Magnetoresistance and Magnetic Ordering Fingerprints in Hydrogenated Graphene

TL;DR

This paper theoretically investigates how hydrogen adatoms induce magnetic ordering in graphene, affecting its conductivity and magnetoresistance, providing predictions for experimental detection of magnetism in hydrogenated graphene.

Contribution

It introduces a mean-field Hubbard model combined with transport calculations to predict magnetic and magnetoresistance signatures in hydrogenated graphene.

Findings

Magnetoresistance signals up to 7% predicted at 0.25% hydrogen density.

Different magnetic states produce distinct conductivity fingerprints.

Theoretical guidance for experimental detection of hydrogen-induced magnetism.

Abstract

Spin-dependent features in the conductivity of graphene, chemically modified by a random distribution of hydrogen adatoms, are explored theoretically. The spin effects are taken into account using a mean-field self-consistent Hubbard model derived from first-principles calculations. A Kubo-Greenwood transport methodology is used to compute the spin-dependent transport fingerprints of weakly hydrogenated graphene-based systems with realistic sizes. Conductivity responses are obtained for paramagnetic, antiferromagnetic, or ferromagnetic macroscopic states, constructed from the mean-field solutions obtained for small graphene supercells. Magnetoresistance signals up to $\sim 7%$ are calculated for hydrogen densities around 0.25%. These theoretical results could serve as guidance for experimental observation of induced magnetism in graphene.