Our projects

Machine learning methods will be applied together with quantum mechanical simulations to understand the reactivity of a heterogeneous catalyst. The project aims to develop a theoretical rationale for high-throughput screening and design of catalyst materials for sustainable energy applications.

Rice is one of the leading crops that constitutes major staple food for almost half of the world’s population. Rice is a major source of energy, but it is poor source of micronutrients. There is a need to improve biofunctional attributes of rice using innovative food-food fortification techniques.

The proposed research will investigate the equity implications of policy strategies intended to achieve a net-zero energy transition in India, along socio-economic dimensions such as wealth, income, and economic risk, by developing simulation techniques to complement existing energy system models.

Chronic conditions – such as cardiovascular disease, cancers, and respiratory conditions – are now the leading causes of mortality and morbidity in high income countries. This project will investigate the patterns and causes of common chronic conditions in India using secondary health data.

Antimicrobial peptides kill microorganisms like bacteria, fungi and parasites by disrupting cellular membranes, despite the varying lipid compositions and structures of membranes in these organisms. How do lipid composition and physical structure of membranes relate to broad-spectrum activity?

Piezoelectric polymers can be used to convert mechanical stimulus into electrical energy, making them suitable for use in devices such as wearable sensors and implants. This project aims to develop novel bioceramic/piezoelectric Polymers nanocomposite for functional coatings on biomedical implants.

The primary aim of this project is to develop and verify a method of control of the occurrence of chaotic flutter in a wind turbine blade section to provide more efficient insight into its occurrence and avoidance in wind energy farms.

This project performs an in-depth study into the aspects of Designing (strength, dynamic response, fatigue and fracture Behaviour), Material selection, Manufacturing and Testing (arrangements of mounting, providing suspension, and assembly) of fiber-reinforced polymer matrix composite shafts.

Deep learning models adapt or generalize sub-optimally across different domains, especially when test domains contain classes unseen during training. This project will investigate open-set continual domain adaptation, with focus on both Computer Vision and Earth observation data.

We set out to develop catalysts based on earth-abundant metal complexes for electrocatalytic carbon utilisation, such as CO2 reduction and CH4 reforming. Small molecule complexes of 3d metal ions will be utilized as a precatalyst for synthesizing novel catalysts for energy conversion reactions.