Graphene monolayer rotation on Ni(111) facilitates bilayer graphene growth

2012 ◽  
Vol 100 (24) ◽  
pp. 241602 ◽  
Author(s):  
Arjun Dahal ◽  
Rafik Addou ◽  
Peter Sutter ◽  
Matthias Batzill
Keyword(s):  
Bilayer Graphene ◽  
Graphene Monolayer ◽  
Graphene Growth

scholarly journals Bilayer Graphene Growth via a Penetration Mechanism

2014 ◽  
Vol 118 (12) ◽  
pp. 6201-6206 ◽  
Author(s):  
Ping Wu ◽  
Xiaofang Zhai ◽  
Zhenyu Li ◽  
Jinlong Yang
Keyword(s):  
Bilayer Graphene ◽  
Graphene Growth ◽  
Penetration Mechanism

scholarly journals A Novel Graphene Modelling: Bottom up Approach - Band Gap Studies to Transport in Python

2021 ◽  
Author(s):  
Bhavya Bhardwaj ◽  
Bala Tripura Sundari B
Keyword(s):  
Band Gap ◽  
Bilayer Graphene ◽  
Graphene Nanoribbon ◽  
Monolayer Graphene ◽  
Hamiltonian Matrix ◽  
Graphene Monolayer ◽  
Matrix Formulation ◽  
Material Models ◽  
Bottom Up ◽  
20 Nm

Abstract In this work we develop a computational, quantum level monolayer graphene nanoribbon (GNR) MOSFET of channel length of 10 and 20 nm, with a width of 2 nm and contacts of 2nm width is attached. To develop the MOSFET channel, a bottom up approach is adopted by developing the material model. First the material models of graphene nanoribbon is developed using pybinding module tool in python. The material models of monolayer, bilayer graphene nanoribbon are built on the principles of tight binding module. The methodology developed is based on the Hamiltonian matrix formulation that has been used to determine the E-k plots and LDOS plots of graphene monolayer, bilayer graphene nano ribbon. The GNR MOSFET that is structurally built in python is used to simulate graphene as a switch. Its band gap characteristics is presented as its performance as a switch and is verified with relevant work. Then GNR MOSFET is modelled using quantum principles of NEGF and greens function to determine the transmission characteristics and the I-V characteristics for channel lengths of 10 nm and 20 nm.

scholarly journals Quasi-free-standing monolayer and bilayer graphene growth on homoepitaxial on-axis 4H-SiC(0 0 0 1) layers

Carbon ◽  
2015 ◽  
Vol 82 ◽  
pp. 12-23 ◽  
Author(s):  
J. Hassan ◽  
M. Winters ◽  
I.G. Ivanov ◽  
O. Habibpour ◽  
H. Zirath ◽  
...  
Keyword(s):  
Bilayer Graphene ◽  
Free Standing ◽  
Graphene Growth

Layer-by-Layer AB-Stacked Bilayer Graphene Growth Through an Asymmetric Oxygen Gateway

2019 ◽  
Vol 31 (16) ◽  
pp. 6105-6109 ◽  
Author(s):  
Bing Liu ◽  
Yaochen Sheng ◽  
Shenyang Huang ◽  
Zhongxun Guo ◽  
Kun Ba ◽  
...  
Keyword(s):  
Bilayer Graphene ◽  
Layer By Layer ◽  
Graphene Growth

scholarly journals On the Consistency of the Exfoliation Free Energy of Graphenes by Molecular Simulations

2021 ◽  
Vol 22 (15) ◽  
pp. 8291
Author(s):  
Anastasios Gotzias ◽  
Elena Tocci ◽  
Andreas Sapalidis
Keyword(s):  
Free Energy ◽  
Saline Water ◽  
Bilayer Graphene ◽  
Umbrella Sampling ◽  
Van Der Waals Interactions ◽  
Monolayer Graphene ◽  
Graphene Monolayer ◽  
Shear Direction ◽  
Liquid Environment ◽  
Before And After

Monolayer graphene is now produced at significant yields, by liquid phase exfoliation of graphites in solvents. This has increased the interest in molecular simulation studies to give new insights in the field. We use decoupling simulations to compute the exfoliation free energy of graphenes in a liquid environment. Starting from a bilayer graphene configuration, we decouple the Van der Waals interactions of a graphene monolayer in the presence of saline water. Then, we introduce the monolayer back into water by coupling its interactions with water molecules and ions. A different approach to compute the graphene exfoliation free energy is to use umbrella sampling. We apply umbrella sampling after pulling the graphene monolayer on the shear direction up to a distance from a bilayer. We show that the decoupling and umbrella methods give highly consistent free energy results for three bilayer graphene samples with different size. This strongly suggests that the systems in both methods remain closely in equilibrium as we move between the states before and after the exfoliation. Therefore, the amount of nonequilibrium work needed to peel the two layers apart is minimized efficiently.

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