Hongqing Shi

Harnessing the influence of reactive edges and defects of graphene substrates for achieving complete cycle of room-temperature molecular sensing.

Molecular doping and detection are at the forefront of graphene research, a topic of great interest in physical and materials science. Molecules adsorb strongly on graphene, leading to a change in electrical conductivity at room temperature. However, a common impediment for practical applications reported by all studies to date is the excessively slow rate of desorption of important reactive gases such as ammonia and nitrogen dioxide. Annealing at high temperatures, or exposure to strong ultraviolet light under vacuum, is employed to facilitate desorption of these gases. In this article, the molecules adsorbed on graphene nanoflakes and on chemically derived graphene-nanomesh flakes are displaced rapidly at room temperature in air by the use of gaseous polar molecules such as water and ethanol. The mechanism for desorption is proposed to arise from the electrostatic forces exerted by the polar molecules, which decouples the overlap between substrate defect states, molecule states, and graphene states near the Fermi level. Using chemiresistors prepared from water-based dispersions of single-layer graphene on mesoporous alumina membranes, the study further shows that the edges of the graphene flakes (showing p-type responses to NO₂ and NH₃) and the edges of graphene nanomesh structures (showing n-type responses to NO₂ and NH₃) have enhanced sensitivity. The measured responses towards gases are comparable to or better than those which have been obtained using devices that are more sophisticated. The higher sensitivity and rapid regeneration of the sensor at room temperature provides a clear advancement towards practical molecule detection using graphene-based materials.

Modelling the role of size, edge structure and terminations on the electronic properties of trigonal graphene nanoflakes

Graphene nanoflakes provide a range of opportunities for engineering graphene for future applications, due to the large number of configurational degrees of freedom associated with the addition of different types of corners and edge states in the structure. Since these materials can, in principle, span the molecular to macroscale dimensions, the electronic properties may also be discrete or continuous, depending on the application in mind. However, since the widespread use of graphene nanoflakes will require them to be predictable, stable and robust against variations associated with some degree of structural polydispersivity, the development of a complete understanding of the relationship between structure, properties and property dispersion is essential. In this paper we used electronic structure computer simulations to model the thermodynamic, mechanical and electronic properties of trigonal graphene nanoflakes with acute (highly reactive) corners. We find that these acute corners introduce new features that are different to the obtuse corners characteristic of hexagonal graphene nanoflakes, as well as different electronic states in the vicinity of the Fermi level. The structure and properties are sensitive to size and functionalization, and may provide new insights into the engineering of graphene nanoflake components.

High throughput theory and simulation of nanomaterials: exploring the stability and electronic properties of nanographene

As the level of complexity of nanoscale materials increases, new methods for quantifying accurate structure–property relationships must be found. The addition of more structural degrees of freedom can represent significant challenges to conventional experiments, but serves only to increase the total number of calculations needed in virtual experiments. By combining a combinatorial approach with electronic structure simulations it is possible to rapidly sample a large configuration space with atomic level precision. These techniques have been used here to explore the electronic properties of graphene quantum dots, and show that the energy of the Fermi level is extremely sensitive to the length of edges in the zigzag direction. This would not have been apparent from experiments unless samples could be prepared with atomic level resolution.

Site-dependent stability and electronic structure of single vacancy point defects in hexagonal graphene nano-flakes.

Graphene nano-flakes and quantum dots have considerable potential as components for nanodevices, since the finite in-plane dimension and additional edge and corner states provide potential for band gap engineering. However, like semi-infinite graphene membranes, they may contain different configurations of vacancy point defects that may be difficult to predict or control. In this paper we use density functional tight binding simulations to explore the impact of different geometric configurations of vacancies in unterminated (radical), mono-hydride and di-hydride terminated nano-flakes with zigzag or armchair edges. The results reveal that the planar structure is more uniformly preserved (with less distortion) when vacancies are located near the edges and corners, due to the combined effect of vacancy-edge-corner reconstructions, and passivating the circumference reduces the scattering of the band gap, but not the scattering of the energy of the Fermi level. In general, and regardless of the possible application, the use of zigzag-edged nano-flakes with stable edge/corner passivation is desirable to ensure reliability, and reduce the impact of an unknown number and configurations of vacancies.

Quantitative Structure-Property Relationship Modeling of Electronic Properties of Graphene Using Atomic Radial Distribution Function Scores.

The intrinsic relationships between nanoscale features and electronic properties of nanomaterials remain poorly investigated. In this work, electronic properties of 622 computationally optimized graphene structures were mapped to their structures using partial-least-squares regression and radial distributions function (RDF) scores. Quantitative structure-property relationship (QSPR) models were calibrated with 70% of a virtual data set of 622 passivated and nonpassivated graphenes, and we predicted the properties of the remaining 30% of the structures. The analysis of the optimum QSPR models revealed that the most relevant RDF scores appear at interatomic distances in the range of 2.0 to 10.0 Å for the energy of the Fermi level and the electron affinity, while the electronic band gap and the ionization potential correlate to RDF scores in a wider range from 3.0 to 30.0 Å. The predictions were more accurate for the energy of the Fermi level and the ionization potential, with more than 83% of explained data variance, while the electron affinity exhibits a value of ∼80% and the energy of the band gap a lower 70%. QSPR models have tremendous potential to rapidly identify hypothetical nanomaterials with desired electronic properties that could be experimentally prepared in the near future.