Phonon self-energy corrections have mostly been studied theoretically and experimentally for phonon modes with zone-center (q = 0) wave-vectors. Here, gate-modulated Raman scattering is used to study phonons of a single layer of graphene (1LG) in the
frequency range from 2350 to 2750 cm-1, which shows the G* and the G-band features originating from a double-resonant Raman process with q ot= 0. The observed phonon renormalization effects are different from what is observed for the zone-center q = 0 case. To explain our experimental findings, we explored the phonon self-energy for the phonons with non-zero wave-vectors (q ot= 0) in 1LG in which the frequencies and decay widths are expected to behave oppositely to the behavior observed in the corresponding zone-center q = 0 processes. Within this framework, we resolve the identification of the phonon modes contributing to the G* Raman feature at 2450 cm-1 to include the iTO+LA combination modes with q ot= 0 and the 2iTO overtone modes with q = 0, showing both to be associated with wave-vectors near the high symmetry point K in the Brillouin zone.
This paper examines the interplay of opinion exchange dynamics and communication network formation. An opinion formation procedure is introduced which is based on an abstract representation of opinions as $k$--dimensional bit--strings. Individuals in
teract if the difference in the opinion strings is below a defined similarity threshold $d_I$. Depending on $d_I$, different behaviour of the population is observed: low values result in a state of highly fragmented opinions and higher values yield consensus. The first contribution of this research is to identify the values of parameters $d_I$ and $k$, such that the transition between fragmented opinions and homogeneity takes place. Then, we look at this transition from two perspectives: first by studying the group size distribution and second by analysing the communication network that is formed by the interactions that take place during the simulation. The emerging networks are classified by statistical means and we find that non--trivial social structures emerge from simple rules for individual communication. Generating networks allows to compare model outcomes with real--world communication patterns.
