Thermodynamic Behavior of Doped Graphene: Impact of Heavy Dopant Atoms.

This study investigates the effect of incorporating heavy dopant atoms on the topological transitions in the energy spectrum of graphene, as well as on its thermodynamic properties. A tight-binding model is employed that incorporates a lattice composition parameter associated with the dopant's effect to obtain the electronic spectrum of graphene. Thus, the substitutional atoms in the lattice impact the electronic structure of graphene by altering the connectivity of the Dirac cones and the symmetry of the energy surface in their spectrum.

Entropy and Negative Specific Heat of Doped Graphene: Topological Phase Transitions and Nernst's Theorem Revisited.

This study explores the thermodynamic properties of doped graphene using an adapted electronic spectrum. We employed the one-electron tight-binding model to describe the hexagonal lattice structure. The dispersion relation for graphene is expressed in terms of the hopping energies using a compositional parameter that characterizes the different dopant atoms in the lattice.

Dirac fermions duality in graphene: Ripples and fractional dimensions as function of temperature.

Graphene consists of coupled direct/dual fermionic sub-systems and, consequently, the thermal properties of both are intrinsically correlated. The dual is characterized by negative temperatures, and its free energy keeps opposite sign concerning the direct. The growth of ripples in graphene becomes related to temperature rises with fractional spatial dimension ~2.

Impact of Noise on a Dynamical System: Prediction and Uncertainties from a Swarm-Optimized Neural Network.

An artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass chaotic time series in the short-term x(t + 6). The performance prediction was evaluated and compared with other studies available in the literature.