(97c) Elucidating the Mechanisms of Ion Permeation through Sub-Nanometer Graphene Pores: Uncovering Free Energy Barriers Via High-Throughput Molecular Simulations"

Water scarcity has become one of the most pressing environmental challenges for modern society. To address this issue, the effective desalination of saline water is considered a possible solution, given the ample amount of easily accessible seawater.¹ Reverse osmosis (RO) technology with permeable membranes is widely used in water desalination plants, but traditional membranes have a relatively low water flux and high energy cost.^2,3 Atomically thin graphene with a high-density of sub-nanometer pores represents the ideal membrane for ionic and molecular separations.⁴ Graphene has emerged as a promising candidate for RO membranes due to its unique properties, but achieving high performance is hindered by the challenge of controlling the size and distribution of nanopores.^2,4 The presence of a single large nanopore in the graphene membrane can compromise its performance by allowing unwanted solutes to pass through with water.^2-4 Therefore, it is necessary to develop sub-nanometer pores to achieve high water flux and selective solute rejection.^4,5 However, experiments that accurately characterize the distribution of sub-nanometer pores in graphene membranes is limited due to the necessary resolution. The integration of computational materials science and simulation-based research has been instrumental in advancing the development of graphene-based RO membranes.^3,6 By providing precise control over the pore size, atomic-level simulations can couple and excel the design of functional graphene membranes in the laboratory. The precise control of pore size is a critical factor in developing graphene-based RO membranes, which may enable the development of molecular sieves that can separate compounds with atomic-scale differences in size and shape.^3,5,6 Non-equilibrium MD simulations are utilized to investigate water permeation through graphene nanopores, providing insights into water flow rates and guiding the design of efficient water filtration systems.⁷ The simulated graphene nanopore area, pressure, and water flux is validated to water permeation experiments with a varied distribution of nanopore area. Additionally, umbrella sampling simulations and Potential of Mean Force (PMF) profiles are generated to understand the dynamics of sieving chlorine, sodium, and potassium ions in water through varied graphene nanopore area. This collaborative approach of atomically thin graphene nanopore simulations and experiments provides a descriptive sub-nanometer view designed to better understand the complex relationships involved in membrane selectivity of RO technology.

References

1. Mesfin M. Mekonnen, & Arjen Y. Hoekstra (2016). Four billion people facing severe water scarcity. Science Advances, 2(2), e1500323.

2. Meidani, K.; Cao, Z.; Barati Farimani, A. Titanium Carbide MXene for Water Desalination: A Molecular Dynamics Study. ACS Appl. Nano Mater. 2021, 4 (6), 6145–6151. https://doi.org/10.1021/acsanm.1c00944.

3. Cohen-Tanugi, D.; Grossman, J. C. Nanoporous Graphene as a Reverse Osmosis Membrane: Recent Insights from Theory and Simulation. Desalination 2015, 366, 59–70. https://doi.org/10.1016/j.desal.2014.12.046.

4. Cheng, P.; Kelly, M. M.; Moehring, N. K.; Ko, W.; Li, A.-P.; Idrobo, J. C.; Boutilier, M. S. H.; Kidambi, P. R. Facile Size-Selective Defect Sealing in Large-Area Atomically Thin Graphene Membranes for Sub-Nanometer Scale Separations. Nano Lett. 2020, 20 (8), 5951–5959. https://doi.org/10.1021/acs.nanolett.0c01934.

5. Kang, Y.; Zhang, Z.; Shi, H.; Zhang, J.; Liang, L.; Wang, Q.; Ågren, H.; Tu, Y. Na+ and K+ Ion Selectivity by Size-Controlled Biomimetic Graphene Nanopores. Nanoscale 2014, 6 (18), 10666–10672. https://doi.org/10.1039/C4NR01383B.

6. Williams, C. D.; Siperstein, F. R.; Carbone, P. High-Throughput Molecular Simulations Reveal the Origin of Ion Free Energy Barriers in Graphene Oxide Membranes. Nanoscale 2021, 13 (32), 13693–13702. https://doi.org/10.1039/D1NR02169A.

7. M. J. Abraham, T. Murtola, R. Schulz, S. Páll, J. C. Smith, B. Hess, E. Lindahl. “GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers”. SoftwareX, Volumes 1–2, 2015, Pages 19-25. https://doi.org/10.1016/j.softx.2015.06.001.