Polymeric graphitic carbon nitride (gCN) compounds are promising materials in photoactivated electrocatalysis thanks to their peculiar structure of periodically spaced voids exposing reactive pyridinic N atoms. These are excellent sites for the adsorption of isolated transition metal atoms or small clusters that can highly enhance the catalytic properties. However, several polymorphs of gCN can be obtained during synthesis, differing for their structural and electronic properties that ultimately drive their potential as catalysts. The accurate characterization of the obtained material is critical for the correct rationalization of the catalytic results; however, an unambiguous experimental identification of the actual polymer is challenging, especially without any reference spectroscopic features for the assignment. In this work, we optimized several models of melem-based gCN, taking into account different degrees of polymerization and arrangement of the monomers, and we present a thorough computational characterization of their simulated XRD, XPS, and NEXAFS spectroscopic properties, based on state-of-the-art density functional theory calculations. Through this detailed study, we could identify the peculiar fingerprints of each model and correlate them with its structural and/or electronic properties. Theoretical predictions were compared with the experimental data whenever they were available.
Keywords: DFT; NEXAFS; XPS; XRD; graphitic carbon nitride; melem polymerization; melon; surface science.