Density Functional Theory for Computational Studies of Adsorption on Nanomaterials

Density Functional Theory (DFT) has become a fundamental computational technique for investigating adsorption processes on nanomaterials, offering a valuable compromise between accuracy and computational demands. This review covers the theoretical principles of DFT, including key approximations and computational strategies, alongside its applications across various nanostructured materials such as clusters, nanotubes, two-dimensional materials, and porous frameworks. Important insights are presented on adsorption energies, adsorption geometries, electronic structure changes, and charge transfer effects. Additionally, the review discusses how doping and surface modifications can enhance adsorption performance. Key challenges, including functional limitations, the treatment of weak interactions, finite-size effects, and computational resource requirements, are examined. The incorporation of machine learning and multi-scale modeling techniques is also explored as promising routes to improve predictive capabilities and capture dynamic adsorption phenomena. This comprehensive overview highlights the critical role of DFT in guiding the design of nanomaterials with improved adsorption properties for applications in catalysis, sensing, environmental remediation, and energy technologies