Graphene definition

F ig. 29. Schematic graph showing the definition of the parameter, R, used to empirically estimate the fraction of single graphene layers in hard carbon samples. 

The N-doped carbons with a nanotube backbone combine a moderate presence of micropores with the extraordinary effect of nitrogen that gives pseudocapacitance phenomena. The capacitance of the PAN/CNts composite (ca. 100 F/g) definitively exceeds the capacitance of the single components (5-20 F/g). The nitrogen functionalities, with electron donor properties, incorporated into the graphene rings have a great importance in the exceptional capacitance behavior. 

Figure 3.2 (a) Different types of carbon nanotubes result from formal rolling up of a graphene layer, (b) definition of the descriptors n and m and the parameter space,... 

Another method to access aeidie groups on activated carbons is direct potentio-metric titration, but it suffers from very slow exchange equilibria in solution [113] and the lack of definite endpoints. The latter faet reflects the spread of dissociation constants of most surface funetionalities on carbons caused by interactions between neighboring groups and effeets resulting from the limited size of the graphene sheets. Assuming a continuous, rather than discrete, distribution of pK values, Contescu et al. [114] showed that potentiometric titration data could be transformed to proton affinity distribution (PAD) eurves and that the results were in... 

The above discussion provides summary of QSPR/QSAR approaches applied to classical, chemical compounds. However, an analysis devoted to nanomaterials having gigantic and complex molecular architecture lead to necessity of definition of new approaches for the predictive modelling, because the representation of their molecular structure by means of molecular graph and/or SMILES sometimes becomes very problematic (e.g. multi-walled carbon nanotubes [34], graphene [35]). In the first approximation, the optimal descriptors for such species should be a collector of all available data which are able to impact the physicochemical and/or biochemical behavior of nanomaterials. This concept is displayed in Fig. 12.6. 

The classification scheme shown is not definite. For example, the distinction between NPs and clusters cannot be established on the basis of dimensional criteria. Although the term cluster is used for small Au NPs [34], in principle, they are characterized by a well-defined structure [35], while the mobility of the surface atoms in the NPs does not allow one to ascribe them an exact geometrical shape. Similarly, although transmission electron microscopy (TEM) images depict carbon black particles as spherical and they are thus classified as OD nano-objects, they actually consist of disordered graphene sheets. 

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