Understanding the chemistry of customized carbonaceous nanomaterials

Carbon-based nanomaterials have exceptional properties, that can lead to new advanced applications in many fields of technology and science, for instance: energy solutions, protective and biomedical coatings, and biosensing. This thesis is focused on computational methods used to understand better the structure-property relationships of these materials. We look deeply into surface chemistry of carbon nanomaterials and develop a new method for characterization of carbonaceous materials by combining computational and experimental data. In addition to elemental carbon, carbonaceous nanomaterials often contain other elements, like hydrogen and oxygen. As the properties of the materials depend on their chemical composition we carried out a detailed analysis about reactivity of amorphous carbon surfaces to obtain more information on this connection. In this work, a combination of novel computational methods were employed: density functional theory-level calculations, with machine learning-based structure construction and classification of the chemical environments present in the structures. This approach enables us to achieve statistically meaningful data-set, with high accuracy and low computational cost. Once we have studied the reactivity of carbon, careful material characterization is in place, so that it is possible to compare computational results with experimental samples. This comparison is valuable in two ways: we want to understand the experimental data better but, on the other hand, there is a need to make sure that the computational samples reflect experimental results in a realistic way. In other words, our models enable computational experiments. X-ray spectroscopy provides an extremely accurate means for analyzing the surface chemistry of substances in general. However, interpretation of the experimental data, especially when the sample is disordered, can prove to be challenging. The computational method that we introduce in this thesis can recreate the X-ray spectra from first principles, which provides a long searched connection between macroscopic and atomic level structures. As a conclusion, the information gained by combining novel computational methods with experimental science can be used to develop customized materials, e.g., for electrochemical detection of dopamine, which is used as a model system in this study, but also for several other promising applications.