Abstract

Semiconductor-based gas sensors were prepared, that use n-type tin oxide (SnO₂) and  tin oxide: zinc oxide composite (SnO₂)_1-x(ZnO)_x at different x ratios using pulse laser deposition at room temperature. The prepared thin films were examined to reach the optimum conditions for gas sensing applications, namely X-ray diffraction, Hall effect measurements, and direct current conductivity. It was found that the optimum crystallinity and maximum electron density, corresponding to the minimum charge carrier mobility, appeared at 10% ZnO ratio. This ratio appeared has the optimum NO₂ gas sensitivity for 5% gas concentration at 300 °C working temperat

In this research tri metal oxides were fabricated by simple chemical spray pyrolysis technique from (Sn(NO₃)₂.20 H₂O, Zn(NO₃)₂.6 H₂O, Cd(NO₃)₂.4 H₂O) salts at concentration 0.1M with mixing weight ratio 50:50 were fabricated on silicon substrate n-type (111). (with & without the presence of grooves by the following diemensions (20μm width, 7.5μm depth) with thickness was about ( 0.1 ±0.05 µm) using water soluble as precursors at a substrate temperature 550 ºC±5, with spray distance (15 cm) and their gas sensing properties toward H₂S gas at different concentrations (10,50,100,500 ppmv) in air were investigated at room te

This work deals with the production of light fuel cuts of (gasoline, kerosene and gas oil) by catalytic cracking treatment of secondary product mater (heavy vacuum gas oil) which was produced from the vacuum distillation unit in any petroleum refinery. The objective of this research was to study the effect of the catalyst -to- oil ratio parameter on catalytic cracking process of heavy vacuum gas oil feed at constant temperature (450 °C). The first step of this treatment was, catalytic cracking of this material by constructed batch reactor occupied with auxiliary control devices, at selective range of the catalyst –to- oil ratio parameter (  2, 2.5, 3 and 3.5) respectively.  The conversion of heavy vacuum gas

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

2 - 5,5 cm mosquito fishes (Gambusia affinis) were collected from brooks of Baghdad university . This study aimed to identify the histological structure and embryonic development of the gas bladder . The results revealed that the gas bladder of the adult fish consists of single chamber, paraphysoclistous , and its wall consist of three layers. The primordium of the gas bladder appears in 2,5 mm fish embryo as an evagination from the right side of endoderm of foreguts wall. This primordium consists of two thecae, inner, is simple columnar epithelium and outer originates from undifferentiated mesoderm. Simultaneously with the appearance of gas gland in the ventral side of the sac in 4 mm embryo, morphological and histological chan

Thin films of In2O3-CdO at various CdO contents (0.01, 0.02, 0.03, 0.04 and 0.05) were deposited on transparent substrate which is glass using chemical spray pyrolysis deposition method at substrate temperature 150oC. The structural properties was studied to characterize the prepared materials by XRD analysis. Surface morphology has been illustrated using scanning electron microscopy which proved the nanosize of prepared materials. This materials have been used as gas sensor for toxic gas which is hydrogen sulfide H2S. The sensitivity and response speed have been investigated with addition of CdO nanoparticles. © 2021, S.C. Virtual Company of Phisics S.R.L. All rights reserved.