Porous silicon (PS) layers were formed on n-type silicon (Si) wafers using Photo- electrochemical Etching technique (PEC) was used to produce porous silicon for n-type with orientation of (111). The effects of current density were investigated at: (10, 20, 30, 40, and50) mA/cm2 with etching time: 10min. X-ray diffraction studies showed distinct variations between the fresh silicon surface and the synthesized porous silicon. The maximum crystal size of Porous Silicon is (33.9nm) and minimum is (2.6nm) The Atomic force microscopy (AFM) analysis and Field Emission Scanning Electron Microscope (FESEM) were used to study the morphology of porous silicon layer. AFM results showed that root mean square (RMS) of roughness and the grain size of porous silicon decreased as etching current density increased and FESEM showed that a homogeneous pattern and confirms the formation of uniform porous silicon. The chemical bonding and structure were investigated by using Fourier transformation infrared spectroscopy (FTIR). The band gap of the samples obtained from photoluminescence (PL). These results showed that the band gap of porous silicon increase with increasing porosity.
Roughness length is one of the key variables in micrometeorological studies and environmental studies in regards to describing development of cities and urban environments. By utilizing the three dimensions ultrasonic anemometer installed at Mustansiriyah university, we determined the rate of the height of the rough elements (trees, buildings and bridges) to the surrounding area of the university for a radius of 1 km. After this, we calculated the zero-plane displacement length of eight sections and calculated the length of surface roughness. The results proved that the ranges of the variables above are ZH (9.2-13.8) m, Zd (4.3-8.1) m and Zo (0.24-0.48) m.
The present study experimentally and numerically investigated the impact behavior of composite reinforced concrete (RC) beams with the pultruded I-GFRP and I-steel beams. Eight specimens of two groups were cast in different configurations. The first group consisted of four specimens and was tested under static load to provide reference results for the second group. The four specimens in the second group were tested first under impact loading and then static loading to determine the residual static strengths of the impacted specimens. The test variables considered the type of encased I-section (steel and GFRP), presence of shear connectors, and drop height during impact tests. A mass of 42.5 kg was dropped on the top surface at the m
... Show MoreThe objective of this study is to verify the overall performance and evaluate the wastewater quality of the wastewater treatment plant at the Abu Ghraib Dairy Factory and compare the results with the Iraqi Quality Standards (IQS) for effluent disposal and with the national determinants of treated water use. Agricultural irrigation wastewater, which included daily assessment records of the main parameters affecting wastewater [five-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), total dissolved solids (T.D.S), total suspended solids (TSS), phosphate (PO4), nitrate (NO3), hydrogen ion concentration (pH)] obtained from the quality control department of Abu Ghraib dairy plant registered from January 2017 to December 2020. Th
... Show MoreThis study aims at shedding light on the linguistic significance of collocation networks in the academic writing context. Following Firth’s principle “You shall know a word by the company it keeps.” The study intends to examine three selected nodes (i.e. research, study, and paper) shared collocations in an academic context. This is achieved by using the corpus linguistic tool; GraphColl in #LancsBox software version 5 which was announced in June 2020 in analyzing selected nodes. The study focuses on academic writing of two corpora which were designed and collected especially to serve the purpose of the study. The corpora consist of a collection of abstracts extracted from two different academic journals that publish for writ
... Show MoreDetection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with
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