Dr Angan Sengupta, a distinguished academic, holds a PhD from IIT Bombay and currently serves as an Associate Professor in the Department of Chemical Engineering at IIT Jodhpur. His specialisation includes multiscale modelling and simulations to address non-exhaustive, interesting and industry-oriented problems related to fire and explosion safety, material design for sustainable energy and environment. Presently, in his research group (ACES Laboratory) the focus is on the application of molecular and classical thermodynamics along with transport theories to understand the thermo-physical phase behaviour of confined soft matters at a length of a few nanometres.

Key Publications:

• Mukesh Kumar, Angan Sengupta*, Nithin B. Kummamuru, Molecular simulations for carbon dioxide capture in silica slit pores", Materials Today: Proceedings, 102, Pages 194-202, (2024).
• Aparna Singh, Angan Sengupta*, Debanjan Guha Roy, œA Molecular Simulation Study on Pore-scale Behaviour of Nitrogen-based Fracking Fluids for Potential Geo-energy Applications", Microporous and Mesoporous Materials, 378, 113252, (2024).
• Mukesh Kumar, Angan Sengupta*, " A Molecular Simulation Study on Selective Adsorption of CO2 from an Industrial Ternary Gas Mixture inside Porous Silica and Kaolinite Adsorbents", Energy&Fuels, 39, 8540 ˆ’8566, (2025).
• Sarangi Veliyil Santhamma, Sahana Hanumanthappa Kenchgundi, and Angan Sengupta*, "A Molecular Simulation Study for Understanding the Effects of Two Water Channel Morphologies in Nafion with Various Hydration Levels on Adsorption and Nonideal Behaviour of Methanol at Operating Conditions of Direct Methanol Fuel Cells", Accepted, (2025).
• Anupam Kumari, Angan Sengupta, Ajay Agarwal, "The Role of Conducting Yarns in Shaping Next-Generation Wearable Sensors: A Review", Applied Materials & Interfaces, 17, 42541-42567, (2025).

Ongoing Projects:

• Designing Potential Adsorbents via Molecular Modelling and Simulations for the High Temperature Carbon Dioxide Capture. Funded by: SERB
• Molecular Designing of Polymer Electrolyte Membranes Using Computational Approach to Reduce Methanol Crossover in Direct Methanol Fuel Cell. Funded by: IITJ
• AI-ML-based Computational Material Design for Carbon Dioxide Capture. In Collaboration with the University of Buffalo, USA
• Data pipeline build-up and ML model deployment for Heat Exchangers. Funded by: IOCL