and applying the classical Banach fixed point theorem along with the Banach–Nečas–Babuška result. Regarding the discrete scheme, feasible choices of finite element subspaces that can be used include Raviart–Thomas spaces for the auxiliary tensor and vector unknowns, piecewise polynomials for the velocities and the concentrations, and continuous polynomial space of lowest order for the traces, yielding stable discrete schemes. An optimal a priori error estimate is derived, and numerical results illustrating both, the performance of the scheme confirming the theoretical rates of convergence, and its applicability, are reported.

Strategic Optimization of PFC Coagulation for Saline Water Pre-treatment: Enhancing RO Filtration Efficiency in the Mekong Delta

Abstract This study critically evaluates the application of Poly Ferric Chloride (PFC) as a pre-treatment coagulant in desalination, particularly for enhancing Reverse Osmosis (RO) filtration in the saline-affected Mekong Delta region. The research focuses on the optimization of PFC dosages and pH levels to maximize coagulation efficiency in varying salinity conditions. Through an extensive series of experiments, an optimal coagulation efficiency of 93.7% for Total Suspended Solids (TSS) removal was achieved with a PFC dosage of 0.7 ml at a pH of 11 in a 5 ppt saline solution. However, increasing salinity levels were found to complicate this relationship, revealing a nuanced interplay between PFC dosage, pH, and salinity. A particularly critical observation was the decreasing effectiveness of PFC as salinity levels rose, indicating a need for escalating PFC dosages in higher saline waters to sustain coagulation effectiveness. The study significantly contributes to the understanding of PFC pre-treatment in water desalination processes, highlighting the compound’s role in improving the input quality for RO systems and extending membrane longevity. These findings are pivotal for designing adaptive water treatment strategies, ensuring the sustainability and effectiveness of water purification in salinity-vulnerable contexts.

Synthesis of CuO/Fe3O4 Nanocomposite for enhanced solar thermal desalination

Nanoparticle (NPs) have attracted the attention of scientists and researchers in water remediation due to the improvement in its processability, and its cost effectiveness. The present work analyzed the synergistic effect of CuO (NPs) with different ratios (0, 0.5, 0.6, 0.7, 0.8, 0.9 and 1 wt %) on the structural, and optical properties of Fe3O4 (NPs), that can be used in water desalination process. The general methodology for preparation and structural properties of CuO (NPs), Fe3O4 (NPs) and CuO/Fe3O4 (NCs) were analyzed by (XRD) and (TEM) measurements. The morphological combination of (SEM) with (EDX), were used for determining the elemental identification of CuO/Fe3O4 (NCs). The characterization topography was found to be strongly affected by the change of the CuO concentration. Furthermore, the Urbach energy (EU), steepness parameter (σ), electron-phonon interaction (Ee-p), optical band gap (Eg), refractive index (n) and optical conductivity (σopt) were calculated. The optical absorption studies in the UV–Visible region, revealed that the CuO/Fe3O4 (NCs) was found to be direct allowed transition, and the energy bandgap value decreased from 2.52 eV to 2.15 eV based on the percentage change of CuO (NPs) in CuO/Fe3O4 (NCs). Moreover, the value of thermal conductivity (k), and thermal efficiency (η), showed an increase with the increase of the ratio of CuO (NPs), which may be an indication for opening an innovation way that can satisfy the need of seawater desalination technology.

Visible-light-activated Fe2O3–TiO2 nanoparticles enhance biofouling resistance of polyethersulfone ultrafiltration membranes against marine algae Chlorella vulgaris

This study investigated the modification of polyethersulfone (PES) ultrafiltration membranes with TiO2 and Fe2O3–TiO2 nanoparticles to enhance their hydrophilicity and biofouling resistance against the marine microalgae Chlorella vulgaris. It is a common freshwater and marine microalga that readily forms biofilms on membrane surfaces, leading to significant flux decline and increased operational costs in ultrafiltration processes. The microalgae cells and their extracellular polymeric substances (EPS) adhere to the membrane surface, creating a dense fouling layer that impedes water permeation. The modified membranes were characterized using contact angle measurements, scanning electron microscopy, and pure water flux/resistance tests. Short-term ultrafiltration experiments evaluated the membranes’ antifouling performance by measuring flux decline, flux recovery ratio, and relative flux reduction during C. vulgaris filtration. The TiO2 membrane showed improved hydrophilicity and antifouling over the pristine PES membrane, while the Fe2O3–TiO2 nanocomposite membrane exhibited the best performance. It reduced the water contact angle and showed only a 5% relative flux reduction compared to 60% for the pristine membrane. SEM images confirmed reduced microalgal deposition on the nanocomposite surface. Long-term tests with microalgal cells under dark and visible light conditions in saline water further assessed the membranes’ biofouling resistance. The Fe2O3–TiO2 membrane maintained 59 L/m2 h water flux under visible light after immersion in the microalgal solution, outperforming the pristine (38 L/m2 h) and TiO2 (52 L/m2 h) membranes. This superior antifouling was attributed to photocatalytic generation of reactive oxygen species inhibiting microalgal adhesion. This study demonstrates a promising strategy for mitigating biofouling in membrane-based water treatment and desalination processes.

Utilising Artificial Intelligence to Predict Membrane Behaviour in Water Purification and Desalination

Water scarcity is a critical global issue, necessitating efficient water purification and desalination methods. Membrane separation methods are environmentally friendly and consume less energy, making them more economical compared to other desalination and purification methods. This survey explores the application of artificial intelligence (AI) to predict membrane behaviour in water purification and desalination processes. Various AI platforms, including machine learning (ML) and artificial neural networks (ANNs), were utilised to model water flux, predict fouling behaviour, simulate micropollutant dynamics and optimise operational parameters. Specifically, models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and support vector machines (SVMs) have demonstrated superior predictive capabilities in these applications. This review studies recent advancements, emphasising the superior predictive capabilities of AI models compared to traditional methods. Key findings include the development of AI models for various membrane separation techniques and the integration of AI concepts such as ML and ANNs to simulate membrane fouling, water flux and micropollutant behaviour, aiming to enhance wastewater treatment and optimise treatment and desalination processes. In conclusion, this review summarised the applications of AI in predicting the behaviour of membranes as well as their strengths, weaknesses and future directions of AI in membranes for water purification and desalination processes.

Hierarchical Ti3C2Tx MXene@Honeycomb nanocomposite with high energy efficiency for solar water desalination

The utilization of solar-driven interfacial evaporation holds immense promise in enhancing energy efficiency and establishing sustainable methods for seawater desalination and water purification. While designing the materials to achieve high evaporation efficiency, the tuning of materials with porosity and surface chemistry is very crucial. Novel sustainable materials are of great importance for solar water desalination applications since clean water production is utmost important in the current era. There exists a lack of exploration in modifying the surface wettability states of solar evaporators to expedite the vapor generation rates. In this study, we showcase a hydrophilic Ti3C2Tx MXene-coated carbonized honeycomb (CHC) (Ti3C2Tx MXene@CHC) nanocomposite-based hexagonal-shaped evaporator surface. This is the first-time report on the effective utilization of hierarchical CHC for the preparation of solar absorber comprising of Ti3C2Tx MXene@CHC nanocomposite, particularly for the solar water desalination. The Ti3C2Tx MXene@CHC nanocomposite evaporator achieves an impressive water evaporation rate of 1.6 kg m−2 h−1 with 90% efficiency under 1 sun illumination. The augmented thickness of the water layer in the hydrophilic surface of the Ti3C2Tx MXene@CHC nanocomposite helps in facilitating the rapid escape of water molecules. The relatively elongated contact lines in the hydrophobic region simultaneously ensure substantial water evaporation, significantly enhancing the water desalination process. The Ti3C2Tx MXene@CHC nanocomposite exceeds stringent quality benchmarks, signaling its potential for solar water desalination.

Reverse Osmosis Concentrated Water Treatment Technology Research

With the national “double carbon” goal, energy saving and consumption reduction strategic plan to promote, more and more enterprises will be “zero discharge” of wastewater as the ultimate goal of industrial wastewater. Reverse osmosis (RO) process is the most common way of water treatment process, in the treatment of water treatment and pure water preparation at the same time, will be accompanied by a variety of inorganic and organic pollutants contained in the by-products of concentrated water, these difficult to deal with high salt, complex composition of the reverse osmosis concentration of water, not directly discharged without treatment, not only to make a lot of water in the concentration of water to be a waste of water resources, will be a large extent on the ocean, soil and other natural environments to cause irreversible damage. Reversible damage to the ocean, soil and other natural environments. Therefore, it is of great significance to protect the environment by finding an economical and efficient treatment method for RO concentrated water. For the regeneration of fresh water resources of RO concentrated water, there have been many international reports on the development of zero liquid discharge (ZLD) or near-zero liquid discharge technology, through the membrane technology to reverse osmosis concentrated water concentration, water recycling, reduce the volume of concentrated water, and then based on the thermal technology of concentrated water desalination, evaporation and crystallization process leads to the recovery of water, and at the same time, the process of solid by-products after dehydration, purification, can also produce a considerable amount of water. At the same time, the solid by-products produced in the process can be dehydrated and purified, which can also produce considerable economic value of commodities. Concentrated water concentration process around how to achieve low-cost RO concentrated water concentration process, in the disc tube reverse osmosis (DTRO), electrodialysis (ED), membrane distillation (MD) and forward osmosis (FO) and other new membrane concentration technology field carried out a wide range of research on the concentration of further concentrated water to provide more prospects for application. Solidification crystallization through reasonable crystallization control and solid-liquid separation technology, can achieve effective separation and purification of solutes, for various industries to provide solid products in line with the quality requirements, commonly used evaporation crystallization, cooling crystallization, solvent crystallization, ion exchange methods and other ways. Through the study of reverse osmosis concentrated water concentration treatment, crystallization and solidification of the two important links of the production process plan, summarize the practicality of various technologies and advantages and disadvantages, integration of existing technologies, research suitable for reverse osmosis concentrated water resources utilization of economic technology solutions, optimize the treatment process, and ultimately realize the application of engineering examples, is the direction of the efforts of researchers in recent years.

Triple strategies for process salt reduction in industrial wastewater treatment: The case of coking wastewater

Human activities introduce nutrient elements into wastewater, yet the saline byproducts from treatment are poorly managed. Selecting a low-salt process, without compromising treatment efficacy, is essential for reaching the zero-discharge goal in water treatment. Based on the engineering treatment process of coking wastewater, the source and reduction strategy of salts were studied. The results showed that 73.21 % of the residual salt originated from raw coal, 10.87 % from raw water, and 15.92 % from chemicals, respectively. Triple strategies for process salt reduction (PSR): load salt reduction, technology salt reduction and cyclic salt reduction can bring 0.411 mS/cm·m3, 0.217 mS/cm·m3 and −0.070 mS/cm·m3 salt reduction, and also bring 1.455 CNY/m3, 0.836 CNY/m3 and 0.360 CNY/m3 net present value, respectively. Meanwhile, PSR is also a low-carbon strategy, which will bring about carbon emission reduction of 1.6948 kg CO2-eq/m3, 0.4898 kg CO2-eq/m3 and 1.4082 kg CO2-eq/m3 respectively. The Resource/Carbon source Management-Oxic − Hydrolytic & Denitrification − Oxic (A-OHO) process, incorporating PSR, cuts costs by 27.83–31.66 % and carbon emissions by 18.68–19.54 % compared to conventional wastewater treatment methods. It can be predicted that the PSR in the standard treatment stage can provide a guarantee for the subsequent water reuse and desalination process to reduce project investment and operating costs, and at the same time benefit the macro environment from the aspects of carbon emission reduction and sludge reduction.

Ab Initio Simulation of Liquid Water without Artificial High Temperature.

Comprehending the structure and dynamics of water is crucial in various fields, such as water desalination, ion separation, electrocatalysis, and biochemical processes. While reported works show that the ab initio molecular dynamics (AIMD) can accurately portray water's structure, the artificial high temperature (AHT) from 120 to 30 K is needed to mimic the quantum nature of hydrogen-bond network from GGA, metaGGA to hybrid functionals. The AHT proves to be an inadequate approach for systems involving aqueous multiphase mixtures, such as water-solid interfaces and aqueous solutions. This is due to the activation of additional phonons in other phases, which can lead to an overestimation of the dynamics of nearby water molecules. In this work, we find that the regularized SCAN (rSCAN) functional effectively captures both the structure and dynamics of liquid water at ambient conditions without AHT. Moreover, rSCAN closely matches experimental results for the hydration structures of alkali, alkali earth, and halide ions. We anticipate that the versatile and accurate rSCAN functional will emerge as a key tool based on ab initio simulation for investigating chemical processes in aqueous environments.

Biofouling control of thermophilic bacteria in membrane distillation

Previously, fouling of MD membranes was perceived to be minimal due to the operational conditions preventing bacterial growth. However, recent studies presented fouling of MD membranes, largely due to various halophilic and thermophilic bacterial. The current study presented potential of antibacterial cellulose nanocrystals (CNCs) capped silver nanoparticles (AgNPs) towards biofouling control in MD water desalination processes. Interestingly, the CNCs were used as capping and reducing agent in a microwave mediated synthesis of AgNPs. The green synthesis approach of AgNPs and its successful use in MD processes was reported for the first. The MD experiments were carried out using a series of modified PVDF membranes and feed solution spiked with a thermophilic G. stearothermophilus. Upon evaluation of fouled membranes, particularly PVDF, PVP-equipped PVDF, and f-CNTs-modified PVDF, the surface energy of interactions were −131 mJ/m2, −31.5 mJ/m2, −28.6 mJ/m2, suggesting favorable interaction between the membranes and bacterial foulant. The findings of this study were supported by fouling layer and bacterial growth as seen from scanning electron microscopy, atomic force microscopy and Fourier transform infrared results. Upon membrane surface modification using CNCs-capped AgNPs, bacterial growth on membrane surface was reduced. Specifically, the positive cohesion energy between the CNCs-capped AgNPs-modified PVDF membrane and thermophilic bacterial foulant was + 63.2 mJ/m2, distinctly proving reduced fouling. The findings were supported by 5.5 % flux decay of the modified membrane compared to 79.3 % flux decay of virgin PVDF membrane. This suggested a long-term membrane integrity in thermophilic bacterial contaminated feed solutions during the MD water desalination.

Progress in the Sustainable Development of Biobased (Nano)materials for Application in Water Treatment Technologies.

Water pollution remains a widespread problem, affecting the health and wellbeing of people around the globe. While current advancements in wastewater treatment and desalination show promise, there are still challenges that need to be overcome to make these technologies commercially viable. Nanotechnology plays a pivotal role in water purification and desalination processes today. However, the release of nanoparticles (NPs) into the environment without proper safeguards can lead to both physical and chemical toxicity. Moreover, many methods of NP synthesis are expensive and not environmentally sustainable. The utilization of biomass as a source for the production of NPs has the potential to mitigate issues pertaining to cost, sustainability, and pollution. The utilization of biobased nanomaterials (bio-NMs) sourced from biomass has garnered attention in the field of water purification due to their cost-effectiveness, biocompatibility, and biodegradability. Several research studies have been conducted to efficiently produce NPs (both inorganic and organic) from biomass for applications in wastewater treatment. Biosynthesized materials such as zinc oxide NPs, phytogenic magnetic NPs, biopolymer-coated metal NPs, cellulose nanocrystals, and silver NPs, among others, have demonstrated efficacy in enhancing the process of water purification. The utilization of environmentally friendly NPs presents a viable option for enhancing the efficiency and sustainability of water pollution eradication. The present review delves into the topic of biomass, its origins, and the methods by which it can be transformed into NPs utilizing an environmentally sustainable approach. The present study will examine the utilization of greener NPs in contemporary wastewater and desalination technologies.

Composite carbon electrode with a coating of nanostructured, reduced graphene oxide for water electrodialysis

Electrodialysis (ED) and electrodeionization (EDI) are the new methods that are being used in water desalination processes. Reliable, electrochemically stable and efficient electrodes are the crucial components of the ED/EDI electrodialysers. The article proposes a new material for electrodes in electromembrane desalination systems. Graphene composite electrodes were created by bonding carbon fibres with epoxy resin and then coated with a layer of nanostructured, reduced graphene oxide. The graphene electrode material underwent electrochemical characterization by cyclic voltammetry, electrochemical impedance spectroscopy and potentiostatic polarization techniques. FTIR and Raman spectroscopy were used to determine the material’s chemical structure. The change in the surface morphology and elemental composition of the electrodes after fabrication and exploitation of the composite was studied by SEM and EDS. The electrodes were used successfully in multi-electrode electrodialysis devices, resulting in a desalination rate of over 90%. The electrodes were proven to be functional and durable. It was also confirmed that the oxidation/reduction phenomena on the electrode surfaces were fully reversible after changing their polarization, which was used cyclically to clean the electrodialyser. The parameters obtained indicate that this material can also be successfully used in other electrode processes. Graphical

Water desalination, and energy consumption applications of 2D nano materials: hexagonal boron nitride, graphenes, and quantum dots

Abstract In this article, we explore the role of nanotechnology in addressing water scarcity through water desalination. The scope of nanotechnology in water treatment is discussed, emphasizing the potential of 2D nanomaterials such as hexagonal boron nitride (h-BN), graphene, and quantum dots in revolutionizing desalination technologies. Various water desalination techniques, including membrane distillation (MD), solar-powered multi-stage flash distillation (MSF), and multi-effect distillation (MED), are analyzed in the context of nanomaterial applications. The review highlights the energy-intensive nature of conventional water treatment methods and underscores nanomaterials’ potential to enhance efficiency and sustainability in water desalination processes. Challenges facing desalination, such as scalability and environmental impact, are acknowledged, setting the stage for future research directions.