Cell Design and Materials: Alternative Chemistries
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Text on page 410
Zinc-ion Batteries Research in 2025 continues to push zinc-ion batteries (ZIBs) toward greater durability, manufacturability, and commercial relevance. Basic Chemistry
Academic Work
Zinc-ion batteries are being developed using both near neutral and basic electrolytes. These batteries have a pathway to $50/kWh cell cost level due to the low cost of materials and existing supply chain for the primary battery market. With the theoretical max specific capacity of 308 mAh/g, a lot of up and coming companies and research is being performed to advance zinc-ion batteries to solve manganese dioxide dissolution and improve rechargeability.
Cathode research remains the dominant focus, especially around Mn-based materials to improve cycle life, stability and scalability. This includes:
Novel cathode materials & hybrid designs (CuO2, iPOPs, Polyaniline)
Thinner and more scalable cathodes
Carbon coating processes and structural tuning (β-MnO2, α-MnO2)
Doping strategies (Ni, Bi, K, Fe, Zn) to improve stability and conductivity
Adhesion improvements, titanium dioxide surface interfaces, and dissolution shielding approaches (Carboxyl-CNTs, PN-MnO2)
Hydrothermal synthesis(high valence, polymorphs) and ion-exchange processes adapted for manufacturability
Vanadium sulfide fundamental studies for alternative chemistries
Dry-electrode and other manufacturing capabilities Electrolyte development continues to minimize dendrites and extend lifetime
Electrolyte additives for suppression of side reactions (Al2(SO4)3, Mannitol)
Hydrogel electrolytes (Polyanionic, Salt-Concentrated) to improve safety and Zn interface stability
Engineering of cathode–electrolyte interfaces to reduce degradation Supporting improvements indicate the field is transitioning beyond core chemistry:
Current collector enhancements
Separator advancements for ion selectivity and safety Development of characterization and data-driven approaches:
Characterization of failure modes (in situ, modeling)
Mechanistic fundamentals to correlate structure and performance
Incorporating machine learning for material optimization
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