A DFT-based mini-review on adsorbent materials for hydrogen cyanide removal

Abstract

Abstract

Abstract The reduction of hydrogen cyanide (HCN) from the atmosphere remains a major challenge due to its severe toxicity and the limitations of current removal technologies, such as high operating costs and insufficient adsorption energies. This study systematically investigates the adsorption mechanisms of HCN on diverse adsorbent platforms using density functional theory (DFT). Our findings identify graphitic carbon nitride (g-C 3 N 4 ) as a very promising candidate due to its structural simplicity and high adsorption efficiency. However, to overcome the inherent limitations of g-C 3 N 4 , we show that strategic engineering, particularly through transition metal functionalization and semiconductor hybridization significantly optimizes the adsorption energy profile and enhances molecular selectivity. These modifications transform the material from a passive adsorbent to a highly active platform for HCN detection and adsorption. Among the modified systems, W-modified g-C 3 N 4 has the strongest interaction with HCN and exhibits the best Eads value of -3.104 eV compared to the doped versions. This makes the material a more effective candidate for detecting and capturing HCN molecules from the environment. Looking ahead, these results provide a computational model for the design of multi-component structures and pave the way for the development of highly sensitive, low-cost, and robust HCN sensors and remediation systems. Future research should focus on the large-scale synthesis of these frameworks and their long-term stability under varying environmental conditions to translate these computational insights into practical industrial applications.