Fingerprints of disorder source in graphene

Zhao, Peichao; Yuan, Shengjun; Katsnelson, M. I.; Raedt, Hans De

doi:10.1103/physrevb.92.045437

Cited by 38 publications

(24 citation statements)

References 52 publications

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“…[53][54][55] Complementary information can be obtained from the carrier-density dependence of the conductivity. 56 Our theory links the conductance distribution of a pn junction in a large magnetic field to the same set of coefficients and, thus, provides an additional and independent method to determine these.…”

Section: Discussionmentioning

confidence: 99%

Graphene pn junction in a quantizing magnetic field: Conductance at intermediate disorder strength

et al. 2016

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In a graphene pn junction at high magnetic field, unidirectional "snake states" are formed at the pn interface. In a clean pn junction, each snake state exists in one of the valleys of the graphene band structure, and the conductance of the junction as a whole is determined by microscopic details of the coupling between the snake states at the pn interface and quantum Hall edge states at the sample boundaries [Tworzydlo et al., Phys. Rev. B 76, 035411 (2007)]. Disorder mixes and couples the snake states. We here report a calculation of the full conductance distribution in the crossover between the clean limit and the strong disorder limit, in which the conductance distribution is given by random matrix theory [Abanin and Levitov, Science 317, 641 (2007)]. Our calculation involves an exact solution of the relevant scaling equation for the scattering matrix, and the results are formulated in terms of parameters describing the microscopic disorder potential in bulk graphene. arXiv:1607.07758v1 [cond-mat.mes-hall]

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Section: Discussionmentioning

confidence: 99%

Graphene pn junction in a quantizing magnetic field: Conductance at intermediate disorder strength

et al. 2016

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“…The electronic structure is described by a π‐orbital tight‐binding Hamiltonian in a real‐space representation for a Bernal‐stacked few‐layer honeycomb‐type lattice (see Figure ):

\overset{H}{true} = \sum_{l = 1}^{N_{layer}} {\overset{H}{true}}_{l} + \sum_{l = 1}^{N_{layer} - 1} {true \hat{H}}^{'}_{l}

where N layer is a number of layers, H l is a Hamiltonian contribution of l ‐th layer, and H l ′ describes hopping parameters between neighbor layers (vanishes in case of one layer), that is,

{\hat{H}}_{l} = - γ_{0}^{1} \sum_{〈 i, j 〉} c_{i}^{†} c_{j} - γ_{0}^{2} \sum_{〈 〈 i, j 〉 〉} c_{i}^{†} c_{j} - γ_{0}^{3} \sum_{〈 i, j 〉} c_{i}^{†} c_{j} + \sum_{i} V_{i} c_{i}^{†} c_{i}

c_{i}^{normal†}

and c i are creation or annihilation operators acting on a quasi‐particle located at the site i ( = m , n ), where m and n are numbers of each i site along zigzag edge ( x direction) and armchair edge ( y direction), respectively, as shown in Figure . The summation over i runs the entire honeycomb lattice, while j is restricted to the nearest‐neighbors (in the first term), next nearest‐neighbors (second term), and next‐to‐next nearest‐neighbors (third term) of i ‐th site.…”

Section: Defect‐ and Strain‐induced Responses In Energy Spectrum: Simmentioning

confidence: 99%

“…These defects are so‐called short‐range impurities, which can be located represented as substitutional atoms (e.g., like boron or nitrogen), neutral adatoms (such as hydrogen or oxygen), or chemisorbed molecules (e.g., hydroxyl, methyl, nitrophenyl functional groups) covalently bound to C atoms. We model them via the delta‐function potential

V_{i} \equiv \sum_{j = 1}^{N_{imp}} V_{j} δ_{i j}

for each i site of the graphene lattice, where N imp impurities occupy j sites. The ab initio and T‐matrix approach based calculations for common impurity (adsorbed) atoms result to the typical estimated values for the on‐site potential V j ≡ V 0 ≤ 80| u | .…”

Section: Defect‐ and Strain‐induced Responses In Energy Spectrum: Simmentioning

confidence: 99%

“…For instance, in case of resonant impurities, V 0 ≈ 60| u |. For definiteness in calculation procedure, we chose V 0 = 37| u | ≈ 100 eV . The behavior of such impurities is similar to the behavior of vacancies due to the completely electron localization at an impurity site.…”

Section: Defect‐ and Strain‐induced Responses In Energy Spectrum: Simmentioning

confidence: 99%

“…The outlined methodology (algorithm) for computing density of states has been testified and validated in several publications dealing with perfect (defect‐free) graphene monolayer and graphene sheets containing point or/and extended (line) defects. Moreover, application of the algorithm for pristine (defectless) graphene layer with millions of atoms results to the DOS curve, which conforms to the curve that can be obtained from derived analytical expression reasonable for infinite honeycomb lattice without defects …”

Section: Defect‐ and Strain‐induced Responses In Energy Spectrum: Simmentioning

confidence: 99%

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Defect‐Pattern‐Induced Fingerprints in the Electron Density of States of Strained Graphene Layers: Diffraction and Simulation Methods

Radchenko

Tatarenko

Lizunov

et al. 2019

Physica Status Solidi (b)

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The paper combines two theoretical approaches – the method of grazing dynamical diffraction (which allows performing the nondestructive structural diagnostics of defects in the near‐surface layers) with efficient numerical simulation method (which enables computation of electron structure in realistically large systems with millions of atoms) – for studying electronic properties in uniaxially strained graphene layers with point defects: impurity atoms. Electron density of states (DOS) is proved sensitive to the direction of uniaxial tensile deformation and configuration of defects. If defects are distributed orderly, the band gap value (estimated from the DOS curves) varies nonmonotonically versus the stretching deformation along zigzag‐edge direction. In this case, the minimal tensile strain required for the band gap opening is found to be smaller than that for defect‐free graphene, and the maximum band gap value is close to that predicted for failure limit of the defect‐free graphene. The obtained results play a significant part for band gap engineering in graphene: via spatial configuring of defects and external tensile stresses.

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The Impact of Uniaxial Strain and Defect Pattern on Magnetoelectronic and Transport Properties of Graphene

Radchenko

Sahalianov

Tatarenko

et al. 2019

Handbook of Graphene

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Fingerprints of disorder source in graphene

Cited by 38 publications

References 52 publications

Graphene pn junction in a quantizing magnetic field: Conductance at intermediate disorder strength

Graphene pn junction in a quantizing magnetic field: Conductance at intermediate disorder strength

Defect‐Pattern‐Induced Fingerprints in the Electron Density of States of Strained Graphene Layers: Diffraction and Simulation Methods

The Impact of Uniaxial Strain and Defect Pattern on Magnetoelectronic and Transport Properties of Graphene

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