The CH4/CO2 Gas Mixture Separation Using the Graphene, SiC, and BN Nanochannels: A Comprehensive Computational Approach
Year: 2025
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide This study presents a comprehensive computational investigation into the separation of CH 4 /CO 2 gas mixtures using nanochannels based on graphene, silicon carbide (SiC), and boron nitride (BN). Molecular dynamics (MD) simulations were employed to evaluate the adsorption dynamics, permeation rates, and separation efficiency of these two-dimensional materials under varying pressures and configurations. Graphene exhibited superior CH 4 permeability due to its atomic thickness and high surface energy, though selectivity was limited by weak interaction with CO 2 . SiC demonstrated moderate gas permeability with enhanced CO 2 selectivity, attributed to its stronger adsorption sites and balanced transport dynamics. BN displayed exceptional impermeability, effectively acting as a molecular sieve for CO 2, driven by its high surface polarity and robust barrier properties. The diffusion coefficients and interaction energies revealed a strong material dependence, with CO 2 exhibiting higher diffusion and adsorption on polar surfaces. At the same time, CH 4 showed reduced mobility due to weaker van der Waals interactions. These findings underscore the significance of nanoscale structural control and surface engineering in optimizing gas separation performance. The insights provided by this study lay a robust foundation for designing hybrid nanochannel architectures, which leverage the unique properties of each material to achieve advanced separation systems. This work holds promise for developing scalable, energy-efficient membranes for clean energy applications, carbon capture, and industrial gas processing.
Source: ACS Omega
Authors: Jafar Azamat, Mahdi Alizadeh, Nima Ajalli
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
Estimated APC paid: $1,935
Funder: Farhangian University
Award: G4906707484
OpenAlex ID: W4412643344
