Silicon carbide (SiC) has attracted increasing attention as a robust photoelectrode material for solar water splitting due to its exceptional chemical stability, mechanical strength, and resistance to photocorrosion. Recent advances in nanostructuring—particularly the development of nanoporous SiC architectures—have dramatically improved light absorption, charge separation, and charge transport in this material. This review summarizes current strategies to enhance the PEC performance of SiC, including hierarchical nanostructuring, defect engineering (e.g., doping to tailor band structure), heterojunction formation with co-catalysts, and incorporation of plasmonic nanoparticles. Remaining challenges are discussed, notably the wide band gap of common SiC polytypes (limiting visible-light utilization) and rapid charge-carrier recombination. In addition, we examine the techno-economic prospects for SiC-based PEC systems, outlining the efficiency and durability benchmarks required for commercial hydrogen production. Finally, we propose future research directions to achieve efficient, durable SiC photoelectrodes and to guide the development of scalable PEC water-splitting devices. This review uniquely integrates material design strategies with techno-economic evaluation, providing a roadmap for SiC-based PEC systems.

In this study, a fully coupled three-dimensional multiphysics CFD model of a photoelectrochemical (PEC) water splitting cell incorporating a ZnO/BiVO₄ photoanode was developed using COMSOL Multiphysics^® 6.1. The model integrates semiconductor charge transport, ionic transport (diffusion, migration, and convection), electrochemical kinetics, and fluid dynamics within a widely adopted experimental configuration. This work focuses on coupling the dominant transport phenomena governing macro-scale PEC behavior under realistic operating conditions, allowing the overall PEC behavior to be captured without resolving microscopic interfacial complexities. The simulation results show good agreement with previously reported experimental data in terms of photocurrent density, demonstrating the capability of the model to reproduce photocurrent behavior. The developed framework provides insight into the interplay between photogenerated charge transport and electrochemical reactions and can serve as a predictive tool for analyzing and optimizing PEC system performance prior to experimental implementation.

В данной работе представлена поэтапная методика синтеза гетероструктуры BiVO4/ZnO, сформированной на основе нанотрубчатых массивов оксида цинка. На первом этапе синтеза наностержни ZnO были получены гидротермальным методом, который позволяет контролировать морфологию, размеры и однородность образцов. Далее полученные наностержни подвергались электрохимическому травлению, в результате чего происходило селективное растворение центральной части структур и формирование полых нанотрубок ZnO с развитой удельной поверхностью. Синтезированные ZnO-нанотрубки использовались в качестве шаблона для последующего построения гетероструктуры BiVO4/ZnO. Осаждение BiVO4 осуществлялось двухэтапным методом, обеспечивающим равномерное распределение материала по поверхности нанотрубок и формирование квантовых точек BiVO4, что способствует улучшению межфазного контакта и разделению фотогенерированных носителей заряда. Для комплексной характеристики полученных структур применялись методы сканирующей электронной микроскопии (SEM), просвечивающей электронной микроскопии (TEM) и рентгеновской дифрактометрии (XRD), позволяющие исследовать морфологию, внутреннюю структуру и фазовый состав образцов. Кроме того, был проведён предварительный анализ структур ZnO в форме наностержней и нанотрубок, выполнен обзор литературных данных и осуществлён их сравнительный анализ с точки зрения перспектив применения в фотоэлектрохимических системах. Ключевые слова: квантовые точки, оксид цинка, ванадат висмута, гидротермальный синтез, нанотрубки, наностержни.

AI-enabled intelligent sensing systems are increasingly used in Internet of Things (IoT) applications, edge–cloud environments, industrial automation, and cyber-physical systems. Their design, however, requires simultaneous consideration of sensing performance, computational demand, communication requirements, and limited energy resources. Much of the existing literature addresses these aspects separately, although their interactions often determine the performance and autonomy of the complete sensing platform. This review examines energy-aware AI-enabled sensing systems from a system-level perspective, with particular attention to the trade-offs that arise under resource-constrained operating conditions. Representative studies spanning intelligent sensing, embedded intelligence, edge–cloud computing, wireless sensor networks, AIoT, and energy-aware system design are analyzed to identify common architectural principles and design constraints. Four recurring system-level trade-offs are considered, namely measurement accuracy versus energy consumption, latency versus energy savings, local computation versus communication load, and model fidelity versus computational efficiency. The review further develops a multidimensional taxonomy of energy-aware sensing architectures based on energy source, operational mode, processing model, and communication strategy. These elements are integrated into a common framework linking sensing performance, computational requirements, communication demand, and energy allocation. Particular attention is also given to hybrid and multimodal sensing, distributed intelligence, TinyML, hardware-aware model deployment, and adaptive resource allocation. Remaining challenges include scalability, operation under changing sensing conditions, cross-platform evaluation, trustworthy embedded AI, and the transition from laboratory demonstrations to long-term deployment. The resulting system-level perspective provides a basis for designing intelligent sensing platforms in which sensing, computation, communication, and energy resources are considered jointly rather than optimized in isolation.

The process of sulfurization of nickel foam surfaces to obtain Ni ₃ S ₂ layers with high electrochemical capacitance and stability during electrochemical cycling has been extensively studied. However, the role of nickel hydroxide layers, which are expected to form under the electrochemical operating conditions of the Ni ₃ S ₂ /NF electrode, has not been sufficiently investigated. In the present work, it is demonstrated that the hydroxide phase makes a significant contribution to both electrochemical capacitance and cyclic stability. The Ni ₃ S ₂ /NF electrode was fabricated via a single–step hydrothermal method in the presence of thiourea at 160 °C. The initial structure of Ni ₃ S ₂ on the NF surface was subsequently modified through electrochemical cycling in a KOH electrolyte. The increase in electrochemical capacitance of the electrode was accompanied by the formation of multiple nickel hydroxide phases, as identified by X–ray diffraction (XRD) and Raman spectroscopy. The electrode exhibited high performance stability over 20,000 galvanostatic charge–discharge (GCD) cycles at a current density of 20 A g ⁻¹ , retaining 90% of its maximum capacitance. The specific capacitance of the Ni ₂ S ₃ electrode was 758 F g ⁻¹ at a current density of 2.7 A g ⁻¹ . When the current density increased to 90 A g ⁻¹ , the specific capacitance decreased to 233 F g ⁻¹ , corresponding to 30% of the capacitance at 2.7 A g ⁻¹ .