Random eigenvalues of nanotubes

TL;DR

This paper investigates the statistical properties of closed paths in graphene and carbon nanotube models, revealing how their eigenvalues and path counts behave and converge as nanotube circumference increases.

Contribution

It introduces explicit formulas for the distributions of closed path counts in nanotubes and demonstrates their convergence to hexagonal lattice behavior as size grows.

Findings

Closed path counts form a moment sequence from uniform distributions.

Explicit formulas for PDFs and MGFs of these distributions are provided.

As nanotube circumference increases, distributions converge to those of the hexagonal lattice.

Abstract

The hexagonal lattice and its dual, the triangular lattice, serve as powerful models for comprehending the atomic and ring connectivity, respectively, in \textit{graphene} and \textit{carbon $(p,q)$--nanotubes}. The chemical and physical attributes of these two carbon allotropes are closely linked to the average number of closed paths of different lengths $k\in\mathbb{N}_0$ on their respective graph representations. Considering that a carbon $(p,q)$--nanotube can be thought of as a graphene sheet rolled up in a matter determined by the \textit{chiral vector} $(p,q)$, our findings are based on the study of \textit{random eigenvalues} of both the hexagonal and triangular lattices presented in \cite{bille2023random}. This study reveals that for any given \textit{chiral vector} $(p,q)$, the sequence of counts of closed paths forms a moment sequence derived from a functional of two independent uniform distributions. Explicit formulas for key characteristics of these distributions, including probability density function (PDF) and moment generating function (MGF), are presented for specific choices of the chiral vector. Moreover, we demonstrate that as the \textit{circumference} of a $(p,q)$--nanotube approaches infinity, i.e., $p+q\rightarrow \infty$, the $(p,q)$--nanotube tends to converge to the hexagonal lattice with respect to the number of closed paths for any given length $k$, indicating weak convergence of the underlying distributions.