Experimental and theoretical characterization of commercial nanofiltration membranes for the treatment of ion exchange spent regenerant

Fluoride Removal Using Nanofiltration-Ranged Polyamide Thin-Film Nanocomposite Membrane Incorporated Titanium Oxide Nanosheets

Drinking water defluoridation has attracted significant attention in the scientific community, from which membrane technology, by exploring thin film nanocomposite (TFN) membranes, has demonstrated a great potential for treating fluoride-contaminated water. This study investigates the development of a TFN membrane by integrating titanium oxide nanosheets (TiO2 NSs) into the polyamide (PA) layer using interfacial polymerization. The characterization results suggest that successfully incorporating TiO2 NSs into the PA layer of the TFN membrane led to a surface with a high negative charge, hydrophilic properties, and a smooth surface at the nanoscale. The TFN membrane, containing 80 ppm of TiO2 NSs, demonstrated a notably high fluoride rejection rate of 98%. The Donnan-steric-pore-model-dielectric-exclusion model was employed to analyze the effect of embedding TiO2 NSs into the PA layer of TFN on membrane properties, including charge density (Xd), the pore radius (rp), and pore dielectric constant (εp). The results indicated that embedding TiO2 NSs increased Xd and decreased the εp by less than the TFC membrane without significantly affecting the rp. The resulting TFN membrane demonstrates promising potential for application in water treatment systems, providing an effective and sustainable solution for fluoride remediation in drinking water.

Quantifying uncertainty in nanofiltration transport models for enhanced metals recovery

To decarbonize our global energy system, sustainably harvesting metals from diverse sourcewaters is essential. Membrane-based processes have recently shown great promise in meeting these needs by achieving high metal ion selectivities with relatively low water and energy use. An example is nanofiltration, which harnesses steric, dielectric, and Donnan exclusion mechanisms to perform size- and charge-based fractionation of metal ions. To further optimize nanofiltration systems, multicomponent models are needed; however, conventional methods necessitate large amounts of data for model calibration, introduce substantial uncertainty into the characterization process, and often yield poor results when extrapolated. In this work, we develop a new computational architecture to alleviate these concerns. Specifically, we develop a framework that: (1) reduces the data requirement for model calibration to only charged species measurements; (2) eliminates uncertainty propagation problems present in conventional characterization processes; (3) enables exploration of pH optimization for enhancing metal ion selectivities; and (4) enables uncertainty quantification to assess the sensitivity of partition coefficients and ion driving forces to learned pore size distributions. Our framework captures eight independent datasets comprising over 500 measurements to within ±15%. Our studies also suggest that the expectation-maximization algorithm can effectively learn pore size distributions and that optimizing pH can improve metal ion selectivities by a factor of 3-10×. Our findings also reveal that image charges appear to play a less pronounced role in dielectric exclusion under the studied conditions and that ion driving forces are more sensitive to pore size distributions than partition coefficients.

Resource recovery from RO concentrate using nanofiltration: Impact of active layer thickness on performance

Modelling the removal of monovalent and divalent ions from seawater via nanofiltration is crucial for pre-treatment in seawater reverse osmosis systems. Effective separation of divalent ions through nanofiltration and allowing the permeate containing only monovalent ions to pass through the reverse osmosis system produces pure NaCl salt from the concentrate. However, the Donnan steric pore model and dielectric exclusion assume a uniformly distributed cylinder pore morphology, which is not representative of the actual membrane structure. This study analyzed the impact of membrane thickness on neutral solute removal and investigated the effect of two different methods for calculating the Peclet number on rejection rates of monovalent and divalent salts. Results show that membrane thickness has a significant effect on rejection rates, particularly for uncharged solutes in the range of 0.5-0.7 solute radius to membrane pore size ratio. Operating pressures above 10 bar favour the use of effective active layer thickness over the membrane pore size to calculate the Peclet number. At low pressures, using the effective active layer can lead to overestimation of monovalent salt rejection and underestimation of divalent salt rejection. This study highlights the importance of appropriate Peclet number calculation methods based on applied pressure when modelling membrane separation performance.

Experimental and Modeling Study of the Nanofiltration of Alcohol-Based Molecules and Amino Acids by Commercial Membranes

The nanofiltration performance of three commercial membranes was analyzed by the Steric Pore Model (SPM) and the extended Nernst-Planck diffusion equation inside membrane pores. The model was completed with the equation to predict the concentration polarization, and the mass transfer coefficient was determined by considering the presence of a feed spacer. The model parameters that characterized the performance of the membrane were the hydrodynamic coefficient, which accounts for the possible variations in solute size and membrane pore radius, the effective membrane thickness, and the water permeability coefficient. All experiments were conducted at fixed feed pH of 6. The rejections of uncharged solutes (glucose for membranes with a high molecular weight cut-off (MWCO) and glycerol and ethylene glycol for membranes with a low MWCO) allowed the model parameters to be determined. We found that glycerol and ethylene glycol overestimate the membrane pore radius due to their ability to interact with the membrane matrix. Therefore, the rejection of glycine as a small amino acid was explored to characterize the membranes with low MWCO since these molecules do not interact with the membrane matrix and have an almost zero charge at pH values between 4.5 and 6.5. Based on the experimental rejections, it was stated that glucose and glycine could be separated by these membranes operating in continuous diafiltration mode.

Lithium Concentration from Salt-Lake Brine by Donnan-Enhanced Nanofiltration

Membranes offer a scalable and cost-effective approach to ion separations for lithium recovery. In the case of salt-lake brines, however, the high feed salinity and low pH of the post-treated feed have an uncertain impact on nanofiltration's selectivity. Here, we adopt experimental and computational approaches to analyze the effect of pH and feed salinity and elucidate key selectivity mechanisms. Our data set comprises over 750 original ion rejection measurements, spanning five salinities and two pH levels, collected using brine solutions that model three salt-lake compositions. Our results demonstrate that the Li⁺/Mg²⁺ selectivity of polyamide membranes can be enhanced by 13 times with acid-pretreated feed solutions. This selectivity enhancement is attributed to the amplified Donnan potential from the ionization of carboxyl and amino moieties under low solution pH. As feed salinities increase from 10 to 250 g L^-1, the Li⁺/Mg²⁺ selectivity decreases by ∼43%, a consequence of weakening exclusion mechanisms. Further, our analysis accentuates the importance of measuring separation factors using representative solution compositions to replicate the ion-transport behaviors with salt-lake brine. Consequently, our results reveal that predictions of ion rejection and Li⁺/Mg²⁺ separation factors can be improved by up to 80% when feed solutions with the appropriate Cl^-/SO₄^2- molar ratios are used.

Prediction of single salt rejection in PES/CMS based membranes

This study explains the modeling of synthesized membranes using the Donnan Steric Pore model (DSPM) based on the Extended Nernst Planck Equation (ENP). Conventionally, structural parameters required to predict the performance of the membranes were determined through tedious experimentation, which in this study are found using a new MATLAB technique. A MATLAB program is used to determine the unknown structural parameters such as effective charge density (X_d), effective pore radius (r_p), and effective membrane thickness to porosity ratio (Δx/A_k) by using the single solute rejection and permeation data. It was found that the model predicted the rejection of studied membranes accurately, with the E5C1 membrane exceeding the others (E5, E5C5) for rejection of single and divalent salt's aqueous solutions. The rejection of 100 ppm aqueous solution of NaCl for E5C1 was around 60%, whereas, for an aqueous solution of 100 ppm, CaCl₂ rejection reached up to 80% at 10 bar feed pressure. The trend of salt rejection for all three membranes was found to be in the following order: E5C1 > E5C5 > E5, confirming that their structural parameters-controlled ion transport in these membranes. The structural parameters, such as effective pore radius, effective membrane thickness to porosity ratio, and effective charge density for the best performing membrane, i.e., E5C1, were determined to be 0.5 nm, 16 μm, and -6.04 mol/m^3,respectively. Finally, it can be asserted that this method can be used to predict the real performance of membranes by significantly reducing the number of experiments previously required for the predictive modeling of nanofiltration-type membranes.

Removal of Copper, Nickel, and Zinc Ions from an Aqueous Solution through Electrochemical and Nanofiltration Membrane Processes

Heavy metal contamination in water is a major health concern, directly related to rapid growth in industrialization, urbanization, and modernization in agriculture. Keeping this in view, the present study has attempted to develop models for the process optimization of nanofiltration (NF) membrane and electrocoagulation (EC) processes for the removal of copper, nickel, and zinc from an aqueous solution, employing the response surface methodology (RSM). The variable factors were feed concentration, temperature, pH, and pressure for the NF membrane process; and time, solution pH, feed concentration, and current for the EC process, respectively. The central composite design (CCD), the most commonly used fractional factorial design, was employed to plan the experiments. RSM models were statistically analyzed using analysis of variance (ANOVA). For the NF membrane, the rejection of Zn, Ni, and Cu was observed as 98.64%, 90.54%, and 99.79% respectively; while the removal of these through the EC process was observed as 99.81%, 99.99%, and 99.98%, respectively. The above findings and a comparison with the conventional precipitation and adsorption processes apparently indicate an advantage in employing the NF and EC processes. Further, between the two, the EC process emerged as more efficient than the NF process for the removal of the studied metals.

Enhanced modelling and experimental validation of ultra-low pressure reverse osmosis membrane system for treatment of synthetic brackish water

Water purification from brackish water sources has been acknowledged as one of the most promising ways to produce drinkable water in water-scarce areas. In this study, an ultra-low pressure reverse osmosis (ULPRO) membrane was numerically and experimentally investigated to produce drinking water by the removal of sodium chloride salt which provides further validation of the model from a practical perspective. An enhanced predictive model based on the Donnan-Steric Pore Model with dielectric exclusion (DSPM-DE) incorporating the osmotic effects was formulated in process simulation. The feed pressure and concentration were optimized as input variables and interaction between them was observed, while salt rejection and water recovery rate were taken as response attributes. The results obtained on the ULPRO membrane showed that the performance depends on the charge, steric, and dielectric effects. Furthermore, the enhanced model was validated with the experimental data attained from a laboratory-scale filtration system with good accuracy in the salt rejection and water recovery results. Comparing the enhanced DSPM-DE with the existing solution diffusion model reveals that the enhanced model predicts the membrane performance better and thereby qualifies itself as a reliable model for desalination of brackish water using ULPRO membrane.

Positively charged nanofiltration membrane synthesis, transport models, and lanthanides separation

The design and understanding of rejection mechanisms for both positively and negatively charged nanofiltration (NF) membranes are needed for the development of highly selective separation of multivalent ions. In this study, positively charged nanofiltration membranes were created via an addition of commercially available polyallylamine hydrochloride (PAH) by conventional interfacial polymerization technique. Demonstration of real increase in surface zeta potential, along with other characterization methods, confirmed the addition of weak basic functional groups from PAH. Both positively and negatively charged NF membranes were tested for evaluating their potential as a technology for the recovery or separation of lanthanide cations (neodymium and lanthanum chloride as model salts) from aqueous sources. Particularly, the NF membranes with added PAH performed high and stable lanthanides retentions, with values around 99.3% in mixtures with high ionic strength (100 mM, equivalent to ~6,000 ppm), 99.3% rejection at 85% water recovery (and high Na+/La3+ selectivity, with 0% Na+ rejection starting at 65% recovery), and both constant lanthanum rejection and permeate flux at even pH 2.7. Donnan steric pore model with dielectric exclusion elucidated the transport mechanism of lanthanides and sodium, proving the potential of high selective separation at low permeate fluxes using positively charged NF membranes.

Tuning the Ion-Selectivity of Thin-Film Composite Nanofiltration Membranes by Molecular Layer Deposition of Alucone

This work addresses a key challenge of tailoring the ion selectivity of a thin-film composite nanofiltration membrane to a specific application, such as water softening, without altering the water permeability. We modified the active surface of a commercial NF270 membrane by molecular layer deposition (MLD) of ethylene glycol-Al (EG-alucone). With increasing deposition cycles, we found that the MLD precursors first infiltrated and deposited in the active layer of NF270, then inflated the active layer, and finally deposited on the surface as a distinct EG-alucone layer. The deposition process changed the morphology of the membrane active layer and decreased the overall density of its fixed negative charge by embedding the positively charged EG-alucone. Filtration experiments revealed that these modifications affected the ion separation properties of the membrane without significantly hindering the water permeability. Specifically, the permeation of Na⁺ increased relative to that of Mg²⁺, as indicated by the permselectivity of Na⁺ salts over Mg²⁺ salts. The changes in permselectivities with an increasing number of MLD cycles were rationalized using the dielectric, steric, and electrostatic ion exclusion mechanisms, which are related to the membrane material, pore size, and fixed charge, respectively. These relations open a path for the rational design of nanofiltration membranes with tailored selectivity by tuning the properties of the MLD layer. Filtration results of natural brackish groundwater using the MLD modified membranes agreed with the single salt experiments. As a result, water hardness was 26% lower for the permeate obtained using the MLD-modified membranes, which were found stable even during a 24 h filtration run. These results highlight the practical potential of this approach in enhancing water softening efficiency.

Characterization of Selected Polymeric Membranes Used in the Separation and Recovery of Palladium-Based Catalyst Systems

Membrane separation processes tender a capable option for energy-demanding separation processes. Nanofiltration (NF) and reverse osmosis (RO) membranes are among the most explored, with a latent use in the chemical industry. In this study, four commercial membranes (NF90, NF270, BW30, and XLE) were investigated for their applicability based on the key structural performance characteristics in the recycling of Pd-based catalysts from Heck coupling post-reaction mixture. Pure water and organic solvent permeabilities, uncharged solute permeability, swelling, and catalyst rejection studies of the membranes were conducted as well as the morphological characterization using Fourier transform infrared, field emission gun scanning electron microscopy, and atomic force microscopy. Characterization results showed trends consistent with the manufactures' specifications. Pure water and organic solvent fluxes generally followed the trend NF270 > NF90 > BW30 > XLE, with the solvent choice playing a major role in the separation process. Pd(PPh₃)₂Cl₂ was well rejected by almost all membranes in 2-propanol; however, XLE rejects Pd(OAc)₂ better at high pressure in acetonitrile. Our study, therefore, revealed that the separation and reuse of the two catalysts by NF90 at 10 bar resulted in 97% and 49% product yields with 52% and 10% catalyst retention for Pd(OAc)₂ while Pd(PPh₃)₂Cl_2. gave 87% and 6% yields with 58% and 36% catalyst retention in the first and second cycles, respectively. Considering, the influence of membrane-solute interactions in Pd-catalyst rejection, a careful selection of the polymeric membrane and solvent, a satisfactory separation, and recovery can be achieved.