Mohammed Zwawi
King Abdulaziz University
47 Papers
80 Citations
Mohammed Zwawi is an academic researcher from King Abdulaziz University. The author has contributed to research in topics: Chemistry & Computer science. The author has an hindex of 6, co-authored 28 publications.
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Papers
A Review on Natural Fiber Bio-Composites, Surface Modifications and Applications.
TL;DR: In this article, a detailed analysis is carried out in this review paper to discuss developments in bio-composites, including structure, morphology, and modifications of fiber, mechanical properties, degradable matrix materials, applications, and limitations of bio composites.
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Algae as an attractive source for cosmetics to counter environmental stress
TL;DR: In this paper, the authors consider the environmental stresses on human skin and how they may be mitigated using cosmetics created using algae; special attention will be paid to external factors, both generally and specifically (amongst them light exposure and pollutants).
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Synthesis and characterization of Co-Al mixed oxide nanoparticles via thermal decomposition route of layered double hydroxide
Mohamed Helmy Abdel-Aziz,Mohamed Helmy Abdel-Aziz,M. Sh. Zoromba,M. Sh. Zoromba,Mohamed Bassyouni,Mohamed Bassyouni,Mohammed Zwawi,Abdullah Alshehri,Ahmed F. Al-Hossainy +8 more
TL;DR: In this paper, mixed cobalt-aluminum layered double hydroxide (Co-Al LDH) is synthesized in the presence of functional amino-organic compounds, including glycine, acetamide and urea.
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Fabrication of heterojunction diode using doped-poly (ortho-aminophenol) for solar cells applications
Ahmed F. Al-Hossainy,M. Sh. Zoromba,M. Sh. Zoromba,Mohamed Helmy Abdel-Aziz,Mohamed Helmy Abdel-Aziz,Mohamed Bassyouni,Alaa Attar,Mohammed Zwawi,A. A. I. Abd-Elmageed,Hisham A. Maddah,A. Ben Slimane +10 more
TL;DR: In this article, the authors used polyethylene glycol (PEG200) as a soft template for poly ortho aminophenol (POAP) polymerization.
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Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms
TL;DR: The neural network algorithm obtained in this study could be implemented in the eventuality of making a vital decision in the process operation of the co‐gasification process for hydrogen production.
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