Battery formation
Designing faster first-charge protocols with physics-based models of the solid-electrolyte interphase and lithium plating.
University of Toronto
Mohamed
El Sayed.
Publishing as Mohamed Basyoni
Understanding batteries.
Making intelligence useful.
I connect electrochemistry, physics-based models, and machine learning to help build better batteries—from the first charge to the manufacturing floor.
Ph.D. candidate, Electrical & Computer Engineering
Battery scientist & R&D engineer
to practical impact.Toronto, Canada
Electrochemistry+Physics-informed ML+Battery manufacturing
Research
The first charge.
A better starting point.
My doctoral work asks how we can shorten lithium-ion battery formation while preserving the physical constraints that make a cell safe and reliable.
Doctoral researchListen to the cell.
Listen to the cell.
Learn from the physics.
Combining snapshot electrochemical impedance spectroscopy with a leak-audited digital twin to inform fast-formation decisions.
Supervised by Francis Dawson and Jason Hattrick-Simpers, University of Toronto.
Explore modeling workElectrochemical intelligence
Finding the few impedance measurements that matter, so battery diagnostics can move from the laboratory into manufacturing.
Machine learning for physical systems
Building interpretable surrogates with rigorous validation across temperature, charging rate, and cell capacity.
Publications & manuscripts
Ideas, tested and shared.
Scientific Reports · 13, 13369
A novel machine learning approach for surface roughness quantification and optimization of cast-on-strap lead-antimony alloy via two-point correlation function
M. Basyoni, Y. Jiao & N. K. Allam
Snapshot-EIS-guided, physics-aware machine learning for fast lithium-ion battery formation: a leak-audited digital-twin framework
M. Basyoni, J. Hattrick-Simpers & F. Dawson
Hierarchical MWCNT/graphene conductive scaffolds in the negative active material of automotive lead-acid batteries enable delayed gassing, improved charge acceptance and extended cycle life
N. K. Allam, H. Kasken & M. El Sayed
Experience
Across disciplines.
Close to the real problem.
A foundation in physics, training in materials science, and hands-on battery R&D shape how I approach research.
Research & industry
2024 — present
Doctoral Researcher
University of Toronto
Physics-aware machine learning, snapshot EIS, and digital twins for lithium-ion battery formation.
2020 — 2024
R&D Engineer
German Company for Manufacturing Batteries
Lead-acid grid design, alloy and active-material development, and cell qualification testing.
2019 — 2024
Research Assistant, Energy Materials
The American University in Cairo
Surface characterization with machine learning and advanced carbon additives for commercial batteries.
2017 — 2018
Research Assistant, Nanophotonics
The American University in Cairo
Optical-fibre modeling and nanoplasmonic structures for sensing and energy harvesting.
Teaching & building
Knowledge becomes valuable
when others can use it.
I teach circuits, electromagnetism, and engineering mathematics at the University of Toronto. Previously, I taught physics laboratories at the American University in Cairo.
I’m also developing ImpedanceIQ, a pre-incorporation formation-intelligence software venture, and building independent software projects that connect research, engineering, and deployment.
Open the campaign concept studio demoLet’s connect
Good questions make
great collaborations.
For battery R&D, research collaborations, and opportunities
at the intersection of physical systems and machine learning.