Density Functional Theory Investigation of Temperature-Dependent Properties of Cu-Nitrogen-Doped Graphene as a...

In this study, density functional theory (DFT) was used to investigate the influence of temperature on the performance of a novel Cu-nitrogen-doped graphene Cu₂-N₈/Gr nanocomposite as a catalyst for the oxygen reduction reaction (ORR) in fuel cell applications. Our DFT calculations, conducted using Gaussian 09w with the 3-21G/B3LYP basis set, focus on the Cu-nitrogen-doped graphene nanocomposite cathode catalyst, exploring its behavior at three distinct temperatures: 298.15 K, 353.15 K, and 393.15 K, under acidic conditions. Our analysis of formation energies indicates that the structural stability of the catalyst remains unaffected as the temperature varies within the potential range of 0-7.21 V. Notably, the stability of the ORR steps experiences a marginal decrease with increasing temperature, with the exception of the intermediate OH + H₂O (*OH + H + *OH). Interestingly, the optimization reveals the absence of single OH and H₂O intermediates during the reactions. Furthermore, the OH + H₂O step is optimized to form the OH + H + OH intermediate, featuring the sharing of a hydrogen atom between dual OH intermediates. Free energy calculations elucidate that the catalyst supports spontaneous ORR at all temperatures. The highest recorded maximum cell potential, 0.69 V, is observed at 393.15 K, while the lowest, 0.61 V, is recorded at 353.15 K. In particular, the Cu₂-N₈/Gr catalyst structure demonstrates a reduced favorability for the H₂O₂ generation at all temperatures, resulting in the formation of dual OH intermediates rather than H₂O₂. In conclusion, at 393.15 K, Cu₂-N₈/Gr exhibits enhanced catalyst performance compared to 353.15 K and 298.15 K, making it a promising candidate for ORR catalysis in fuel cell applications.