Special Issue Information

The development of high-performance batteries and supercapacitors has created an urgent need for advanced electrode materials. As key components of energy storage devices, electrodes directly impact electrochemical performance, cycle stability, and energy efficiency. However, traditional electrode materials face challenges such as structural degradation, limited conductivity, and slow ion transport, which hinder further advancements in energy storage technology.

The emergence of smart electrodes, which integrate nanomaterials, self-healing materials, and real-time monitoring capabilities, provides new pathways to address these limitations. By incorporating adaptive properties that respond dynamically to temperature, pressure, and voltage fluctuations, smart electrodes enhance energy storage efficiency, durability, and safety. Additionally, integrating machine learning (ML) in electrode design has enabled a paradigm shift in materials discovery, offering predictive insights into performance, degradation mechanisms, and structural evolution over time.

To advance this field, researchers are exploring the design of nanomaterials such as graphene, MXenes, metal-organic frameworks (MOFs), and transition metal oxides, which significantly enhance pseudocapacitance, electrochemical double-layer capacitance, and ion transport dynamics. At the same time, self-healing materials and real-time monitoring technologies are being developed to improve electrode lifespan and safety, while dendrite suppression strategies for lithium-based batteries are proving crucial for long-term reliability. Meanwhile, breakthroughs in flexible and stretchable smart electrodes are unlocking new applications in wearable electronics and next-generation energy storage devices.

This Special Issue seeks to highlight cutting-edge advancements in smart electrode materials, design strategies, and integration techniques that will shape the future of energy storage. We welcome original research and review articles that delve into novel synthesis methods, fundamental mechanistic studies, machine learning-driven material optimization, and scalable fabrication approaches. By fostering interdisciplinary collaboration and knowledge exchange.

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