Investigation of Electric Field Tunable Optical and Electrical Characteristics of Zigzag and Armchair Graphene Nanoribbons: An Ab Initio Approach.

Graphene nanoribbons (GNRs), categorized into zigzag and armchair types, hold significant promise in electronics due to their unique properties. In this study, optical properties of zigzag and armchair GNRs are investigated using density functional theory (DFT) in conjunction with Kubo-Greenwood formalism. Our findings reveal that optical characteristics of both GNR types can be extensively modulated through the application of a transverse electric field, e.g., the refractive index of the a zigzag GNR is shown to vary in the range of n = 0.3 and n = 9.9 for the transverse electric field values between 0 V/Å and 10 V/Å. Additionally, electrical transmission spectra and the electrical conductivities of the GNRs are studied using DFT combined with non-equilibrium Green's function formalism, again uncovering a strong dependence on the transverse electric field. For example, the conductance of the armchair GNR is shown to vary in the range of G = 6 µA/V and G = 201 µA/V by the transverse electric field. These results demonstrate the potential of GNRs for use in electronically controlled optoelectronic devices, promising a broad range of applications in advanced electronic systems.

Estimation of the unemployment rate in Turkey: A comparison of the ARIMA and machine learning models including Covid-19 pandemic periods.

The article focuses on analyzing the robustness of Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) methods in unemployment rate estimation. In this context, a stochastic trend in the unemployment rate was determined by using monthly data in Turkey. The oil price, real exchange rate, interest rate and unemployment rate variables are imported into the ARIMA and ANN models with 176 data samples for the period of 01.01.2008-31.08.2022. The results of the conventional linear ARIMA and nonlinear ANN regressor models are compared. The comparison results show that the ARMA (2,1) model is the most suitable model for the unemployment rate estimation. This conclusion was reached based on ARMA (2,1) and ANN's RMSE, MAE, MAPE and R2 parameters. From the results of the specified criteria, it was found that both models gave results close to the actual unemployment rate however ARMA (2,1) was the more appropriate model for the current data set. The actual unemployment data and the estimated values are also given verifying the better modeling of the developed ARMA (2,1) model. In addition, there are meaningful relationships between month variables and the employment rate. This result supports that the unemployment possesses chronic reasons in Turkey. On the other side, the unemployment rate forecasting error of the ARMA (2,1) is higher than the ANN model for the 2020-2021 period during the intense pandemic. This result is important because it shows that during the times of the economic uncertainty caused by the Covid-19 pandemic, forecasts employing the neural network model is observed to have lower errors than the results of autoregressive moving average model. Therefore, under an economic uncertainty, it is shown that modeling the unemployment rate using artificial neural network provides novel insights for economic forecasting.

Investigation and comparison of graphene nanoribbon and carbon nanotube based SARS-CoV-2 detection sensors: An ab initio study.

The rapid detection of SARS-CoV-2, the pathogen of the Covid-19 pandemic, is obviously of great importance for stopping the spread of the virus by detecting infected individuals. Here, we report the ab initio analysis results of graphene nanoribbon (GNR) and carbon nanotube (CNT) based SARS-CoV-2 detection sensors which are experimentally demonstrated in the literature. The investigated structures are the realistic molecular models of the sensors that are employing 1-pyrenebutyric acid N-hydroxysuccinimide ester as the antibody linker. Density functional theory in conjunction with non-equilibrium Green's function formalism (DFT-NEGF) is used to obtain the transmission spectra, current-voltage and resistance-voltage characteristics of the sensors before and after the attachment of the SARS-CoV-2 spike protein. The operation mechanism of the GNR and CNT based SARS-CoV-2 sensors are exposed using the transmission spectrum analysis. Moreover, it is observed that GNR based sensor has more definitive detection characteristics compared to its CNT based counterpart.

Computation of the Binding Energies between Human ACE2 and Spike RBDs of the Original Strain, Delta and Omicron Variants of the SARS-CoV-2: A DFT Simulation Approach.

The receptor binding domain (RBD) of SARS-CoV-2 binds to human ACE2 leading to infection. In this study, the complexes that are formed by the attachment of the SARS-CoV-2 spike RBDs of the original strain, delta and omicron variants to the human ACE2 are investigated via density functional theory (DFT) simulations to obtain binding energies. The DFT computations are performed without fragmenting the interfaces to involve longer-range interactions for improved accuracy, which is one of the primary features of the approach used in this study. Basis set superposition error corrections and van der Waals dispersions are also included in the DFT simulations. The binding energies of the SARS-CoV-2 spike RBDs of the original strain, delta and omicron variants to the human ACE2 are computed as -4.76, -6.68, and -11.77 eV, respectively. These binding energy values indicate that the binding of the omicron variant to the ACE2 is much more favorable than the binding of the original strain and the delta variant, which constitute a molecular reason for the takeover of the omicron variant. The binding energies and the decomposition of these energies found in this study are expected to aid in the development of neutralizing agents.

Density functional theory computation of the binding free energies between various mutations of SARS-CoV-2 RBD and human ACE2: molecular level roots of the contagiousness.

The receptor-binding domain (RBD) of SARS-CoV-2 attaches to the human ACE2 to initiate binding of SARS-CoV-2 to human cell and leads to the infection process afterwards. In this study, various mutations of SARS-CoV-2 spike RBD and human ACE2 complexes are investigated via density functional theory (DFT) computations to obtain binding free energies. The DFT computations are performed without fragmenting the interfaces to involve longer-range quantum mechanical interactions for improving accuracy. The vibrational free energies, van der Waals dispersion forces and basis set superposition error corrections are also included in the calculations. The results show that the absolute value of the binding energy of B.1.1.7 mutated spike RBD-ACE2 complex is more than five times higher than that of the original strain. The results of this study are expected to be useful for a deeper understanding of the relation of the binding free energies and the level of contagiousness.

Observation of negative differential resistance in mesoscopic graphene oxide devices.

The fractions of various functional groups in graphene oxide (GO) are directly related to its electrical and chemical properties and can be controlled by various reduction methods like thermal, chemical and optical. However, a method with sufficient controllability to regulate the reduction process has been missing. In this work, a hybrid method of thermal and joule heating processes is demonstrated where a progressive control of the ratio of various functional groups can be achieved in a localized area. With this precise control of carbon-oxygen ratio, negative differential resistance (NDR) is observed in the current-voltage characteristics of a two-terminal device in the ambient environment due to charge-activated electrochemical reactions at the GO surface. This experimental observation correlates with the optical and chemical characterizations. This NDR behavior offers new opportunities for the fabrication and application of such novel electronic devices in a wide range of devices applications including switches and oscillators.

Voltage-Dependent Electronic Transport Properties of Reduced Graphene Oxide with Various Coverage Ratios.

Graphene is mainly implemented by these methods: exfoliating, unzipping of carbon nanotubes, chemical vapour deposition, epitaxial growth and the reduction of graphene oxide. The latter option has the advantage of low cost and precision. However, reduced graphene oxide (rGO) contains hydrogen and/or oxygen atoms hence the structure and properties of the rGO and intrinsic graphene are different. Considering the advantages of the implementation and utilization of rGO, voltage-dependent electronic transport properties of several rGO samples with various coverage ratios are investigated in this work. Ab initio simulations based on density functional theory combined with non-equilibrium Green's function formalism are used to obtain the current-voltage characteristics and the voltage-dependent transmission spectra of rGO samples. It is shown that the transport properties of rGO are strongly dependent on the coverage ratio. Obtained results indicate that some of the rGO samples have negative differential resistance characteristics while normally insulating rGO can behave as conducting beyond a certain threshold voltage. The reasons of the peculiar electronic transport behaviour of rGO samples are further investigated, taking the transmission eigenstates and their localization degree into consideration. The findings of this study are expected to be helpful for engineering the characteristics of rGO structures.