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Beilstein J. Nanotechnol. 2019, 10, 644–653, doi:10.3762/bjnano.10.64
Figure 1: Illustration of the gas molecule adsorption on the armchair graphene nanoribbon (AGNR) unit cell.
Figure 2: Carrier velocity versus carrier concentration in armchair graphene nanoribbon (AGNR).
Figure 3: Illustration of the gas sensor with the 8-AGNR FET platform. The structure consists of the left and...
Figure 4: Orientation of CO and NO gas molecules on the 8-AGNR plane.
Figure 5: Energy band structure of the 8-AGNR: (a) bare AGNR, (b) after CO adsorption, (c) after NO adsorptio...
Figure 6: Armchair graphene nanoribbon (AGNR) sensor I–V characteristics obtained using the Atomistix Toolkit...
Figure 7: The comparison study for the I–V characteristics of the sensor simulated by MATLAB and the Atomisti...
Figure 8: Sensitivity of the armchair graphene nanoribbon (AGNR) in the presence of CO and NO molecules.
Beilstein J. Nanotechnol. 2014, 5, 603–609, doi:10.3762/bjnano.5.71
Figure 1: A schematic of a graphene-based EGFET including the bias configuration (three-electrode electrochem...
Figure 2: A cross-section of graphene-based electrolyte-gated field effect transistor, together with the equi...
Figure 3: The proposed model of quantum capacitance of EGFETs based single-layer graphene.
Figure 4: A flowchart of ACO-based algorithm for optimizing the quantum capacitance model.
Figure 5: Comparison between the proposed single-layer graphene quantum capacitance model, the optimized prop...
Figure 6: The convergence profile of the optimization of the proposed model using ACO technique.