Portrait of Dr. Mohamed Elsenety
75+ Publications
DFT & AI Chemist
Perovskite PVs

Researcher · Lecturer · Innovator

Dr. Mohamed
Elsenety

Pioneering |

I bridge experimental synthesis, computational DFT, and machine learning to engineer next-generation functional materials — from perovskite solar cells to intelligent chemical sensors.

Publications (75+) View CV Explore ML Apps
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About Me

Scientist, Educator & Innovator

Currently a Researcher at the Institute of Nanoscience and Nanotechnology (INN), NCSR "Demokritos," Athens — and a faculty member at Al-Azhar University, Cairo.

My interdisciplinary approach fuses solid-state synthesis, crystal structure determination, high-level DFT calculations, and AI-driven discovery to address global challenges in clean energy and environmental remediation.

Athens, Greece & Cairo, Egypt

Dr. Mohamed Mahmoud Elsenety is a chemist and materials scientist whose work spans the full chain from molecule to device. He holds a Ph.D. from the National and Kapodistrian University of Athens and has held postdoctoral positions in Greece and Taiwan (NTHU).

His research integrates experimental and theoretical approaches — solid-state and solution-phase synthesis, crystal structure determination, Rietveld analysis, DFT calculations using CASTEP, Gaussian, and Quantum ESPRESSO, molecular docking via MOE, and advanced characterization (XRD, IR, NMR, mass spectrometry, TGA/DTA/DSC, SEM, AFM, TEM, XPS, PL spectroscopy).

He also employs artificial intelligence, machine learning, and deep learning to guide the design and optimization of functional materials, bridging computational modeling with real-world applications in solar energy, sensing, and water purification.

Research Focus

Areas of Expertise

☀️
Perovskite & DSSCs

Design and fabrication of high-efficiency perovskite and dye-sensitized solar cells for next-generation photovoltaics.

⚛️
Computational DFT & MD

Density functional theory and molecular dynamics simulations using CASTEP, Gaussian, and Quantum ESPRESSO on Linux/Windows.

🤖
AI & Machine Learning

Neural networks and deep learning for property prediction, materials discovery acceleration, and intelligent chemical sensors.

💧
Photocatalysis & Water Treatment

Nanostructured catalysts for solar-driven hydrogen evolution and degradation of organic pollutants in wastewater.

🔬
Advanced Characterization

XRD, Raman, FTIR, NMR, SEM/TEM/AFM, XPS, PL, TRPL, CV, EIS, TGA/DTA/DSC — from nanoscale to bulk materials.

🧪
Synthesis & Crystallography

Organic/inorganic synthesis, MOFs, COFs, polymers. Crystal structure solving and Rietveld analysis for 0D–3D materials.

Research Keywords

What I Work On

⚡ Perovskite Solar Cells 🧠 Machine Learning 🌊 Photocatalysis 🔭 XRD / Crystallography 🔥 DFT Calculations 🧬 MOFs & COFs 💡 DSSCs 🤖 Deep Learning 🧪 Nanostructures 📊 Rietveld Analysis 🛢️ Wastewater Treatment 📡 Optical Sensors ⚗️ Quantum ESPRESSO 🔮 Molecular Docking 🌡️ TGA / DSC / DTA 🔬 SEM / TEM / AFM 💻 CASTEP / Gaussian ⚙️ XPS / UPS
Technical Skills

Core Competencies

A balanced researcher equally comfortable at the synthesis bench, the characterization suite, and the computational workstation — with strong data science skills applied throughout.

Diploma in AI — Machine Learning & Deep Learning, Space Code Academy, Cairo (2021–2022)
Experimental
Material Synthesis (Organic / Inorganic / Hybrid)95%
Advanced Characterization (XRD, SEM, XPS, PL…)92%
Solar Cell Fabrication & Testing88%
Computational
DFT (CASTEP / Gaussian / Quantum ESPRESSO)90%
Python / ML / Deep Learning85%
Crystal Structure Analysis & Rietveld Refinement93%
Tools & Software
Materials Studio / VESTA / CrystalMaker88%
Origin / MATLAB / Statistical Analysis82%
Selected Work

Research Activity

View All Publications
Synthesis and Characterization
Synthesis & Characterization

Organic/inorganic compounds, complexes, MOFs, COFs, and polymers — characterized via a comprehensive analytical toolkit.

XRD · Raman · FTIR · NMR · UV-Vis · PL · TRPL · AFM · TGA · SEM · XPS · GC-MS · HPLC

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Crystallography
Crystallography & Crystal Structure Solving

Single-crystal and powder X-ray diffraction, Rietveld refinement for 0D, 1D, 2D, and 3D materials — revealing atomic-scale architecture.

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Computational DFT
Computational DFT & Molecular Dynamics

High-level DFT calculations and MD simulations using CASTEP, Gaussian, and Quantum ESPRESSO on both Windows and Linux platforms.

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Advanced Materials for Photovoltaics
Advanced Materials for Photovoltaic Applications

Exploring novel perovskite and dye-sensitized solar cell architectures to push the boundaries of solar energy conversion efficiency.

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Machine Learning in Materials Science
Machine Learning in Materials Science

Neural network models that predict material properties and dramatically accelerate the discovery and optimization of functional materials.

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Nanostructured Catalysts for Renewable Energy
Nanostructured Catalysts for Renewable Energy

Development of efficient nanoscale catalysts for solar-driven hydrogen production and photocatalytic degradation of environmental pollutants.

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Funding & Projects

Grants & Collaborations

Secured competitive funding from Egyptian and European agencies, leading projects at the intersection of materials science, renewable energy, and circular economy.

2024 – 2025 · EU NextGeneration
3GPV-4INDUSTRY Project
Development of efficient third-generation photovoltaic materials for industrial applications.
2022 – 2024 · ASRT Egypt
Precious Metals Recovery from Urban Waste
Circular economy approach to recover critical metals from electronic and urban waste streams.
2022 – 2024 · ASRT Egypt
Recycling Old Acid Batteries into Solar Cells
Transforming end-of-life lead-acid batteries into perovskite solar cell precursors.
2022 – 2023 · ASRT Egypt (Bedaya)
Perovskite Solar Cells & Machine Learning
Supervised 5 students in integrating AI-guided optimization into perovskite device fabrication.