Accurate relationships between water diffusivity and chemical–morphological characteristics of membranes are mandatory for membrane material optimizations. To meet this challenge, careful post-processing of previous extensive Molecular Dynamics (MD) simulations was performed in this work to obtain the distribution of water molecules in nanometer-thick polyamide active layers, both at equilibrium and during water permeation. The polyamide active layer was modeled as a linear, uncross-linked TMC–MPD network, representing the low-crosslink-density limit of the FT-30 chemistry. This allowed us to gain insights into molecular-level structures and macroscopic transport patterns. The water volume fractions and Fick diffusivities, extracted from the aforementioned MD simulations, were also used to evaluate the thermodynamic factor accounting for the water activity gradient and the Maxwell–Stefan diffusivity, directly related to the chemical structure of the ultrathin polyamide active layer. Furthermore, three effective-medium-based approaches (Mackie–Meares, Bruggeman, and Maxwell models) were used to predict the water diffusion coefficients using, as for MS coefficients, input derived from MD simulations. Hence the results were compared for a rigorous assessment of the predictive capability of these models. Using the equilibrium membrane hydration, the analysis shows that Bruggeman and Maxwell models provide more accurate predictions compared to the Mackie–Meares model; indeed, the former provide diffusion coefficients in the range of experimental values available in the literature. On the contrary, the Mackie–Meares model yields less accurate values, even compared to diffusivities obtained using the previous molecular dynamics simulations. Ultimately, the results demonstrate that the effective medium-based approaches lose accuracy when transient hydration is used.
Diffusion processes involving membranes are of fundamental importance in both science and technology, since membranes serve as selective interfaces that regulate the transport of mass, charge, and information across multiple length and time scales. In this study, we employ molecular dynamics (MD) simulations to calcula...
Pressure and concentration profiles from non-equilibrium molecular dynamics (NEMD) simulations are often used to infer water transport mechanisms in dense and swollen polymer membranes. However, the mechanical restraints needed to stabilize simulated membranes can alter these profiles and their interpretation. Here, we...
Water in soft electrochemical materials is far more than a passive solvent—it directly governs ionic transport, dielectric response, and structural stability. Unlocking the full potential of hydrogel electrolytes requires mastering the delicate interplay between polymer networks, confined water dynamics, and mobile ion...
Karina Mihilewicz, Katarzyna Bialik-Wąs, Konstantinos N. Raftopoulos· Materials· 0 citations
Abstract Content of image described in text. Filtration flows through nanoporous membranes play a crucial role in a range of cutting-edge technologies, including water purification, osmotic power generation and targeted drug delivery. Molecular dynamics (MD) simulations are currently considered the state-of-the-art app...
K. Wittkowski, M. Chinappi, F. Gallaire et al.· Journal of Fluid Mechanics· 1 citation
Spatial regulation of monomer diffusion during interfacial polymerization (IP) is critical for engineering high-performance polyamide (PA) reverse osmosis membranes yet remains difficult to control with conventional nanomaterial additives. Here, we report a biomimetic diffusion-programming strategy using Mycobacterium...
Wei-Wen Chen, Meng Zhang, Xi Zhang et al.· ACS Applied Materials and In...· 0 citations
Spontaneous imbibition is an important process in enhanced oil recovery, yet its accurate prediction in nanoscale pores remains a significant challenge due to the breakdown of continuum assumptions at the nanoscale. The classical Lucas-Washburn (L-W) equation, which suggests that imbibition length is proportional to th...
Shi-Xun Bai, Ze-Jun Wang, Gang Bai et al.· Processes· 0 citations
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