The permeation of potassium and chloride ions through nanotubes: a molecular simulation study

Understanding the performance of RHO type zeolite membrane for CH4/N2 separation based on molecular dynamics and deep neural network methods

This study shows a molecular dynamics (MD) simulation study on the performance of the RHO zeolite membrane for separating nitrogen from methane/nitrogen gas mixtures. The contamination of natural gas, predominantly composed of methane, with nitrogen diminishes its value. Zeolite membranes offer promising prospects for gas separation due to their stability, rigid pore structure, and molecular sieving properties. The study investigates the impact of pressure difference (up to 30 MPa), feed composition, and membrane thickness on the separation rate at a system temperature of 298 K. Results demonstrate that the RHO zeolite membrane exhibits high permeability and selectivity for N2 separation, surpassing the upper limit defined by Robson with a maximum permeability of 2.14 × 105 GPU (Gas Permeation Units). Exceptional selectivity of N2 over CH4 molecules is observed. Additionally, altering the feed composition and membrane thickness positively influences the membrane's separation performance, thereby enhancing its efficiency. The findings contribute to the advancement of separation technologies, providing valuable insights into the potential application of zeolite membranes for efficient N2 separation from CH4/N2 gas mixtures in natural gas processing. Furthermore, the study explores the use of Deep Neural Network (DNN) models to predict the membrane's performance under diverse operating conditions. The DNN models, trained using simulation data from MD simulations, exhibit high accuracy with a coefficient of determination (R2) exceeding 0.9, ensuring reliable predictions. The integration of DNN models facilitates the optimization of zeolite membrane-based gas separation systems, improving their design and operation.

Computational Indicator Approach for Assessment of Nanotoxicity of Two-Dimensional Nanomaterials

The increasing growth in the development of various novel nanomaterials and their biomedical applications has drawn increasing attention to their biological safety and potential health impact. The most commonly used methods for nanomaterial toxicity assessment are based on laboratory experiments. In recent years, with the aid of computer modeling and data science, several in silico methods for the cytotoxicity prediction of nanomaterials have been developed. An affordable, cost-effective numerical modeling approach thus can reduce the need for in vitro and in vivo testing and predict the properties of designed or developed nanomaterials. We propose here a new in silico method for rapid cytotoxicity assessment of two-dimensional nanomaterials of arbitrary chemical composition by using free energy analysis and molecular dynamics simulations, which can be expressed by a computational indicator of nanotoxicity (CIN_2D). We applied this approach to five well-known two-dimensional nanomaterials promising for biomedical applications: graphene, graphene oxide, layered double hydroxide, aloohene, and hexagonal boron nitride nanosheets. The results corroborate the available laboratory biosafety data for these nanomaterials, supporting the applicability of the developed method for predictive nanotoxicity assessment of two-dimensional nanomaterials.

Efficient Removal of Heavy Metals from Aqueous Solutions through Functionalized γ-Graphyne-1 Membranes under External Uniform Electric Fields: Insights from Molecular Dynamics Simulations

Carbon-based nanosheet membranes with functionalized pores have great potential as water treatment membranes. In this study, the separation of Hg²⁺ and Cu²⁺ as heavy metal ions from aqueous solutions using a functionalized γ-graphyne-1 nanosheet membrane is investigated by molecular dynamics simulations. The simulation systems consist of a γ-graphyne-1 nanosheet with -COOH or -NH₂ functional groups on the edge of pores placed in an aqueous solution containing CuCl₂ and HgCl₂. An external electric field is applied as a driving force across the membrane for the separation of heavy metal ions using these functionalized pores. The ion-membrane and water molecule-membrane interaction energies, the radial distribution function of cations, the retention time and permeation of ions through the membrane, the density profile of water and ions, and the hydrogen bond in the system are investigated, and these results reveal that the performance of -NH₂-functionalized γ-graphyne-1 is better than that of -COOH-functionalized γ-graphyne-1 in the separation of Cu²⁺, while the Hg²⁺ cations encounter a high energy barrier as they pass through the membrane, especially in the -COOH-functionalized pore, due to their larger ionic radius and the smaller pore size of this membrane.

The effect of an electric field on ion separation and water desalination using molecular dynamics simulations

Using molecular dynamics simulations, we analyze ion separation and water purification through a piston-driven graphene/carbon-nanotube filter in the presence of an external electric field. Three different magnitudes of electric field are applied along the nanotube's axial direction with the goal of separating sodium and chloride ions in a NaCl aqueous solution. For comparison purposes, we also study the same system in zero fields. Our results show that sufficiently large values of the electric field strength greatly improve the ion separation process. At the highest field strength, the theoretical efficiency of the filter in removing salt from water exceeds 95% indicating its applicability in commercial filtration processes to produce fresh water. These results suggest that the proposed set-up can be used to design highly efficient nanostructured membranes for water desalination.

Significant curvature effects of partially charged carbon nanotubes on electrolyte behavior investigated using Monte Carlo simulations

Carbon nanotubes and graphene are among the major nanomaterials in nanoscience and technology. Despite having π electrons, these nanocarbon allotropes have been simply considered as neutral in classical calculations. In this study, the effects of partial charges on graphene and curved interfaces on molecular adsorption were investigated using Monte Carlo simulations of N2 and NaCl aqueous solutions on graphene and carbon nanotubes. The simulated N2 adsorption behavior and adsorption potential on partially charged and non-charged graphene coincided with each other. The adsorption potentials suggested that partially charged graphene attracted Na ions and repelled Cl ions. However, those tendencies were not present in NaCl aqueous solutions on graphene. Conversely, in partially charged carbon nanotube models, a preference for Na ions and repulsion of Cl ions in the internal nanospaces were observed in the adsorption potentials using Monte Carlo simulations. Curved interfaces in the internal nanospaces of carbon nanotubes enhanced these properties, suggesting significant electrostatic interactions in a curved π-conjugated system.

Removal of heavy metals from water through armchair carbon and boron nitride nanotubes: a computer simulation study

Number of heavy metals permeation from the (7,7) CNT and the (7,7) BNNT in the applied voltages.

Water desalination through armchair carbon nanotubes: a molecular dynamics study

Separation of ions from water using armchair carbon nanotubes.