Supercapacitors (SCs) are gaining attention for Internet of Things (IoT) devices because of their impressive characteristics, including their high power and energy density, extended lifespan, significant cycling stability, and quick charge–discharge cycles. Hence, it is essential to make precise predictions about the capacitance and lifespan of supercapacitors to choose the appropriate materials and develop plans for replacement. Carbon-based supercapacitor electrodes are crucial for the advancement of contemporary technology, serving as a key component among numerous types of electrode materials. Moreover, accurately forecasting the lifespan of energy storage devices may greatly improve the efficient handling of system malfunctions. Researchers worldwide have increasingly shown interest in using machine learning (ML) approaches for predicting the performance of energy storage materials. The interest in machine learning is driven by its noteworthy benefits, such as improved accuracy in predictions, time efficiency, and cost-effectiveness. This paper reviews different charge storage processes, categorizes SCs, and investigates frequently employed carbon electrode components. The performance of supercapacitors, which is crucial for Internet of Things (IoT) applications, is affected by a number of their characteristics, including their power density, charge storage capacity, and cycle longevity. Additionally, we provide an in-depth review of several recently developed ML-driven models used for predicting energy substance properties and optimizing supercapacitor effectiveness. The purpose of these proposed ML algorithms is to validate their anticipated accuracies, aid in the selection of models, and highlight future research topics in the field of scientific computing. Overall, this research highlights the possibility of using ML techniques to make significant advancements in the field of energy-storing device development.

Semiconductor ionic electrolytes, especially heterostructure composites, have a significant role in enhancing oxide ion conductivity and peak power density (PPD) because of their interfacial contact. In this work, the fluorite structure CeO₂ and spinel-based CoAl₂O₄ samples, as a heterostructure composite electrolyte, are successfully fabricated. The p-type CoAl₂O₄ and n-type CeO₂ heterostructure (CeO₂-CoAl₂O₄) used as an electrolyte exhibits a cell performance of 758 mW/cm² under fuel cell H₂/air conditions at 550 °C, which is quite higher than the pure CoAl₂O₄ and CeO₂ fuel cell devices. Scanning electron microscopy (SEM) and high-resolution transmission electron microscopy (HR-TEM) verified the heterostructure formation including the morphological analysis of the prepared heterostructure composite. The heterostructure-based CeO₂-CoAl₂O₄ composite achieved a higher ionic conductivity of 0.13 S/cm at 550 °C temperature, which means that the constructed device successfully works as an electrolyte by suppressing electronic conductivity. Meanwhile, the obtained results demonstrate the semiconductor ionic heterostructure effect by adjusting the appropriate composition to build heterostructure of the n-type (CeO₂) and p-type (CoAl₂O₄) components and built-in electric field. So, this work exhibits that the constructed device can be effective for energy conversion and storage devices.

The development of a single junction photoelectrode material having specific properties is essential and challenging for the efficient application in solar water splitting for oxygen production and a high value-added product, hydrogen. Moreover, the present material solutions based on binary metal oxides offer limited catalytic activity and hydrogen production efficiency. Therefore, it is paramount to develop and exploit a unique range of materials derived from ternary metal oxides with specifically engineered properties to advance in photoelectrochemical (PEC) water splitting. Among the ternary oxides, copper vanadates offer promising characteristics, such as a narrow bandgap and catalytic surface properties along with favorable band edges for facile oxygen evolution reaction (OER), which is considered the bottleneck step in performing overall water dissociation. Furthermore, the copper vanadates allow the tuning of the stoichiometry through which a wide range of polymorphs and materials could be obtained. This review provides a complete outlook on the range of copper vanadates and the established synthesis approach, morphology, crystal structure, band edge properties, and PEC characterizations. Mainly, the underlying charge dynamic properties, carrier path length, effect of doping, and influence of surface catalysts are discussed. The review concludes that the advancement toward obtaining low-bandgap materials is a main challenge to overcome the limitations for efficient water dissociation to OER and copper vanadates, which offer a promising solution with their unique properties and advantages. Importantly, intense and strategically focused research is vital to overcome the scientific challenges involved in copper vanadates and to explore and exploit new polymorphs to set new efficiency benchmarks and PEC water splitting solutions.