The project would deal with intensification of unit operations such as desalting of crude oil or air cleaning or phase transitions using strong electric fields. The student would be conducting experiments, simulations and theory. The simulations would be based on COMSOL multiphysics, while the experiments will involve videography and microscopy. The aim is to made new observations at microscopic levels and harness them for process intensification at industrial scale. The group has in the past demonstrated such approach for desalters and air-cleaners.

Organ-on-chip technologies are emerging as important tools for creating realistic laboratory models of human tissues, with applications in drug testing and disease studies. This project aims to design, fabricate, and experimentally validate microfluidic organ-on-chip platforms that mimic essential features of real tissues. The work will involve computer-aided design of microscale devices, soft-lithography or laser-based fabrication, basic material and surface characterization, and operation of microfluidic systems under controlled flow conditions.

With increased penetration of renewable electricity from wind and solar, that modern electricity grid is expected to be more dynamic as it actively responds to supply and demand of renewable electricity. In this scenario, redox flow batteries are seen as a key technology for longer-term energy storage (time ≥ 4 hours). This SLP is an opportunity to perform a review of various chemistries for flow batteries, compute the levelized cost of storage and determine the key limiting factors that prevent widespread commercialization of this technology.

Hydrogen gas is a common chemical feedstock in a wide variety of industrial processes and also a potential energy carrier. Conventional process for hydrogen generation are carbon intensive. Water electrolyzers are electrochemical devices that use electricity to split water into its elemental components (H2 and O2).

Isotactic polypropylene (iPP) is the second most produced plastic (nearly 100 million tons/annum). iPP is a semicrystalline thermoplastic, and is typically processed from the melt state into final products. Thermoplastics can be melted and re-processed: therefore, iPP can, in principle, be recycled thermomechanically. However, melt processing takes place at elevated temperatures, typically near 240oC and can result in scission of molecular bonds. This results in deterioration of properties of the plastic.

Catalysis and Process intensification in the manufacture of fine chemicals: The work involves identification of catalyst, and the best possible reaction conditions for the process of given chemical (e.g. furfuryl alcohol, acetyl furan). A process flow diagram will be developed and simulate to study the techno-economic analysis. 

Reduction in silver loss in copper electro-fining process. The ore used for copper manufacturing contains precious metals like silver. The project involves study of the existing industrial process in detail. Identify the reasons for the silver loss and provide solutions and create a proof of concept in the lab to taken for commercialization. 

Sustainability through circular economy in foundry sand reclamation. The project deals with involves process intensification in the foundry sand reclamation process developed by IITB. The objective is to reduce carbon and water footprint without compromising the cost-effectiveness. 

Our group has recently introduced a new class of materials, comprising polyethylene chains covalently grafted to a layered inorganic substrate (See: https://pubs.acs.org/doi/abs/10.1021/acsapm.3c00649). This novel material, that we refer to as PE-clay, has remarkable and unprecedented properties. For example, it exhibits adhesion to metal surfaces with strengths that are over 100X that for regular polyethylene, even at an inorganic loading of less than 2%. Further, the modulus of this material is significantly higher than that for polyethylene of comparable crystallinity.

Data-driven machine learning models are increasingly being used to generate stable materials which form the basis for rational design of novel catalysts and battery materials. These models are trained on a database of stable materials computed using first principles methods. Based on this database, architectures such as the variational auto-encoder, generative adversarial networks and (more recently) the transformer learn an implicit probability distribution. This distribution is used to decides if a given material is stable.