Molecular dynamics simulation of steady-state droplet condensation on a fiber in direct contact membrane distillation settings

Understanding the dynamics of droplet condensation-evaporation behavior on fibers is important for improving the performance of fibrous membranes that are used in water purification applications, e.g., Direct Contact Membrane Distillation (DCMD). DCMD is a promising method of purifying water when low-grade waste heat or renewable energies are available. However, DCMD suffers from low throughput mass flux, and it is also prone to membrane flooding. We used molecular dynamics simulations in this work as the conventional (experimental or computational) methods do not have the required atomistic resolution to reveal the dynamics of water condensation-evaporation on the surface of fibers. Our sim-ulations indicate that vapor flux across the membrane remains constant (with no droplet formation on the fiber) when the fibers' Young-Laplace Contact Angle (YLCA) is greater than a critical value at which condensation is suppressed. However, mass flux decreases with time at lower YLCAs due to the formation and growth of water droplets on the fibers, which could ultimately lead to membrane flooding. We also studied the impact of feed temperature, permeate temperature, fiber diameter, fiber position, and domain size on the fiber critical YLCA. Optimizing these parameters allows the use of a wide array of materials in membrane fabrication, including even hydrophilic materials, while preventing membrane flooding and also enhancing mass flux. In this work, we also present a novel methodology to simulate steady-state droplet condensation-evaporation process in the framework of molecular dynamics simulation, i.e., sim-ulation times >-10 ns, in contrast to the quasi-steady-state simulations (simulation time < 2 ns) reported in most previous studies. Our work demonstrates a simulation platform to study the dynamics of the water condensation-evaporation on fibers and can be used to guide the design of DCMD membranes.(c) 2022 Elsevier B.V. All rights reserved.