The sensitivity of SnO2 nanoparticles/reduced graphene oxide hybrid to NO2 gas is discussed in the present work using density functional theory (DFT). The SnO2 nanoparticles shapes are taken as pyramids, as proved by experiments. The reduced graphene oxide (rGO) edges have oxygen or oxygen-containing functional groups. However, the upper and lower surfaces of rGO are clean, as expected from the oxide reduction procedure. Results show that SnO2 particles are connected at the edges of rGO, making a p-n heterojunction with a reduced agglomeration of SnO2 particles and high gas sensitivity. The DFT results are in good agreement with the experimental characterization of both SnO2 and rGO using energy gap and XPS values. Gibbs free energy, enthalpy, and entropy of the various considered reactions are calculated. Results show that the sensitivity of the rGO/SnO2 hybrid to NO2 gas is the result of the interplay of the dissociation and oxidation reactions of NO2 gas.
This paper discusses a comparative study to relate parametric and non-parametric mode decomposition algorithms for response-only data. Three popular mode decomposition algorithms are included in this study: the Eigensystem Realization Algorithm with the Natural Excitation Technique (NExT-ERA) for the parametric algorithm, as well as the Principal Component Analysis (PCA) and the Independent Component Analysis (ICA) for the non-parametric algorithms. A comprehensive parametric study is provided for (i) different response types, (ii) excitation types, (iii) system damping, and (iv) sensor spatial resolution to compare the mode shapes and modal coordinates of using a 10-DOF building model. The mode decomposition results are also compared using
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This study aimed to evaluate the health effects of exposure to particulate matter (PM2.5 and PM10) and gaseous pollutants, including carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2), on the hematological parameters of workers exposed to charcoal grilling emissions in restaurants. Air pollutant concentrations were measured in six barbecue restaurants located in Al-Rusafa District, Baghdad, during December 2024 and January 2025. Nine measurements were recorded monthly during morning and evening peak cooking periods. Blood samples were collected from two groups: grilling workers exposed directly to charcoal smoke (n = 30) and customers from dining areas as the control group (n = 30). Hematological an
... Show MoreA substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when co
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