Metal–gas and metal–air batteries are electrochemical systems that use gaseous reactants such as oxygen or carbon dioxide at a porous gas-diffusion electrode. Representative systems include lithium–oxygen, lithium–carbon dioxide, sodium–air, zinc–air, aluminum–air, and iron–air batteries. These technologies are attractive because the gaseous cathode reactant does not need to be stored entirely as a solid active material, creating the potential for very high theoretical specific energy. Lithium–carbon dioxide batteries are also of interest because they combine energy storage with the electrochemical utilization of carbon dioxide. During discharge, the metal anode is oxidized, metal ions move through the electrolyte, and the gas is reduced at the cathode to form products such as lithium peroxide or lithium carbonate. During charging, these products must decompose reversibly and regenerate the original gas and metal. The major barriers are slow reaction kinetics, high charging overpotentials, electrically insulating discharge products, cathode-pore blockage, electrolyte decomposition, and unstable metal anodes. Current strategies include bifunctional catalysts, redox mediators, stable electrolytes, protected interfaces, hierarchical porous cathodes, anode-protection layers, and controlled gas-management systems. Recent lithium–carbon dioxide studies have demonstrated improved cycling, reduced overpotential, and pouch-cell operation, although full-system mass, gas purification, and practical energy efficiency remain critical concerns. These batteries could eventually serve specialized high-energy or long-duration storage applications, but commercialization will require major improvements in reversibility, cycle life, safety, and system-level scalability.

Lithium-ion battery cathode material development focuses on designing positive-electrode materials that largely determine cell voltage, capacity, energy density, cycle life, safety, and cost. Major commercial cathodes include layered oxides such as NMC and NCA, lithium iron phosphate, and manganese-based materials, while next-generation research is exploring high-nickel, lithium-rich manganese-based, cobalt-free, nickel-free, and disordered-rocksalt chemistries. Cathode development is particularly important for electric vehicles and large-scale energy storage because improvements in the cathode can directly increase driving range, reduce battery weight, and lower dependence on critical minerals. High-nickel materials provide high capacity but often suffer from surface instability, oxygen release, particle cracking, and thermal degradation. Researchers therefore employ elemental doping, protective surface coatings, single-crystal particles, concentration-gradient structures, and advanced electrolyte formulations to improve stability. In-situ spectroscopy, microscopy, computational modeling, and machine learning are increasingly used to understand degradation mechanisms and accelerate material discovery. Practical development must also address precursor quality, synthesis reproducibility, electrode density, manufacturing yield, and cell-level performance. Supply-chain security and recycling are becoming integral parts of cathode design, encouraging the use of abundant elements and recoverable material systems. The future is likely to involve a diversified portfolio of cathodes optimized for different applications rather than a single chemistry replacing all existing materials. 

AI-enabled Prognostics and Health Management integrates condition monitoring, fault diagnosis, degradation forecasting, remaining useful life estimation, and maintenance decision support. This approach is important because it can reduce unexpected failures, production downtime, unnecessary inspections, maintenance costs, and safety risks. AI models analyze data such as vibration, temperature, acoustic signals, electrical measurements, images, operating conditions, and maintenance records to identify early signs of degradation. Machine-learning and deep-learning methods can be used for anomaly detection, fault classification, health-state estimation, and remaining useful life prediction. Current research is moving toward multimodal sensor fusion, physics-informed learning, self-supervised learning, transfer learning, digital twins, and edge-based real-time monitoring. These approaches aim to improve performance when failure data are limited or operating conditions change over time. Major challenges include rare and imbalanced failure data, sensor noise, model drift, differences between machines, uncertainty in predictions, and limited model explainability. Industrial adoption also requires reliable validation methods, cybersecurity, interoperable data systems, and effective integration with existing maintenance workflows. The long-term goal is an intelligent health-management system that continuously assesses equipment condition, predicts future risks, explains its recommendations, and supports safe and cost-effective maintenance decisions.