Structural, mechanical, electronic and optical properties of N-decorated single-walled silicon carbide nanotube photocatalyst for hydrogen evolution via water splitting: a DFT study

This work investigates the fundamental photocatalytic properties of nitrogen-doped single-walled silicon carbide nanotubes (N-doped SWSiCNTs) for hydrogen evolution for the first time. Investigations of the structural, mechanical, electronic, and optical properties of the studied systems were carried out using popular density functional theory implemented in quantum ESPRESSO and Yambo codes. Analysis of the structural properties revealed high mechanical stability with the 3.6% and 7.4% N-doped SWSiCNT. The calculated band gap of the N-doped SWSiCNT with 3.6% demonstrated a value of 2.56 eV which is within the photocatalytic range of 2.3 eV-2.8 eV. The hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) potentials of the 3.6% N-doped SWSiCNT also showed good agreement with previous theoretical data. The studied material showed the best photocatalytic performance in both parallel and perpendicular directions by absorbing photons in the visible region. Therefore, the observed structural, mechanical, electronic and optical behaviors demonstrated by the 3.6% N-doped SWSiCNT exposed it as a better photocatalyst for hydrogen production under visible light.

The potentials of boron-doped (nitrogen deficient) and nitrogen-doped (boron deficient) BNNT photocatalysts for decontamination of pollutants from water bodies

This work investigates the structural, elastic, electronic, and photoabsorption properties of boron- (N-deficient) and nitrogen- (B-deficient) doped single-walled boron nitride nanotube (SWBNNT) for photocatalytic applications for the first time. All calculations of the optimized systems were performed with DFT quantum simulation codes. The results of the structural analysis showed that SWBNNT is stable to both B and N dopants. It was also observed that the photodecomposition activity of the B-doped nanotube improved significantly under the condition of slight compressive stress, while it decreased for the N-doped nanotube. Therefore, N-doped SWBNNT showed poor performance under external pressure. Both B and N-doped systems could narrow the wide band gap of SWBNNT to the photocatalytic region below 3 eV, therefore this material can be used as photocatalysts in water splitting for hydrogen evolution, dye degradation, wastewater treatment, etc. Analysis of the optical properties revealed that B-doped SWBNNT absorbs more photons in the visible range than the N-doped SWBNNT and can therefore be considered as a more efficient photocatalyst. In addition, it was found that all doped nanotubes are anisotropic since the absorption in one direction of nanotube axes is worse than the other.

First-Principle Studies of the Structural, Electronic, and Optical Properties of Double-Walled Carbon Boron Nitride Nanostructures Heterosystem under Various Interwall Distances

Structural, electronic, and optical properties of a new combined system of carbon and boron nitride nanotubes are studied using the DFT first principles as implemented in Quantum ESPRESSO codes. The corrections to the quasi-particle energies were studied via GW hybrid functional implemented in the YAMBO code within the many-body perturbation theory. The studies were performed under different interwall distances of 3.0 nm, 2.5 nm, and 1.5 nm between CNTs and BNNTs. The results showed that the structural properties demonstrated high stability of the double-walled carbon boron nitride nanotube (DWCBNNT) systems under interwall distance (IWD) of 3.00 nm, 2.50 nm, and 1.50 nm. Results also demonstrated an inverse variation between the IWD and the diameter of the DWCBNNT system. In terms of the electronic properties, all three configurations of the DWCBNNTs reveal semiconducting behavior under KS-DFT showing a direct band gap of 3.30 eV, 1.79 eV, and 0.81 eV under IWD of 3.0 nm, 2.5 nm, and 1.5 nm, respectively. Furthermore, the band gap of the DWCBNNT increases with an increase in IWD (decrease in inner tube diameter) and decreases with a decrease in IWD (increase in inner tube diameter). In all three cases, the bands are formed by the molecular orbitals of the armchair CBNNT which are transformed to a series of continuous energy levels; the behaviors of electrons that formed the heterostructure are related to the behavior of electrons in B, C, and N atoms. From the optical properties perspective, the studies were conducted in parallel and perpendicular directions to the nanotubes’ axes. The presence of static dielectric functions in parallel direction at 3.3, 3.4, and 4.5 for nanotubes under 3.0 nm, 2.5 nm, and 1.5 nm demonstrated optical refraction. Refractions were also observed in directions perpendicular to the nanotubes. Furthermore, optical reflections occur when there is a higher absorption. The ability of these CBNNT hybrid systems to refract in all directions revealed the most exciting properties of the armchair CBNNT suitable to be used in magnifying glass materials. The findings further imply that the optical absorption coefficient is inversely related to the diameter of the nanotubes and is directly correlated to the band gap.

Cancerous and Non-Cancerous Brain MRI Classification Method Based on Convolutional Neural Network and Log-Polar Transformation

Magnetic resonance imaging (MRI) offers visual representations of the interior of a body for clinical analysis and medical intervention. The MRI process is subjected to a variety of image processing and machine learning approaches to identify, diagnose, and classify brain diseases as well as detect abnormalities. In this paper, we propose an improved classification method for distinguishing cancerous and noncancerous tumors from brain MRI images by using Log Polar Transformation (LPT) and convolutional neural networks (CNN). The LPT has been applied for feature extraction of rotation and scaling of distorted images, while the integration of CNN introduces a machine learning approach for the tumor classification of distorted images. The dataset was formed with images of seven different brain diseases, and the training set was formed by applying CNN with the extracted features. The proposed method is then evaluated in comparison to state-of-the-art algorithms, showing a definite improvement of the former. The obtained results show that the machine learning approach offers better classification with a success rate of about 96% in both plain brain MR images and rotation- and scale-invariant brain MR images. This work also successfully classified T-1 and T-2 weighted images of neoplastic and degenerative brain diseases. The obtained accuracy is perfected by several kernel procedures, while the combined performance of the two wavelet transformations and a strong dataset make our method robust and efficient. Since no earlier study on machine learning approaches with rotated and scaled brain MRI has come to our attention, it is expected that our proposed method introduces a new paradigm in this research field.