Scientist & Lecturer | Chemistry • Material science • Machine Learning for Materials
I build data-driven tools for chemistry and environmental remediation, combining ML/DL with DFT and experiments. My work spans solar photocatalysis, perovskites, and intelligent sensing.
Dr. Mohamed Mahmoud Elsenety is a Lecturer in the Chemistry Department at Al-Azhar University, Cairo, specializing in materials science, inorganic and organic synthesis, and computational chemistry. His research integrates experimental and theoretical approaches, including solid-state and solution-phase synthesis, crystal structure determination, Rietveld analysis, DFT calculations using Material Studio, Gaussian, and Quantum ESPRESSO, molecular docking via MOE, and advanced material characterization techniques such as XRD, IR, NMR, mass spectrometry, TGA/DTA/DSC, SEM, AFM, TEM, XPS, and photoluminescence spectroscopy.
Dr. Elsenety focuses on applications in energy conversion, sensing, and environmental remediation, including perovskite and dye-sensitized solar cells, chemical and biosensors, and photocatalytic water treatment. He also employs artificial intelligence, machine learning, and deep learning to guide the design and optimization of functional materials. His interdisciplinary approach bridges synthesis, characterization, and computational modeling to advance next-generation materials for sustainable technologies.
This study explores novel materials for enhancing solar cell efficiency through nanotechnology approaches.
Application of neural networks to predict material properties and accelerate discovery processes.
Development of efficient catalysts using nanostructured materials for hydrogen production.