Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part I: Fingerprint Spectra

Carbonaceous materials, especially tetrahedral amorphous carbon (ta-C), can form complex functionalized surface structures and are thus promising candidates for applications in biomedical devices and electrochemistry. Functional groups at ta-C surfaces have been widely studied by spectroscopic techniques; however, interpretation of the experimental data is extremely difficult, especially in the case of X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectroscopy (XAS). The assignments of experimental XPS and XAS signals are normally based on references obtained from molecular or crystalline samples, which are simplified approximations for the far more complex amorphous structures. Here, we use extensive density functional theory (DFT) simulations to predict XAS and XPS signatures for carbon-based materials in more realistic environments, building on large data sets of structural models generated by a machine-learning (ML) interatomic potential. The results indicate clear signatures: individual fingerprint XAS spectra and distinctive XPS binding energy distributions, both in terms of center and broadness of the signal, for chemically different groups. The results point out what kind of structural information can and cannot be extracted with X-ray spectroscopy. This study will enable a deeper physicochemical understanding of experimental data and ultimately theory-based identification and quantification of functional groups in carbonaceous materials.