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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. [ABSTRACT FROM AUTHOR]

Photoelectrochemical (PEC) water splitting is one of the most sustainable approaches for converting solar energy into hydrogen fuel. Affordable and robust photoelectrodes are crucial for the commercialization of PEC technologies. Recently, transition metal-based co-catalysts, especially Ni- and Fe-based catalysts, have attracted much interest owing to their exceptional OER characteristics. Given this, we here proposed the decoration of a Fe-Ni-based cocatalyst on the surface of the TiO2 photoanode for PEC water splitting. The TiO2 photoanode was hydrothermally synthesized and then decorated by Fe-Ni hydroxide catalyst using photo-assisted electrodeposition. The optimized TiO2/FeNiOOH photoanode exhibited the maximum photocurrent density value of 1.36 mA cm−2, which is almost twice the value obtained for bare TiO2, at 1.23 V vs RHE under the AM 1.5 G illumination. Due to the enhanced light absorption in the UV region, the optimized photoanode exhibited remarkable IPCE and photoconversion efficiency of 87.8% and 0.93%, respectively. Furthermore, excellent faradaic efficiencies of ∼90% for H2 and ∼70% for O2 generations were obtained. Predominantly, the enhancement in the photocurrent potentials was explained in detail. Our study shows the roles and benefits of using bimetallic catalysts with TiO2 photoanodes for sustainable water-splitting applications. [ABSTRACT FROM AUTHOR]