Electronic Properties Of Graphene Nanoflakes Research Articles

Thermodynamic Stability and Electronic Properties of Graphene Nanoflakes

We conducted a large set of ab initio density functional theory computations to model a variety of hammer-terminated graphene nanoflakes—finite counterparts of armchair graphene nanoribbons. We focused on the relationships among the length and width of the nanoflakes, the stoichiometry and the conformation of the hydrogen saturation of the caps, and the resulting electronic structure. The energetics and the thermodynamic stability of the nanoflakes were investigated as well. Based on this study, we provide a recipe for determining the most stable saturation of the dangling bonds at the caps, which is generally disregarded in theoretical studies, and we prove that this step is crucial for a reliable description of the electronic structure of these systems. Data analysis proved that flakes far from the most stable C–H pattern exhibited electronic properties that were typical of an unsaturated bonding structure. Based on thermodynamics, we also proved that, for any given flake, there was a well-defined hydrogen content and a conformation of H atoms at the caps, which were favored across a wide range of environmental conditions.

Silver clusters tune up electronic properties of graphene nanoflakes: A comprehensive theoretical study

Graphene-silver composites show a synergic effect of both components with enhanced electronic, adsorption and catalytic properties. We present here the detailed theoretical study on geometric, thermodynamic and electronic properties of silver clusters Agn (n = 2–10) adsorbed on graphene nanoflakes as a model for silver-graphene composites. First, a benchmark study is performed on Agn…coronene complexes (n = 2, 4) by evaluating various functional against reported high level ab-initio method for the selection of best functional for further calculations. Then, density functional theory study at the best chosen level is performed to inquire how clustering of adsorbate attached with carbon surface can impact geometric, electronic and thermodynamic properties of silver-graphene composites. Furthermore, the interaction energies are corrected for dispersion and basis set superposition error in order to get more realistic estimate of interaction energies. In progression, we investigated the dependence of interaction energy upon cluster size and dimensionality. For Agn…coronene, the binding energies increase monotonically as the number of atoms in the cluster increases. However, for Agn…dodecabenzocoronene, the interaction energies show a sudden drop at Ag7…dodecabenzocoronene. The difference in behavior between Agn…dodecabenzocoronene and Agn…coronene for interaction energy is attributed to edge effect present in coronene. Furthermore, coplanar orientations of the silver clusters on polyaromatic hydrocarbons have higher interaction energies. The 2D-3D transition of the silver cluster is observed for Ag7…coronene. CDA analysis reveals that backdonation from dodecabenzocoronene to silver clusters is more dominant in Ag4–Ag7 whereas other clusters have dominant charge transfer to dodecabenzocoronene. Charge transfer analysis via constrained density functional theory (CDFT) reveals significant energy lowering from charge donor-acceptor interactions. Further energy decomposition analysis (EDA) using absolutely-localized molecular orbitals (ALMO) suggests that, while charge-transfer is important in these systems, the sum of electrostatics, Pauli repulsion, and London dispersion are far more significant as silver cluster size increases. NCI results also support the presence of van der Waals and electrostatic interactions. The hollow top is most favorite interaction sites compared to bridge top and head top binding sites over coronene lattice. The ionization potential, electron affinity, frontier molecular orbital analysis, chemical hardness, softness, chemical potential, and Fermi levels are deliberated to verify the stability and reactivity of most stable silver-graphene composites. The remarkable outcome of the current findings makes our silver-graphene composite as a potential applicant for high performance electronic and catalytic devices.

Gap Prediction in Hybrid Graphene-Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks

The electronic properties of graphene nanoflakes (GNFs) with embedded hexagonal boron nitride (hBN) domains are investigated by combined ab initio density functional theory calculations and machine-learning techniques. The energy gaps of the quasi-0D graphene-based systems, defined as the differences between LUMO and HOMO energies, depend not only on the sizes of the hBN domains relative to the size of the pristine graphene nanoflake but also on the position of the hBN domain. The range of the energy gaps for different configurations increases as the hBN domains get larger. We develop two artificial neural network (ANN) models able to reproduce the gap energies with high accuracies and investigate the tunability of the energy gap, by considering a set of GNFs with embedded rectangular hBN domains. In one ANN model, the input is in one-to-one correspondence with the atoms in the GNF, while in the second model the inputs account for basic structures in the GNF, allowing potential use in upscaled systems. We perform a statistical analysis over different configurations of ANNs to optimize the network structure. The trained ANNs provide a correlation between the atomic system configuration and the magnitude of the energy gaps, which may be regarded as an efficient tool for optimizing the design of nanostructured graphene-based materials for specific electronic properties.

Effects of shape, size, and pyrene doping on electronic properties of graphene nanoflakes.

Effects of size, shape, and pyrene doping on electronic properties of graphene nanoflakes (GNFs) were theoretically investigated using density functional theory method with PBE, B3PW91, and M06-2X functionals and cc-pVDZ basis set. Two shapes of zigzag GNFs, hexagonal (HGN) and rhomboidal (RGN), were considered. The energy band gap of GNF depends on shape and decreases with size. The HGN has larger band gap energy (1.23-3.96eV) than the RGN (0.13-2.12eV). The doping of pyrene and pyrene derivatives on both HGN and RGN was also studied. The adsorption energy of pyrene and pyrene derivatives on GNF does not depend on the shape of GNFs with energies between 21 and 27kcal mol-1. The substituent on pyrene enhances the binding to GNF but the strength does not depend on electron withdrawing or donating capability. The doping by pyrene and pyrene derivatives also shifts the HOMO and LUMO energies of GNFs. Both positive (destabilizing) and negative (stabilizing) shifts on HOMO and LUMO of GNFs were seen. The direction and magnitude of the shift do not follow the electron withdrawing and donating capability of pyrene substituents. However, only a slight shift was observed for doped RGN. A shift of 0.19eV was noticed for HOMO of HGN doped with 1-aminopyrene (pyNH2) and of 0.04eV for LUMO of HGN doped with 1-pyrenecarboxylic acid (pyCOOH). Graphical Abstract HOMO and LUMO Energies of pyrene/pyrene derivatives doped Graphene Nanoflakes.

Theoretical study of the design dye-sensitivity for usage in the solar cell device

There are many applications in the polymer chemistry, pharmaceutical, agricultural and industrial fields of the thiadiazole molecule and their derivatives. Allowance of the energy gap of the polymer conjugated is an object of great interesting debit for the possible removal of a doping in the preparation of highly conductivity polymers. Thiadiazoles derivatives are structural foundation of the polymer materials. In this present work, the electronic properties of graphene nanoflakes (GNFs)-phenanthrene-1,3,4-thiadiazoles oligomers are studied and discussed. Where thiadiazoles is expanded from one to 9 unit's molecules at the structure. The energy gap, HOMO, LUMO distribution, total energy, Fermi level energy, work function, maximum wavelength absorption, vertical absorption energies, and oscillator strengths are calculated for each molecule. All calculations are carry out by usage density function theory (DFT) and depended time density function theory (TD-DFT) with the B3LYP/6-31G model in the Gaussian 09W software packages. Results show that increasing the number of monomeric units lead to great enhance in the electronic properties, which caused it decreased the band gap from 3.17eV in the system with one unit of thiadiazole just to 1.35eV in the system with 9units of thiadiazole. This case is raised the value of maximum absorption wavelengths to >500nm to give the better performance in optoelectronic and solar cell, as these structures have prime absorption bands within the solar spectrum.

Influence of oxygen impurities on the electronic properties of graphene nanoflakes

Controlled chemical doping with oxygen impurities is a promising approach for the electronic band engineering of graphene nanoflakes (GNFs). Based on the first-principles of the density functional theory (DFT) calculations, we investigated the effect of various consternations of substitutional impurities from oxygen atoms on the electronic properties of GNFs. Our results show that the electronic properties of GNFs do not only depend on the oxygen impurity concentrations, but also depend on the geometrical pattern of oxygen impurities in the GNFs. Additionally, we also found interesting electronic properties of GNFs structure, which significantly contribute to that oxygen dopants cause a decreased energy gap. So, our results suggest that substitutional impurities are the best viable option for enhancement of desired electronic properties of GNFs.

Structural and electronic properties of graphene nanoflakes on Au(111) and Ag(111).

We investigate the electronic properties of graphene nanoflakes on Ag(111) and Au(111) surfaces by means of scanning tunneling microscopy and spectroscopy as well as density functional theory calculations. Quasiparticle interference mapping allows for the clear distinction of substrate-derived contributions in scattering and those originating from graphene nanoflakes. Our analysis shows that the parabolic dispersion of Au(111) and Ag(111) surface states remains unchanged with the band minimum shifted to higher energies for the regions of the metal surface covered by graphene, reflecting a rather weak interaction between graphene and the metal surface. The analysis of graphene-related scattering on single nanoflakes yields a linear dispersion relation E(k), with a slight p-doping for graphene/Au(111) and a larger n-doping for graphene/Ag(111). The obtained experimental data (doping level, band dispersions around EF, and Fermi velocity) are very well reproduced within DFT-D2/D3 approaches, which provide a detailed insight into the site-specific interaction between graphene and the underlying substrate.

Geometrical features can predict electronic properties of graphene nanoflakes

The experimental discovery of graphene has produced an avalanche of theoretical and computational studies to understand the behaviour of this fascinating material. However, the intrinsic relationships between nanoscale features and graphene stability, electronic properties and reactivity remains poorly investigated. In this work, we correlate the electronic properties of 622 computationally optimized graphene structures to their structural features using machine learning algorithms. Machine learning models of the electron affinity (EA), energy of the Fermi level (EF), electronic band gap (EG) and ionization potential (EI) are calibrated with structural features of 70% of the dataset describing more than 70% of cross-validation variance. Moreover, the predictions of the values of all the properties of a test set of the remaining 30% of dataset were specially accurate with a strong correlation of R2 ∼ 0.9. Machine learning models have tremendous potential to rapidly identify hypothetical nanostructures with desired electronic properties that, considering the latest advances in graphene synthesis and functionalization, could be experimentally prepared in a near future.

Electronic properties of graphene nano-flakes: Energy gap, permanent dipole, termination effect, and Raman spectroscopy

The electronic properties of graphene nano-flakes (GNFs) with different edge passivation are investigated by using density functional theory. Passivation with F and H atoms is considered: C(N(c)) X(N(x)) (X = F or H). We studied GNFs with 10 < Nc < 56 and limit ourselves to the lowest energy configurations. We found that: (i) the energy difference Δ between the highest occupied molecular orbital and the lowest unoccupied molecular orbital decreases with Nc, (ii) topological defects (pentagon and heptagon) break the symmetry of the GNFs and enhance the electric polarization, (iii) the mutual interaction of bilayer GNFs can be understood by dipole-dipole interaction which were found sensitive to the relative orientation of the GNFs, (iv) the permanent dipoles depend on the edge terminated atom, while the energy gap is independent of it, and (v) the presence of heptagon and pentagon defects in the GNFs results in the largest difference between the energy of the spin-up and spin-down electrons which is larger for the H-passivated GNFs as compared to F-passivated GNFs. Our study shows clearly the effect of geometry, size, termination, and bilayer on the electronic properties of small GNFs. This study reveals important features of graphene nano-flakes which can be detected using Raman spectroscopy.

Modelling the role of size, edge structure and terminations on the electronic properties of graphene nano-flakes

The addition of graphene nano-flakes to the suite of materials for graphene-based nanotechnology requires a complete understanding of the relationship between shape, structure, properties and property dispersion. Due to the large number of configurational degrees of freedom, this is a very challenging undertaking, particularly if morphological ensembles contain a reasonable array of sizes, shapes (edges and corners) and edge/corner terminations. We report results of density functional tight-binding simulations of zigzag (ZZ) and armchair (AC) hexagonal graphene nano-flakes, with unterminated, monoyhydride or dihydride terminated edges and corners. We find that hexagonal nano-flakes with AC edges are most likely to be achievable experimentally, providing that sufficient H is present during synthesis (or processing) to facilitate dihydride edge and corner passivation, forming a circumference of sp3 hybridized C atoms. This is significant, since the energy of the Fermi level and electronic density of states in the vicinity of the Fermi level are sensitive to the structural and chemical characteristics of the atoms around the circumference, which can be modified post-synthesis.

Influence of surface chemistry on the electronic properties of graphene nanoflakes

Spin polarized density functional theory was employed to investigate the influence of organic functional groups on the electronic properties of graphene nanoflakes (GNF). We found that for OH functionalized GNF, energy gap decreases as the number of layers is increased regardless of the stacking pattern (ABA and AAA). In the case of SH functionalized GNF the energy gap depends on the number of layers and on the stacking pattern. The observed trends were clarified in terms of interactions between π-electron rich beds. Our results bring new insights into engineering the properties of GNFs by modification of their surface chemistry and stacking conformation.

Structural and electronic properties of graphene nanoflakes

The structures, cohesive energies and HOMO-LUMO gaps of graphene nanoflakes and corresponding polycyclic aromatic hydrocarbons for a large variety of size and topology are investigated at the density functional based tight-binding level. Polyacene-like and honeycomb-like graphene nanoflakes were chosen as the topological limit structures. The influence of unsaturated edge atoms and dangling bonds on the stability is discussed. Our survey shows a linear trend for the cohesive energy as function of Ns/N (N - total number of atoms and Ns is number of edge atoms). For the HOMO-LUMO gap the trends are more complex and include also the topology of the edges.