Revisiting the alkali hydrolysis of polyamide nanofiltration membranes

Recycling and Degradation of Polyamides

As one of the five major engineering plastics, polyamide brings many benefits to humans in the fields of transportation, clothing, entertainment, health, and more. However, as the production of polyamide increases year by year, the pollution problems it causes are becoming increasingly severe. This article reviews the current recycling and treatment processes of polyamide, such as chemical, mechanical, and energy recovery, and degradation methods such as thermal oxidation, photooxidation, enzyme degradation, etc. Starting from the synthesis mechanism of polyamide, it discusses the advantages and disadvantages of different treatment methods of polyamide to obtain more environmentally friendly and economical treatment schemes. Finding enzymes that can degrade high-molecular-weight polyamides, exploring the recovery of polyamides under mild conditions, synthesizing environmentally degradable polyamides through copolymerization or molecular design, and finally preparing degradable bio-based polyamides may be the destination of polyamide.

Are Hansen solubility parameters relevant in predicting the post-treatment effect on polyamide-based TFC membranes?

This study investigates the impact of solvent post-treatment on polyamide-based thin film composite (TFC) membranes, specifically examining the effect on commercial nanofiltration (NF) and reverse osmosis (RO) membranes. Na2SO4 rejection and increase in pure water permeance (PWP) were considered as the output parameters. The disparity in Hansen solubility parameters (HSP) between the post-treatment solution and the polyamide layer of the TFC membrane, denoted by Ra, is well adapted to understand the enhancement in water permeance through the membranes upon treatment. Aqueous solutions of dimethylformamide with a Ra value of 4, acetonitrile with a Ra value of 8.3, and ethanol with a Ra value of 12.7 were used as the post-treatment solutions. Our experimental design, based on the Box-Behnken design of Response Surface Methodology, incorporates variables such as the concentration of the solvent in the solution (% v/v), Ra value, and treatment time (s). Our findings demonstrate that the effect of post-treatment on the TFC membranes is not governed by the Ra value. Notably, while the post-treatment with the aqueous solution of acetonitrile, 80% v/v for 30 s, had considerable effects on NF membranes (124.5% enhancement in PWP; reduction of 3.5% in Na2SO4 rejection), its impact on RO membranes was negligible. Several factors explain this discrepancy, including the limitations of the HSP model for composite polymers, the inaccuracy of the PWP or salt rejection as a swelling indicator, variations in the HSP values of the polyamide layers for different membranes, and possible modifications in the interface between the support membrane and the polyamide layer. In summary, our study provides insights into the complex interactions between solvents and composite membranes, indicating that HSP alone is not a decisive factor in predicting post-treatment effects on polyamide-based TFC membranes.

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.

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.

Response Surface Methodology Based Optimization of Test Parameter in Glass Fiber Reinforced Polyamide 66 for Dry Sliding, Tribological Performance

The tribological performance of a glass fiber reinforced polyamide66 (GFRPA66) composite with varying fiber weight percentage (wt.%) [30 wt.% and 35 wt.%] is investigated in this study using a pin-on-disc tribometer. GFRPA66 composite specimens in the form of pins with varying percentages of fiber viz., 30 wt.% and 35 wt.% are fabricated by an injection molding process. Tribological performances, such as coefficient of friction (COF) and the specific wear rate (SWR), are investigated. The factors affecting the wear of GFRPA66 composites [with 30 wt.% and 35 wt.% reinforcements] are identified based on the process parameters such as load, sliding velocity, and sliding distance. Design Expert 13.0 software is used for the experimental data analysis, based on the design of experiments planned in accordance with the central composite design (CCD) of the response surface methodology (RSM) technique. The significance of the obtained results are analyzed using analysis of variance (ANOVA) techniques. To attain minimum SWR and COF, the wear performance is optimized in dry sliding conditions. The analysis of experimental data revealed that SWR and COF increased with increasing load, sliding velocity, and sliding distance for GFRPA66 [30 wt.%], but decreased with increasing polyamide weight percentage. The SWR for a maximum load of 80 N, and for a sliding velocity of 0.22 m/s, and a sliding distance of 3500 m for GFRPA66 composite specimens with 30 wt.% reinforcements are found to be 0.0121 m³/Nm, while the SWR for the same set of parameters for GFRPA66 composite specimens with 35 wt.% reinforcements are found to be 0.0102 m³/Nm. The COF for the GFRPA66 composite specimens with 30 wt.% reinforcements for the above set of parameters is found to be 0.37, while the GFRPA66 composite specimens with 35 wt.% reinforcements showed significant improvement in wear performance with a reduction in COF to 0.25. Finally, using a scanning electron microscope (SEM), the worn surfaces of the GFRPA66 are examined and interpreted.