Ramanish Singh

MFI zeolite nanosheet membranes are promising candidates for ammonia separation from nitrogen and hydrogen gases, yet questions remain on the origin of their high selectivity. Silanols, Si–OH, are present in high concentration at the surface of zeolite nanosheets, and force-field-based simulations indicate that surface adsorption at the silanols contributes to selectivity. Acidic silanol groups can chemically react with ammonia via transfer of a proton to form ammonium ions, which may further contribute to the ability of zeolite nanosheet membranes to separate ammonia from other gases. In this work, we used first-principles molecular dynamics techniques to simulate ammonia’s behavior within stacked MFI zeolite nanosheets. We found that at 523 K and a loading corresponding to 35 bar, conditions desired for the ammonia separation, about 30% of ammonia reacts with surface silanols. Our work explored H-bonding and proton transfer within this system.

MFI zeolite nanosheets membranes are promising candidates for ammonia separation from nitrogen and hydrogen, yet questions remain on the origin of their high selectivity. Silanols, Si-OH, are present in high concentration at the surface of zeolite nanosheets, and force-field-based simulations indicate that surface adsorption at the silanols contributes to selectivity. Silanols can chemically react with ammonia, which may further contribute to the ability of zeolite nanosheet membranes to separate it from other gases. In this work, we use first-principles molecular dynamics techniques to simulate ammonia’s behavior within stacked MFI zeolite nanosheets. We find that at 523 K and a loading corresponding to 35 bar, conditions desired for the ammonia separation, about 30% of ammonia reacts with surface silanols. Our work explores H-bonding and proton transfer within this system.

High concentration water-in-salt electrolyte (WiSE) systems can expand the electrochemical stability window of water, thereby enabling the application of water-based electrolytes in Li-ion batteries. However, the solvation structure and the dynamics of the ions are not yet fully resolved, and prior molecular-mechanics-based molecular dynamics (MMMD) simulation studies present contrasting viewpoints. In the present work, we utilize first-principles molecular dynamics (FPMD) simulations to study the structure and dynamics of high-concentration (10 and 20 m) LiTFSI electrolyte solutions at 298 and 373 K. Although computationally more expensive than the MMMD simulations, the FPMD simulations, in which the forces on the nuclei are obtained from Kohn–Sham density functional theory reflecting the instantaneous arrangement of the electron density, may offer a more accurate representation of WiSE systems where polarization and charge transfer are important. The FPMD simulations demonstrate disruption of the water hydrogen bonding environment and concurrent formation of an anionic network upon increasing the LiTFSI concentration from 10 to 20 m. However, nanoscale spatial heterogeneity is not observed. Analysis of the Li+ cation dynamics obtained from both FPMD and MMMD simulations indicates that ion transport proceeds predominantly via a mixed-mode mechanism, with contributions from both vehicular motion and hopping depending on concentration and temperature.

Molecular simulations are increasingly used to predict thermophysical properties and explore molecular-level phenomena beyond modern imaging techniques. To make these tools accessible to nonexperts, several open-source molecular dynamics (MD) and Monte Carlo (MC) codes have been developed. However, using these tools is challenging, and concerns about the validity and reproducibility of the simulation data persist. In 2017, Schappals et al. reported a benchmarking study involving several research groups independently performing MD and MC simulations using different software to predict densities of alkanes using common molecular mechanics force fields [ J. Chem. Theory Comput. 2017, 4270−4280 28738147 10.1021/acs.jctc.7b00489 ]. Although the predicted densities were reasonably close (mostly within 1%), the data often fell outside of the combined statistical uncertainties of the different simulations. Schappals et al. concluded that there are unavoidable errors inherent to molecular simulations once a certain degree of complexity of the system is reached. The Molecular Simulation Design Framework (MoSDeF) is a workflow package designed to achieve TRUE (Transparent, Reproducible, Usable-by-others, and Extensible) simulation studies by standardizing the implementation of molecular models for various simulation engines. This work demonstrates that using MoSDeF to initialize a simulation workflow results in consistent predictions of system density, even while increasing model complexity.

We analyze the interplay between fluid inertia and thixotropy during the transient start-up dynamics of a fluidity model which captures the rheological behavior of aging soft (glassy) materials, in a rectilinear shear flow upon application of a step shear rate. We observe that when the steady-state flow curve is nonmonotonic, the system shows transient and/or apparent steady-state shear banding in close qualitative agreement with experimental observations. Due to competition between inhomogeneous aging and rejuvenation caused by fluid inertia during start up, we show that there is an apparent steady-state banding at large times (compared to the relevant momentum diffusion timescale) even for shear rates such that the steady state flow curve allows for a homogeneous flow. Thus, for aging soft glassy materials, the shear rate for achieving homogeneous flow is not necessarily given by the steady-state flow curve. We also observe that the transient and apparent steady-state shear banding behavior is not correlated to the negative slope of the stress-strain dependence during the transient. This work also emphasizes that in order to arrive at a realistic description of shear banding behavior in aging (thixotropic) soft materials, inertia may not always be neglected during the start-up dynamics.