X-ray Spectroscopy Fingerprints of Pristine and Functionalized Graphene

Abstract

In this work, we demonstrate how to identify and characterize the atomic structure of pristine and functionalized graphene materials from a combination of computational simulation of X-ray spectra, on the one hand, and computer-aided interpretation of experimental spectra, on the other. Despite the enormous scientific and industrial interest, the precise structure of these 2D materials remains under debate. As we show in this study, a wide range of model structures from pristine to heavily oxidized graphene can be studied and understood with the same approach. We move systematically from pristine to highly oxidized and defective computational models, and we compare the simulation results with experimental data. Comparison with experiments is valuable also the other way around; this method allows us to verify that the simulated models are close to the real samples, which in turn makes simulated structures amenable to several computational experiments. Our results provide ab initio semiquantitative information and a new platform for extended insight into the structure and chemical composition of graphene-based materials.

Correlation between microstructure and surface chemistry of carbon nanofibers grown using different adhesive layers

“…On the other hand, the TiNi-CNFs, owing to their relatively disordered structure and exposed basal planes, are likely to adsorb and/or form nonspecific attachments with a larger variety of functional groups. The TiNi-CNF surface is also likely to contain a fair amount of defects, for example step defects on basal planes, which are expected to allow richer bonding with O and N [37,38].…”

Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon

“…Nevertheless, we find that our ML model can provide a qualitative understanding of chemical composition when being applied to experimental spectra. For example, when using experimental XANES data of an annealed graphene sample reported by ref as an input, our ML model predicts a composition of 97.9% sp 2 and 2.1% sp 3 carbon. Although defects and other chemical groups may be present in the sample, which can complicate a precise validation of chemical composition prediction with experimental probes, this prediction reasonably aligns with the expected composition of graphene that is primarily composed of sp 2 carbon.…”

Coupled Experimental–Theoretical Characterization of a Carbon Electrode in Vanadium Redox Flow Batteries using X-ray Absorption Spectroscopy

“…In the case of deploying advanced spectroscopy techniques, the difficulty lies in successful interpretation and deconvolution of an ensemble averaged spectrum. 25 In this regard, computational approaches present a unique opportunity to explore different interaction modes between surface functionalities and redox active V-complexes, allowing the construction of a database comprising distinct spectroscopic features with respect to their local chemistry. This approach will not only reveal the electronic origin of the spectroscopic features but also provide accurate and comprehensive references for unbiased interpretation of experimental spectra.…”