Effect of graphene oxide quantum dots on the interfacial polymerization of a thin-film nanocomposite forward osmosis membrane: An experimental and molecular dynamics study

Controllable Design of Polyamide Composite Membrane Separation Layer Structures via Metal-Organic Frameworks: A Review

Optimizing the structure of the polyamide (PA) layer to improve the separation performance of PA thin-film composite (TFC) membranes has always been a hot topic in the field of membrane preparation. As novel crystalline materials with high porosity, multi-functional groups, and good compatibility with membrane substrate, metal-organic frameworks (MOFs) have been introduced in the past decade for the modification of the PA structure in order to break through the separation trade-off between permeability and selectivity. This review begins by summarizing the recent progress in the control of MOF-based thin-film nanocomposite (TFN) membrane structures. The review also covers different strategies used for preparing TFN membranes. Additionally, it discusses the mechanisms behind how these strategies regulate the structure and properties of PA. Finally, the design of a competent MOF material that is suitable to reach the requirements for the fabrication of TFN membranes is also discussed. The aim of this paper is to provide key insights into the precise control of TFN-PA structures based on MOFs.

Recent Developments in Nanocomposite Membranes Based on Carbon Dots

Carbon dots (CDs) have aroused colossal attention in the fabrication of nanocomposite membranes ascribed to their ultra-small size, good dispersibility, biocompatibility, excellent fluorescence, facile synthesis, and ease of functionalization. Their unique properties could significantly improve membrane performance, including permeance, selectivity, and antifouling ability. In this review, we summarized the recent development of CDs-based nanocomposite membranes in many application areas. Specifically, we paid attention to the structural regulation and functionalization of CDs-based nanocomposite membranes by CDs. Thus, a detailed discussion about the relationship between the CDs' properties and microstructures and the separation performance of the prepared membranes was presented, highlighting the advantages of CDs in designing high-performance separation membranes. In addition, the excellent optical and electric properties of CDs enable the nanocomposite membranes with multiple functions, which was also presented in this review.

On the evaluating membrane flux of forward osmosis systems: Data assessment and advanced intelligent modeling

As an emerging desalination technology, forward osmosis (FO) can potentially become a reliable method to help remedy the current water crisis. Introducing uncomplicated and precise models could help FO systems' optimization. This paper presents the prediction and evaluation of FO systems' membrane flux using various artificial intelligence-based models. Detailed data gathering and cleaning were emphasized because appropriate modeling requires precise inputs. Accumulating data from the original sources, followed by duplicate removal, outlier detection, and feature selection, paved the way to begin modeling. Six models were executed for the prediction task, among which two are tree-based models, two are deep learning models, and two are miscellaneous models. The calculated coefficient of determination (R2 ) of our best model (XGBoost) was 0.992. In conclusion, tree-based models (XGBoost and CatBoost) show more accurate performance than neural networks. Furthermore, in the sensitivity analysis, feed solution (FS) and draw solution (DS) concentrations showed a strong correlation with membrane flux. PRACTITIONER POINTS: The FO membrane flux was predicted using a variety of machine-learning models. Thorough data preprocessing was executed. The XGBoost model showed the best performance, with an R2 of 0.992. Tree-based models outperformed neural networks and other models.

Molecular modeling of thin-film nanocomposite membranes for reverse osmosis water desalination

The scarcity of freshwater resources is a major global challenge causedby population and economic growth. Water desalination using a reverse osmosis (RO) membrane is a promising technology to supply potable water from seawater and brackish water. The advancement of RO desalination highly depends on new membrane materials. Currently, the RO technology mainly relies on polyamide thin-film composite (TFC) membranes, which suffer from several drawbacks (e.g., low water permeability, permeability-selectivity tradeoff, and low fouling resistance) that hamper their real-world applications. Nanoscale fillers with specific characteristics can be used to improve the properties of TFC membranes. Embedding nanofillers into TFC membranes using interfacial polymerization allows the creation of thin-film nanocomposite (TFNC) membranes, and has become an emerging strategy in the fabrication of high-performance membranes for advanced RO water desalination. To achieve optimal design, it is indispensable to search for reliable methods that can provide fast and accurate predictions of the structural and transport properties of the TFNC membranes. However, molecular understanding of permeability-selectivity characteristics of nanofillers remains limited, partially due to the challenges in experimentally exploring microscopic behaviors of water and salt ions in confinement. Molecular modeling and simulations can fill this gap by generating molecular-level insights into the effects of nanofillers' characteristics (e.g., shape, size, surface chemistry, and density) on water permeability and ion selectivity. In this review, we summarize molecular simulations of a diverse range of nanofillers including nanotubes (carbon nanotubes, boron nitride nanotubes, and aquaporin-mimicking nanochannels) and nanosheets (graphene, graphene oxide, boron nitride sheets, molybdenum disulfide, metal and covalent organic frameworks) for water desalination applications. These simulations reveal that water permeability and salt rejection, as the major factors determining the desalination performance of TFNC membranes, significantly depend on the size, topology, density, and chemical modifications of the nanofillers. Identifying their influences and the physicochemical processes behind, via molecular modeling, is expected to yield important insights for the fabrication and optimization of the next generation high-performance TFNC membranes for RO water desalination.

Improved Forward Osmosis Performance of Thin Film Composite Membranes with Graphene Quantum Dots Derived from Eucalyptus Tree Leaves

The major challenges in forward osmosis (FO) are low water flux, high specific reverse solute flux (SRSF), and membrane fouling. The present work addresses these problems by the incorporation of graphene quantum dots (GQDs) in the polyamide (PA) layer of thin-film composite (TFC) membranes, as well as by using an innovative polyethersulfone nanofiber support for the TFC membrane. The GQDs were prepared from eucalyptus leaves using a facile hydrothermal method that requires only deionized water, without the need for any organic solvents or reducing agents. The nanofiber support of the TFC membranes was prepared using solution blow spinning (SBS). The polyamide layer with GQDs was deposited on top of the nanofiber support through interfacial polymerization. This is the first study that reports the fouling resistance of the SBS-nanofiber-supported TFC membranes. The effect of various GQD loadings on the TFC FO membrane performance, its long-term FO testing, cleaning efficiency, and organic fouling resistance were analyzed. It was noted that the FO separation performance of the TFC membranes was improved with the incorporation of 0.05 wt.% GQDs. This study confirmed that the newly developed thin-film nanocomposite membranes demonstrated increased water flux and salt rejection, reduced SRSF, and good antifouling performance in the FO process.