Atomic Ordered Functionalization by Covalent and Noncovalent Interactions for Glycine Detection in Armchair Graphene NanoribbonsA Theoretical Approach

This study investigates the covalent and noncovalent functionalization (CF and NCF) of glycine (Gly) on armchair graphene nanoribbons (AGNR₄₂ and AGNR₅₄) at different sites (bulk, single edge, and double edge) using density functional theory (DFT) at the B3LYP/6-311++G(d,p) level. For covalent bonding, two different attachment modes of Gly (<i>O</i> and <i>N</i> sites) were considered. Key sensing parameters such as adsorption energy (<i>E</i>_ad), band gap (<i>E</i>_g), density of states (DOS), charge transfer, electrical conductivity, molecular electrostatic potential (MEP), sensitivity, and recovery time were analyzed. The results show that covalent bonding enhances sensitivity due to strong chemical attachment, while noncovalent bonding enables quicker recovery due to weak physical interactions. Although AGNR₅₄ has a larger surface area, AGNR₄₂ demonstrates better sensitivity with CF. The configurations, such as NCF-Gly-SEAGNR₄₂, NCF-Gly-DEAGNR₄₂, NCF-Gly-SEAGNR₅₄, <i>O</i>-Gly-BFAGNR₅₄, and <i>N</i>-Gly-BFAGNR₅₄, exhibit fast recovery times of 1.599 × 10^–12 s, 6.15 × 10^–4 s, 76 s, 595 s, and 5.293 × 10⁶ s, respectively. Among these, <i>N</i>-Gly-BFAGNR₅₄, NCF-Gly-SEAGNR₅₄, and <i>O</i>-Gly-BFAGNR₅₄ exhibit intermediate sensing responses (0.223, 0.026, and 0.019, respectively) and acceptable recovery time. These findings further emphasize the relationship between sensitivity and reusability, where enhancing one often affects the other. The overall sensing performance is governed not only by the bonding type but also by the surface area, position of sites, adsorption site, adsorption energy, and the orientation of the analyte. Thus, the significant variation in adsorption and electronic properties of AGNRs reveals that atomic-order functionalization enhances the detection of Gly and may be considered a promising biosensor for Gly removal. This study also provides new evidence and deeper insights that advance the current understanding of GNR-based biosensing systems.