Substitutional Si impurities in monolayer hexagonal boron nitride

We report the first observation of substitutional silicon atoms in single-layer hexagonal boron nitride (h-BN) using aberration corrected scanning transmission electron microscopy (STEM). The medium angle annular dark field (MAADF) images reveal silicon atoms exclusively filling boron vacancies. This structure is stable enough under electron beam for repeated imaging. Density functional theory (DFT) is used to study the energetics, structure and properties of the experimentally observed structure. The formation energies of all possible charge states of the different silicon substitutions (Si$_\mathrm{B}$, Si$_\mathrm{N}$ and Si$_\mathrm{{BN}}$) are calculated. The results reveal Si$_\mathrm{B}^{+1}$ as the most stable substitutional configuration. In this case, silicon atom elevates by 0.66{\AA} out of the lattice with unoccupied defect levels in the electronic band gap above the Fermi level. The formation energy shows a slightly exothermic process. Our results unequivocally show that heteroatoms can be incorporated into the h-BN lattice opening way for applications ranging from single-atom catalysis to atomically precise magnetic structures

Model Compression Methods for YOLOv5: A Review

Over the past few years, extensive research has been devoted to enhancing YOLO object detectors. Since its introduction, eight major versions of YOLO have been introduced with the purpose of improving its accuracy and efficiency. While the evident merits of YOLO have yielded to its extensive use in many areas, deploying it on resource-limited devices poses challenges. To address this issue, various neural network compression methods have been developed, which fall under three main categories, namely network pruning, quantization, and knowledge distillation. The fruitful outcomes of utilizing model compression methods, such as lowering memory usage and inference time, make them favorable, if not necessary, for deploying large neural networks on hardware-constrained edge devices. In this review paper, our focus is on pruning and quantization due to their comparative modularity. We categorize them and analyze the practical results of applying those methods to YOLOv5. By doing so, we identify gaps in adapting pruning and quantization for compressing YOLOv5, and provide future directions in this area for further exploration. Among several versions of YOLO, we specifically choose YOLOv5 for its excellent trade-off between recency and popularity in literature. This is the first specific review paper that surveys pruning and quantization methods from an implementation point of view on YOLOv5. Our study is also extendable to newer versions of YOLO as implementing them on resource-limited devices poses the same challenges that persist even today. This paper targets those interested in the practical deployment of model compression methods on YOLOv5, and in exploring different compression techniques that can be used for subsequent versions of YOLO.Comment: 18 pages, 7 Figure

ANTI-DEPRESSANTS AND COVID-19: A NEW RAY OF HOPE

The coronavirus disease pandemic has grown worldwide. As we understand the exact pathophysiology of the disease and how it affects the systems in the human body, we are in the process of discovering and repositioning drugs potentially effective in these regards. A few targets of these drugs are excessive inflammation following SARS-CoV-2 infection and sigma-1 receptor ER chaperone protein, which plays a role in replication. The recent discovery of antidepressants like fluvoxamine and clomipramine acting through these targets may provide a new ray of hope to decrease mortality and morbidity in severe COVID patients

Environmental performance assessment: a comparison and improvement of three existing social housing projects

The energy consumption of buildings accounts for 22% of total global energy use and 13% of global greenhouse gas emissions. In this context, this study aims to evaluate the environmental performance of three social housing designs located in emerging economies by analysing sustainability indicators adopting different technical solutions. The analysis incorporates eleven construction strategies to improve the environmental performance of the buildings. The performance assessment is analysed by using EDGE (Excellence in Design for Greater Efficiencies) Methodology. Therefore, this study aims to help identify the construction strategies, with the aim of improving the operational energy performance (kWh/year/m2floor), operational CO2 emissions (tCO2eq/Year/m2floor), embodied energy (MJ/m2floor) and operational water consumption of housing (m3/year/m2floor). The results showed that when the technical measures are implemented, the energy demand decreases by 38.52% in Case A, 19% in Case B, and 41% in Case C. The embodied energy savings in materials in Case A 3%, Case B 0% and Case C 36% Regarding water consumption, the demand decreases by 46%, 4%, and 12% in Case A, B, and C respectively.Peer ReviewedPostprint (published version

Xe Irradiation of Graphene on Ir(111): From Trapping to Blistering

Using X-ray photoelectron spectroscopy, thermal desorption spectroscopy, and scanning tunneling microscopy we show that upon keV Xe + irradiation of graphene on Ir(111), Xe atoms are trapped under the graphene. Upon annealing, aggregation of Xe leads to graphene bulges and blisters. The efficient trapping is an unexpected and remarkable phenomenon, given the absence of chemical binding of Xe to Ir and to graphene, the weak interaction of a perfect graphene layer with Ir(111), as well as the substantial damage to graphene due to irradiation. By combining molecular dynamics simulations and density functional theory calculations with our experiments, we uncover the mechanism of trapping. We describe ways to avoid blister formation during graphene growth, and also demonstrate how ion implantation can be used to intentionally create blisters without introducing damage to the graphene layer. Our approach may provide a pathway to synthesize new materials at a substrate - 2D material interface or to enable confined reactions at high pressures and temperatures

Enhanced Tunnelling in a Hybrid of Single-Walled Carbon Nanotubes and Graphene

Transparent and conductive films (TCFs) are of great technological importance. The high transmittance, electrical conductivity and mechanical strength make single-walled carbon nanotubes (SWCNTs) a good candidate for their raw material. Despite the ballistic transport in individual SWCNTs, however, the electrical conductivity of their networks is limited by low efficiency of charge tunneling between the tube elements. Here, we demonstrate that the nanotube network sheet resistance at high optical transmittance is decreased by more than 50% when fabricated on graphene and thus provides a comparable improvement as widely adopted gold chloride ($\mathrm{AuCl_3}$) doping. However, while Raman spectroscopy reveals substantial changes in spectral features of doped nanotubes, no similar effect is observed in presence of graphene. Instead, temperature dependent transport measurements indicate that graphene substrate reduces the tunneling barrier heights while its parallel conductivity contribution is almost negligible. Finally, we show that combining the graphene substrate and $\mathrm{AuCl_3}$ doping, the SWCNT thin films can exhibit sheet resistance as low as 36 $\Omega$/sq. at 90% transmittance.Comment: 21 pages, 6 figure