Comparing Sodium-ion Batteries Lithium-ion batteries across

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  There have been concerns regarding the dwindling supply of lithium. The increasing demand for batteries in Electric vehicles, energy storage grids, and consumer electronics, and the high cost of lithium and other raw materials used in lithium-ion batteries (LIBs). A possible alternative for LIBs is sodium-ion batteries (SIBs), owing to their similar properties. This review article compares LIBs’ and SIBs’ operating principles. I then look at the properties of SIBs and LIBs; we compare these battery technologies across three applications: grid storage, electric vehicles, and consumer electronics. The research was done in the form of a literature review, where I found that SIBs are most suitable for grid storage. They could see some applications in electric vehicles, but they are not viable for consumer electronics. For commercialization, there needs to be a rise in the energy density of SIBs and a fall in their cost, which is why future studies on SIBs should be focused on the same. This paper goes through various sources and should provide an idea of where SIBs could potentially replace LIBs.

My Mentor for this research endeavor was Stepan Ozerov, a PhD candidate from Purdue University. This paper is currently under the publication at the International Journal of High School Research.

 I am conducting research in quantum machine learning at Nirma University under the supervision of Prof. Nagendra Gajjar. My work evaluates training efficiency and predictive performance of quantum models relative to classical machine learning approaches, with the goal of identifying conditions under which quantum advantage may emerge. I compare training time, convergence behavior, and accuracy across regression, image, and video tasks using quantum models implemented in PennyLane AI, focusing on practical limitations of near-term quantum ML systems. 

Preliminary results show that while classical models currently train faster on available hardware, quantum models achieve competitive accuracy on higher complexity tasks, indicating promising directions as hardware scales.