Modeling and Experimental Studies of Electrolyte for Zinc Battery Systems

Abstract

Zinc-based batteries have emerged as promising alternatives to lithium-ion systems due to their low cost, inherent safety, and sustainability. However, challenges in electrolyte stability, dendrite formation, and limited lifetime have constrained their practical deployment. Addressing these barriers requires both experimental innovation and data-driven approaches for electrolyte and materials design. This thesis aims to advance zinc battery technologies through four interconnected directions: (i) development of novel electrolyte systems, (ii) optimization of zinc deposition and cycling performance, (iii) machine learning methods for early-stage lifetime prediction, and (iv) accelerated discovery of functional ionic liquids. Molten salt-derived Zn(TFSI)2 electrolytes were investigated for zinc–oxygen batteries across a wide temperature range. Electrochemical, spectroscopic, and microscopy analyses revealed the structural evolution of zinc oxide nanosheets during cycling and highlighted that Zn(TFSI)2·8H2O suppresses parasitic reactions more effectively than Zn(TFSI)2·18H2O, enabling improved reversibility and energy storage potential. Complementary studies on hydrated ZnCl2 electrolytes demonstrated how temperature and hydration level influence zinc nucleation, morphology, and cycling stability. ZnCl2·10H2O achieved 99.2% coulombic efficiency over 50 cycles, while higher operating temperatures increased discharge capacity from 17 mAh g-1 at -10 °C to 72 mAh g-1 at 40 °C. Artificial intelligence approaches were developed to classify and predict battery lifetime from early cycling data. Machine-learned models achieved up to 96% accuracy after only two cycles and 98% with additional data, while human-selected electrochemical features showed strong generalizability across chemistries. Deep learning methods reached 99.5% accuracy with extended cycling data but proved less transferable to systems with distinct degradation profiles. In parallel, convolutional and generative adversarial neural networks were applied to accelerate the discovery of ionic liquids for zinc batteries. These models improved property prediction and successfully generated new candidate electrolytes with enhanced performance at room temperature. Overall, this work provides an integrated experimental–computational framework for electrolyte optimization, electrochemical performance improvement, and AI-driven materials discovery. The findings pave the way toward more durable, efficient, and scalable zinc-based energy storage technologies.