Layer-by-layer aided β-cyclodextrin nanofilm for precise organic solvent nanofiltration

Machine learning for layer-by-layer nanofiltration membrane performance prediction and polymer candidate exploration

In this study, machine learning-based models were established for layer-by-layer (LBL) nanofiltration (NF) membrane performance prediction and polymer candidate exploration. Four different models, i.e., linear, random forest (RF), boosted tree (BT), and eXtreme Gradient Boosting (XGBoost), were formed, and membrane performance prediction was determined in terms of membrane permeability and selectivity. The XGBoost exhibited optimal prediction accuracy for membrane permeability (coefficient of determination (R2): 0.99) and membrane selectivity (R2: 0.80). The Shapley Additive exPlanation (SHAP) method was utilized to evaluate the effects of different LBL NF membrane fabrication conditions on membrane performances. The SHAP method was also used to identify the relationships between polymer structure and membrane performance. Polymers were represented by Morgan fingerprint, which is an effective description approach for developing modeling. Based on the SHAP value results, two reference Morgan fingerprints were constructed containing atomic groups with positive contributions to membrane permeability and selectivity. According to the reference Morgan fingerprint, 204 potential polymers were explored from the largest polymer database (PoLyInfo). By calculating the similarities between each potential polymer and both reference Morgan fingerprints, 23 polymer candidates were selected and could be further used for LBL NF membrane fabrication with the potential for providing good membrane performance. Overall, this work provided new ways both for LBL NF membrane performance prediction and high-performance polymer candidate exploration. The source code for the models and algorithms used in this study is publicly available to facilitate replication and further research. https://github.com/wangliwfsd/LLNMPP/.

Fabrication of polyamide thin film composite membranes using aliphatic tetra-amines and terephthaloyl chloride crosslinker for organic solvent nanofiltration

Given the huge significance of organic solvents in several industrial processes, the use of membranes for recovering the solvents has evolved into an industrially viable process. The current work has been focused on studying the effect of minor changes in the chemistry of the reacting monomers on the organic solvent nanofiltration/solvent resistance nanofiltration (OSN/SRNF) performance of the membranes. The two aliphatic amines with varying aliphatic chain lengths between primary and secondary amines were selected for this purpose. Based on the structure of the resultant active layer, the Janus nanofiltration performance of the membrane was evaluated. The two membranes, 4A-TPC@crosslinked PAN and 4A-3P@crosslinked PAN were fabricated by using two different tetra-amines, 4A (N,N'-bis(3-aminopropyl)ethylenediamine) and 4A-3P (N,N'-Bis(2-aminoethyl)-1,3-propanediamine) crosslinked with terephthaloyl chloride (TPC) on a crosslinked polyacryonitrile (PAN) support through interfacial polymerization (IP). The presence of multiple hydrophobic -CH₂- groups in the structures of the aliphatic amines 4A and 4A-3P develops hydrophobic sites in the hydrophilic polyamide active layers of the membranes. In addition, 4A has two secondary amino groups separated by ethylene (-CH₂-CH₂-) groups, whereas in 4A-3P, the two secondary amino groups are separated by propylene (-CH₂-CH₂-CH₂-) leading to variation in the structural features and performance of the two membranes. Both membranes were fully characterized by several membrane characterization techniques and applied for OSN/SRNF using both polar (methanol, ethanol, and isopropanol) and non-polar (n-hexane and toluene) solvents. Different dyes (Congo red, Eriochrome black T, and Methylene blue) were used as model solutes during the filtration experiment. The 4A-3P-TPC@crosslinked PAN showed n-hexane and toluene flux of 109.9 LMH and 95.5 LMH, respectively. The Congo red (CR) showed the highest rejection, reaching 99.1% for the 4A-TPC@Crosslinked PAN membrane and 98.8% for the 4A-3P-TPC@Crosslinked PAN membrane.

Cold plasma-assisted regeneration of biochar for dye adsorption

Environmental remedies, including adsorption-based water purification, are now being asked to meet the requirement for a low-carbon circular economy requiring low energy and low material consumption. In this regard, we tested the possibility of regenerating adsorbents via cold plasma (CP) treatment for less use of adsorbents and no washing solution. In the adsorption of methylene blue (MB) using carbonized rice husk (CRH) and five successive regeneration cycles by CP treatment, the removal efficiencies were maintained at a moderate level (∼70% of the initial performance), unlike five consecutive adsorption without CP treatment (∼9-13% of the initial performance). The regeneration of CRH by CP treatment was also double-checked by the FESEM, EDS, BET, FTIR, XPS, and surface zeta potential measurements. The successfully recovered adsorption capability is related to the remediation of adsorption sites. It is also worth noting that the required power consumption for recycling by CP treatment was about 6.4 times lower than carbonizing new rice husks. This work provides insights into recovering adsorbents using CP without rigorous, costly, and energy-intensive processes.

Nanocomposite membranes for organic solvent nanofiltration: Recent advances, challenges, and prospects

Organic solvent nanofiltration (OSN) is an emerging technology for the separation of organic solvents that are relevant to the petrochemical, pharmaceutical, food and fine chemical industries. The separation performance of OSN membranes has continued to push the boundary up through advanced membrane fabrication techniques and novel materials for fabricating the membranes. Despite the many advantages, OSN membranes still face such challenges as low solvent permeability and durability in harsh organic solvent conditions. To overcome these limitations, attempts have been made to incorporate nanomaterial fillers into OSN membranes to improve their overall performance. This review analyzes the potential and use of nanomaterials for OSN membranes, including covalent organic frameworks (COFs), metal-organic frameworks (MOFs), metal oxides (MOs) and carbon-based materials (CBMs). Recent advances in the state-of-the-art nano-based OSN membranes, in the form of thin-film nanocomposite (TFN) membranes and mixed matrix membranes (MMMs), are reviewed. Moreover, the separation mechanisms of OSN with nano-based membranes are discussed. The challenges faced by these OSN membranes are also elaborated, and recommendations for further research in this field are provided.