Ultraviolet photodetectors have been widely utilized in several applications, such as advanced communication, ozone sensing, air purification, flame detection, etc. Gallium nitride and its compound semiconductors have been promising candidates in photodetection applications. Unlike polar gallium nitride-based optoelectronics, non-polar gallium nitride-based optoelectronics have gained huge attention due to the piezoelectric and spontaneous polarization effect–induced quantum confined-stark effect being eliminated. In turn, non-polar gallium nitride-based photodetectors portray higher efficiency and faster response compared to the polar growth direction. To date, however, a systematic literature review of non-polar gallium nitride-based photodetectors has yet to be demonstrated. Hence, the objective of this systematic literature review is to critically analyze the data related to non-polar gallium nitride-based photodetectors. Based on the pool of literature, three categories are introduced, namely, growth and fabrication, electrical properties, and structural, morphological, and optical properties. In addition, bibliometric analysis, a precise open-source tool, was used to conduct a comprehensive science mapping analysis of non-polar gallium nitride-based photodetectors. Finally, challenges, motivations, and future opportunities of non-polar gallium nitride-based photodetectors are presented. The future opportunities of non-polar GaN-based photodetectors in terms of growth conditions, fabrication, and characterization are also presented. This systematic literature review can provide initial reading material for researchers and industries working on non-polar gallium nitride-based photodetectors.
Background: Nutritional condition was reported as one of the factors affecting the oral health status, particularly among underprivileged groups. Orphans are one of the known high-risk groups. The aims of this study were to assess the nutritional status of orphans, and its impact on the oral health status. Materials and Methods: Five-hundred children aged 6-12 years old, 254 males and 246 females: institutionalized, non-institutionalized orphans and controls were participated in this study. Nutritional status assessed according to body mass index (BMI). Ramfjord index teeth were examined to assess oral cleanliness and gingival condition. All data were analyzed using SPSS version 23. Results this study revealed the institutionalized orphans
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Machine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a
... Show MoreIn this work chemical vapor deposition method (CVD) for the production of carbon nanotubes (CNTs) have been improved by the addition of S. Steel mesh container (SSMC) inside which the catalyst (Fe/Al2O3) was placed. Scanning electron microscopy (SEM) investigation method used to study nanotubes produced, showed that high yield of two types of (CNTs) obtained, single wall carbon nanotube (SWCNTs) with diameter and length of less than 50nm and several micrometers respectively and nanocoil tubes with a diameter and length of less than 100nm and several micrometers respectively. The chemical analysis of (CNTs) reveals that the main component is carbon (94%) and a little amount of Al (0.32%), Fe (2.22%) the reminder is oxygen. It was also fou
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