Publication Date


Document Type


Committee Members

Amir Farajian (Advisor), Vikram Kuppa (Committee Member), Raghavan Srinivasan (Committee Member), Henry Daniel Young (Committee Member), Yan Zhuang (Committee Member)

Degree Name

Doctor of Philosophy (PhD)


We investigate detection mechanisms of real time sensors, based on ultra-thin (single and bi-atomic layer thick) and ultra-narrow (~1nm) graphene nanoribbons (GNRs), using first principle based theoretical methods. In the first part of this study we study the electronic and magnetic structures of bilayer graphene nanoribbons (BGNRs) beyond the conventional AA and AB stackings, by using density functional theory within both local density and generalized gradient approximations (LDA and GGA). Our results show that, irrespective of the method chosen, stacking arrangements other than the conventional ones are most stable, and result in significant modification of BGNRs characeristics. The most stable bilayer armchair and zigzag structures with a width of ~1 nm are semiconducting with band gaps of 0.04 and 0.05 eV, respectively. We show shift evolution of magnetic states and emergence of magnetization upon deformation in bilayer zigzag GNRs. Band gap dependence on shift can be used to design accurate nanosensors. In the second part of this study we study detection of CO and CO2 gas molecules by change in quantum conductance of armchair graphene nanoribbons (AGNR) with a width of ~1 nm. Quantum conductance modulations are calculated by using second-order Møller-Plesset (MP2) method and density functional theory (DFT) for geometry optimization and a hybrid approach for electronic structure calculations. We determine stable and metastable physisorption orientations of gas molecules with varying concentrations. Our MP2-calculated binding energies relate 8.33% and 16.33% surface coverages of CO and CO2, respectively, to 1.72x104 and 497 parts per million (ppm). With such concentrations molecules adsorption results in conductance characteristics shifts on the order of few meV. As the concentrations detected in experiments are much less, other mechanisms including substrate and/or carrier gas doping as well as adsorption on defects or electrodes may contribute toward gas sensing using graphene plates. We also discuss temperature effects and propose possible methods for improving gas detection by GNRs. Next, we studied interactions of single and double NO2 molecules with graphene nanoribbons using first principles, for nanoelectronic-based sensing of extremely low NO2 concentrations. Adsorption geometries, energy barriers, and room temperature rate constants are determined to assess reaction kinetics. Resultant modulations of quantum transport are determined through Green's function implementation of Landauer's formalism. We show that formation of hydrogen bonded NO2 at edge and physisorbed NO2 at center are processes without barriers, whereas chemisorptions at center or edge are activated processes. Detectable current decrease is predicted for higher concentration hydrogen bonded or for chemisorption cases. Nonbonding and weak sp3 hybridization at the edge of AGNR are shown to be more favorable than center adsorptions, revealing increased edge reactivity compared to graphene. Raman spectra for NO2 chemisorption cases are simulated and discussed with characterization and sensing point of view. We discuss possible measures to enhance sensitivity of GNRs for detecting nitrogen dioxide and similar molecules. We also address the issue of room-temperature effects on electronic transport modulations in AGNR used as a gas sensor. Coherent (excluding electron-phonon interactions) and non-coherent (including electron-phonon interactions) transports are calculated using nonequilibrium Green's function formalism and Born approximation. While these calculations often are computationally demanding, we show that within nanosensor context with physisorbed molecules simple approximations can be made that significantly reduce the calculation time without affecting the results qualitatively.

Page Count


Department or Program

Ph.D. in Engineering

Year Degree Awarded


Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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