Co-designing collectors, electrolytes, and electrodes for sustainable batteries: a roadmap of techno-economics, circularity, and machine learning
Document Type
Review
Publication Date
1-1-2026
Abstract
Global efforts to decarbonize power systems have intensified the search for batteries that pair high energy density and affordability with durability and ease of scale-up. Rather than treating electrodes, electrolytes, and current collectors as isolated pieces, this review takes a stack-level perspective on the entire cell. We survey recent progress in three-dimensional porous current collectors, solid-state and ionic-liquid electrolytes, and next-generation electrode materials, showing how coordinated design at the interfaces raises thermal stability, curbs polarization, and boosts fast-charge capability. A unified sustainability and cost dashboard compare eight leading chemistries, reporting gravimetric energy, specific power, price (USD/kWh), cradle-to-gate carbon intensity (kg CO₂-eq/kWh), and recyclability. Two primary design directions have distinguished themselves. First, highly porous, low-resistance collectors slash interfacial losses. Second, carefully tuned electrolyte–electrode interfaces suppress dendrite formation and broaden temperature tolerance. Finally, we outline a data-driven roadmap from electrolyte selection to transformer-based lifetime forecasting that can compress development timelines and lower scale-up risk. Taken together, these insights position co-designed collectors, electrolytes, and electrodes, reinforced by machine-learning tools and circular manufacturing, as a promising route to the next wave of sustainable batteries.
Publication Title
Journal of Solid State Electrochemistry
ISSN
14328488
DOI
10.1007/s10008-026-06583-3
Recommended Citation
Surender, G.; Gerard, Ong; Prasankumar, Thibeorchews; Omar, Fatin Saiha; Bashir, Shahid; Ramesh, S.; and Ramesh, K., "Co-designing collectors, electrolytes, and electrodes for sustainable batteries: a roadmap of techno-economics, circularity, and machine learning" (2026). Research Publications (2026 to 2030). 334.
https://knova.um.edu.my/research_publications_2026_2030/334