Zoomposium 27: 29 April 2022

Watch Zoomposium 27 (Passcode GP%0Pe%* )


Dr Guanchen Li, James Watt School of Engineering

Multiscale modelling of transport phenomena in electrochemical devices'

My research involves modelling transport phenomena in devices for energy storage and conversion. My experience ranges from the quantum level to the device level with applications including batteries, fuel cells, engines, and electronic devices. I am particularly interested in multi-physics coupling at electrochemical interfaces. My research aims to develop phenomenological theories that can predict device performance from basic measurements. I collaborated extensively with experimental groups in material science and chemistry to study the failure mechanism of ion-conductive ceramics. I also worked with control engineers and mathematicians on developing algorithms for battery simulations and management.

I hope to collaborate with experimental groups interested in electrochemistry, energy materials, interfaces, and electrochemical devices. I am keen to implement predictive physics-based or data-based models in digital chemistry and AI manufacturing. Collaborations on applied maths, computing science and quantum thermodynamics are also of interest.


Dr Yiji Lu, James Watt School of Engineering

‘Clean Energy Future’

Yiji is a Lecturer of Energy Conversion & Storage Systems working on the Renewable and Clean Energy Conversion, Energy Storage and Cleaner/Alternative Fuels to tackle the Climate Change challenges. He has published over 70 articles in high-quality international journals and peer-reviewed conferences. He has been a researcher in the £20m EPSRC National Centre for Energy Systems Integration, and a co-investigator of the CESI-funded Case Study on Integrated Zero-Carbon Hub. Yiji is also a member of the EPSRC Peer Review College and serves as an Associate Editor at Energy Reports, and an Editorial Member in various Energy Theme journals.


Dr Lauritz Thamsen, School of Computing Science

‘Let’s Benefit from Tons of Data... Without Having That Eat The World’

I do believe that there is immense potential in using data-driven methods and machine learning across disciplines. Though, if we are not careful, this development will further increase computing’s already significant environmental footprint. I therefore work on methods, tools, and architectures to support a more resource-efficient and sustainable use of distributed computing infrastructures for data-intensive applications.
If your work involves large-scale data processing in areas such as the life sciences, urban infrastructures, manufacturing, or energy systems or if you are also working towards more sustainable systems in general, I would certainly be happy to talk and discuss possibilities for collaboration.

First published: 6 April 2022