This study investigates the influence of silver oxide (Ag₂O) nanoparticles on the gas-sensing properties of carbon nanotube (CNT) and poly(3-hexylthiophene) (P3HT) composite films. The goal is to enhance the sensitivity and stability of CNT/P3HT-based gas sensors for nitrogen dioxide (NO₂) detection. CNTs and P3HT are composited at various mass ratios, followed by integrating Ag₂O nanoparticles to improve gas interaction and electronic properties. Structural and morphological analyses, including x-ray diffraction, Fourier transform infrared spectroscopy, field emission scanning electron microscopy, and energy dispersive x-ray spectroscopy, ascertain the successful incorporation of Ag₂O into the composites and reveal its role in improving dispersion and reducing CNT entanglement. Also, Gas-sensing tests demonstrate that Ag₂O doping enhances the composite’s response stability and sensitivity, with optimal performance at a CNT ratio of 0.7, where a higher and more stable response to NO₂ is observed. These findings suggest that Ag₂O-doped CNT/P3HT composites offer promising applications in highly sensitive NO₂ gas sensors, with potential scalability for environmental monitoring. Such progress in materials science for sensor technology is crucial for advancing real-time environmental monitoring capabilities, a prerequisite for effective air quality management and public health protection.
Background and Aim. Coronary artery disease (CAD) is a major risk factor for the progression to heart failure (HF), which is associated with an increase in left ventricular volume (LVV). This study aims to measure ventricular function and myocardial perfusion imaging markers of the left side of the heart, which can be performed with injection of a 99mTc at stress and rest by using single-photonemission-computed-tomography (SPECT). Subject and methods. The study included 121 patients with CAD, comprising 53 females and 68 males with ages between 25 to 88 years and 265 healthy subjects comprising 84 males and 181 females. All patients and healthy subjects volunteered to participate in this study. They were classified according to
... Show MoreThe effects of nutrients and physical conditions on phytase production were investigated with a recently isolated strain of Aspergillus tubingensis SKA under solid state fermentation on wheat bran. The nutrient factors investigated included carbon source, nitrogen source, phosphate source and concentration, metal ions (salts) and the physical parameters investigated included inoculum size, pH, temperature and fermentation duration. Our investigations revealed that optimal productivity of phytase was achieved using wheat bran supplemented with: 1.5% glucose. 0.5% (NH4)2SO4, 0.1% sodium phytate. Additionally, optimal physical conditions were 1 × 105 spore/g substrate, initial pH of 5.0, temperature of fermentation 30˚C and fermentation dura
... Show MorePurpose Heavy metals are toxic pollutants released into the environment as a result of different industrial activities. Biosorption of heavy metals from aqueous solutions is a new technology for the treatment of industrial wastewater. The aim of the present research is to highlight the basic biosorption theory to heavy metal removal. Materials and methods Heterogeneous cultures mostly dried anaerobic bacteria, yeast (fungi), and protozoa were used as low-cost material to remove metallic cations Pb(II), Cr(III), and Cd(II) from synthetic wastewater. Competitive biosorption of these metals was studied. Results The main biosorption mechanisms were complexation and physical adsorption onto natural active functional groups. It is observed that
... Show MoreAchieving an accurate and optimal rate of penetration (ROP) is critical for a cost-effective and safe drilling operation. While different techniques have been used to achieve this goal, each approach has limitations, prompting researchers to seek solutions. This study’s objective is to conduct the strategy of combining the Bourgoyne and Young (BYM) ROP equations with Bagging Tree regression in a southern Iraqi field. Although BYM equations are commonly used and widespread to estimate drilling rates, they need more specific drilling parameters to capture different ROP complexities. The Bagging Tree algorithm, a random forest variant, addresses these limitations by blending domain kno