With the rapid deployment of renewable energy and battery energy storage systems (BESS), reliable battery diagnostics are essential for ensuring safety, performance, and long service life. This PhD project will develop next-generation diagnostic and prognostic tools by combining physics-based electrochemical models with machine learning for accurate estimation of battery state, health, and degradation.

Reliable battery operation at low temperatures is critical for defense, drones (UAVs), aerospace, electric mobility, and grid-scale energy storage. This PhD project aims to develop high-performance sodium-ion batteries for cold-climate operation through a combination of experimental research and physics-based electrochemical modeling. The research will focus on understanding ion transport, reaction kinetics, and degradation using advanced electrochemical characterization (e.g., EIS and cycling) and computational modeling.

Study of phase change solvents for CO2 adsorption 

GCMC + MD simulations in LAMMPS or GCMC in RASPA for prediction of CO2 uptake

in this project, one has to learn and implement population balance based modeling and simulate the existing computer code to predict anisotropic ZnO nanoparticle shape and size, as seen in our own experimental data. Such nanoparticles have better ability for dye degradation in wastewater treatment.

The work is mostly computational and will be together with other members in our research group.

We have developed a house-hold scale (16 litre), water purification device, based on nanoparticle-impregnated activated carbon (AC) composite, for disinfection of drinking water. It works by killing of microorganisms by metallic nanoparticles in the composite, whilst the AC part of the composite removes other organic and inorganic pollutants from water. This gives clean, drinking water, in our gravity-driven device, which does not need any electricity to flow water or kill microbes, as in a UV-lamp of a traditional filter, thereby saving energy.

Continuous monitoring of water quality parameters, like total dissolved solids, heavy metals, inorganic ions, organic pollutants etc.is an important measurement, to ascertain quality and use of a water body. This is critical for both a flowing water-stream (river, canal) or a stagnant water-pool, like a lake. To that end, in this project, one has to work with chemical reagants, which have been tested with both synthetic and field-water samples, for various species, like arsenic, fluoride, chromium, iron etc.

The project will focus on developing prototype applications for a few use cases of interest to the chemcials industry. The applications will involve use of AI / ML tools both APIs as well as running custom models on on+site hardware. Applications include image recognition for plant safety, digitalization of legacy measurements and some others involving custom LLM creation. 

This project explores methane pyrolysis as a route to produce clean hydrogen, with solid carbon as a valuable co-product. You will work on developing advanced catalysts with high activity and resistance to deactivation, along with designing and operating reactors for high-temperature conversion. The project combines catalyst development, reaction kinetics, and reactor engineering.