2025 AIChE Annual Meeting

(412e) Atomistic Mechanistic Details and Time Series Modeling of Ion and Water Transport in Polyamide Membranes

Authors

Michael Shirts - Presenter, University of Colorado Boulder
Nathanael Schwindt, Rose-Hulman Institute of Technology
Anthony Straub, Yale University
Razi Epsztein, Yale University
High-salinity brine treatment is critical to increase water supplies and minimize waste discharge. Membrane-based desalination processes such as reverse osmosis (RO) and nanofiltration (NF) are being increasingly considered for brine treatment due to their high energy efficiency compared to thermal processes. However, brine treatment exposes membranes to extremely high pressures and salinities that alter membrane transport properties. The underlying mechanisms by which pressure and concentration affect the rejection of ions have not been fully determined.

In this work, we first study the effect of high pressures and salinities on the ion dehydration mechanism using atomistic molecular simulations of ions in aqueous solution and within an RO membrane. We explore these trends for ions of different size, valency, and charge, defining ion dehydration as the removal of one or more waters to create a void in the hydration shell. We thus isolate the energy barrier to strip waters from the energy barrier to fill the hydration shell with another species. This definition ensures our trends are only dependent on solution properties. We show that system pressure in the operating range of RO and NF membranes does not change the free energy of dehydration in solution. However, at high concentration, the free energy to strip waters is reduced. Stripped waters can more easily coordinate with other ions than disrupt the second hydration shell or bulk water structure. We observe the geometry of dehydration within an RO membrane in order to determine how confinement and membrane groups influence the hydration shell. Our results can provide a valuable reference for identifying the primary mechanisms for ion transport in industry-standard membrane materials.

We also construct atomistic models of the membrane to study ion diffusion, and examine the distribution of coordinated species directly and their effects on transport. In order to propagate the dynamics collected from microsecond simulations out to much longer time and length scales, we use time series modeling. Specifically, we apply nonparametric Bayesian time series analysis, more specifically the sticky hierarchical Dirichlet process autoregressive hidden Markov model (HDP-AR-HMM) to solute trajectories generated from long molecular dynamics (MD) simulations in self-assembled lyotropic liquid crystal membranes and dense polymer membranes. We can better understand the mechanisms contributing to selective transport by grouping these dynamical modes identified by the HDP-AR-HMM into clusters based on multiple metrics aimed at distinguishing solute behavior based on their fluctuations, dwell times in each state, and positions within the membrane structure.