Vanesa Muñoz-Perales

Ph.D.

I am a School of Engineering Distinguished Postdoctoral Fellow at the Massachusetts Institute of Technology, working in the Electrochemical Energy Lab (Prof. Yang Shao-Horn) and The Johnson's group (Prof. Jeremiah A. Johnson). My research focuses on the accelerated discovery of new electrolytes for beyond-lithium batteries using AI and high-throughput experimentation. I investigate formulations for higher ion transport in liquid, polymer and gel-based energy materials. During my PhD at Universidad Carlos III de Madrid (advisor Prof. Marcos Vera and in collaboration with Antoni Forner-Cuenca (EMS, TUe)), I advanced the field of electrochemical flow reactors through the improvement of charge/mass transport by engineering alternative flow cell architectures and porous electrode microstructures. Right after, I worked on membraneless microfluidic redox flow batteries using new electrolyte concepts under the advise of Dr. Rebeca Marcilla in the Electrochemical Processes Unit, IMDEA Energy.

About me

Advancing the energy transition with better batteries

Beyond-Lithium Batteries

With lithium-ion batteries facing resource, safety, and performance challenges, developing beyond-lithium chemistries is essential for future energy storage. To address the unique challenges these alternative systems face, I develop novel electrolytes in liquid, gel, and polymer forms. My work spans from the molecular synthesis of electrolyte materials to detailed investigations of ion transport mechanisms and electrochemical properties.

Engineering Flow Cell Architectures for Improved Mass Transport

Enhancing mass transport in redox flow batteries relies on the interplay between flow field architecture and porous electrode structure. I design and test new geometries that reduce pressure losses while improving electrochemical performance, establishing structure–property–performance relationships that guide the development of more efficient and scalable flow battery systems.

High-throughput Experimentation and Screening

High-throughput experimentation enables rapid data generation across a wide range of conditions, providing the foundation for discovery in electrochemical systems. This data-driven approach is essential for identifying trends, guiding design, and accelerating progress—particularly when integrated with machine learning and AI tools that depend on high-quality, diverse datasets.

Cell Continuum Modeling

Continuum models offer a powerful framework for predicting and optimizing the macroscopic behavior of electrochemical devices. By evaluating operating conditions, geometry, and material properties, these models help improve performance and mitigate undesirable effects in battery systems.