Cover
Volume 2, Issue 3 (2026) – 5 articles
Cover Picture: Heterostructure materials with well-defined interfaces between distinct materials generate key interfacial physical effects, including band bending, built-in electric fields, and lattice strain. An inferior heterointerface severely hinders ion transport across the interface and exacerbates interfacial side reactions, leading to a shorter battery cycle life. In this review, we systematically examine the design principles and functional mechanisms of heterostructures for rechargeable batteries. The fundamental properties of heterostructures can be categorized into built-in electric fields that accelerate charge transfer, catalytic activity arising from interfacial defects, and the buffering effect of rigid-flexible coupled structures. We further summarize the latest advancements in heterostructures to mitigate electrode phase transitions and volume expansion in metal-ion batteries, including the realization of catalytic conversion of lithium polysulfides in lithium-sulfur batteries, the reduction of overpotential for oxygen reactions at the Mott-Schottky interface in metal-air batteries, and the inhibition of hydrogen evolution in aqueous batteries. This review aims to provide guidance for the rational design of heterostructure electrodes in advanced rechargeable batteries, shifting strategies from empirical material combinations toward targeted interfacial functionality.
Back Cover
Volume 2, Issue 3 (2026) – 5 articles
Back Cover Picture: State-of-health (SOH) estimation is a prerequisite for the safe and efficient operation of electrochemical energy storage systems (ESS), but practical deployment remains limited by incomplete observability, inconsistent health labels, domain shift, and insufficient trustworthiness under changing operating conditions. This review synthesizes machine-learning-based SOH estimation across electrochemical ESS, covering lithium-ion, sodium-ion, and aqueous batteries, supercapacitors, and battery-supercapacitor hybrid configurations. The literature is organized along an end-to-end machine-learning-driven pipeline, including health target definition and labeling, field data and observability, health-indicator representation, model learning and generalization, benchmarking and trustworthiness, and deployment-oriented integration. The review shows that reliable SOH estimation does not depend solely on model architecture, but on the alignment among target semantics, signal availability, feature representation, supervision regime, evaluation design, and operational objective. It further highlights that health semantics are device-dependent and should not be transferred mechanically across batteries and supercapacitors. Representative learning strategies are discussed, including structured regression, temporal deep models, transfer and online adaptation, and physics-guided or uncertainty-aware learning. The review also extends the discussion from component-level diagnosis to system-level integration in batteries, supercapacitors, and hybrid ESS. Future progress will depend on observability-aware target design, robust evaluation, and trustworthy deployment rather than algorithmic novelty alone.
