Photoelectrochemical conversion offers a conceptually attractive route for storing intermittent solar energy in chemical bonds, yet the practical performance of semiconductor photoelectrodes remains constrained by a well-recognized triad of losses: incomplete light harvesting, inefficient charge separation, and sluggish interfacial reaction kinetics. This invited Review examines electrochemical strategies for improving semiconductor photoelectrochemical performance, with emphasis on three representative systems: metal-assisted chemical etching of n-type silicon, cyclic voltammetry-based electrodeposition of ZnO/TiO 2 heterojunction nanorod photoanodes, and impedance spectroscopy analysis of NiO/AlGaN/n-GaN tandem photoanodes. Rather than treating these examples as isolated case studies, this Review proposes a structure-interface-kinetics framework. In this view, etching controls the optical and geometric boundary conditions of the semiconductor, electrodeposition determines the electronic quality and chemical selectivity of the surface, and impedance spectroscopy identifies the kinetic bottleneck to guide subsequent materials design. The central conclusion is that higher photocurrent density alone is not a sufficient design objective. A credible photoelectrode architecture must simultaneously preserve crystallinity, generate a beneficial interfacial field, expose kinetically competent catalytic sites, and resist corrosion under operating potentials. The Review therefore argues for an analysis-driven design philosophy in which morphology, band alignment, defect chemistry, and charge-transfer resistance are optimized as coupled variables rather than as independent descriptors.

Show more

Given the rapid advancement of industrialization and urbanization, multiple forms of water pollution have grown increasingly severe. Traditional water treatment technologies face challenges such as low treatment efficiency and secondary pollution. Semiconductor nanomaterial-based photocatalysis has emerged as a promising approach for green water pollution control. This paper adopts a literature review method to systematically explore the photocatalytic water purification mechanisms of semiconductor nanomaterials, summarize four material optimization strategies and review the research progress of classical photocatalytic materials such as TiO₂, ZnO, g-C₃N₄, and Nb-doped SnO₂. Studies indicate that Nb doping introduces oxygen vacancies and synergistically modifies the band structure, significantly improving the poor visible-light response and photocorrosion susceptibility of SnO₂, while enhancing the removal efficiency for organic pollutants, heavy metals, and pathogenic bacteria. At present, this technology is limited by material stability, actual water conditions, and engineering-scale production, making large-scale implementation difficult. In the future, theoretical simulations for material optimization, immobilized catalytic devices, and supporting reactors may facilitate the transition of photocatalytic water treatment technology from laboratory research to industrial applications.

Show more

In the context of high-penetration distributed photovoltaic generation in low-voltage distribution networks, photovoltaic inverters have evolved from single power conversion devices into key equipment for power quality regulation, grid support, and intelligent operation and maintenance. This paper reviews the literature, compares typical engineering applications, focusing on three common issues in recent photovoltaic inverter research: leakage-current suppression in transformerless structures, voltage support and harmonic adaptability during grid-connected operation, and data-driven modeling and parameter optimization under complex operating conditions. The reviewed studies show that common-ground, multilevel, and quasi-Z-source topologies can improve system efficiency and suppress leakage current; Volt-VAR control, energy storage coordination, and cascaded control can improve voltage quality; and black-box modeling, symbolic regression, and swarm intelligence optimization can enhance state prediction and parameter tuning. Future research should further establish unified experimental benchmarks, improve measured data coverage, and facilitate quantitative comparisons across different technical routes.

Show more

The construction industry faces persistent productivity, safety, and coordination problems that cannot be solved by equipment upgrades alone. Under the broader agenda of intelligent construction, construction robots are becoming cyber-physical execution units that connect Building Information Modeling (BIM), sensing, artificial intelligence, and on-site operations. This paper analyzes the technological progress and deployment challenges of construction robots from two interrelated perspectives: autonomous navigation and human-robot collaboration. It argues that multi-sensor perception, BIM-assisted localization, Simultaneous Localization and Mapping (SLAM), improved path planning, and digital-twin-enabled safety monitoring have improved the feasibility of robotic systems in complex construction environments. However, wide deployment is still constrained by unstructured site conditions, model-to-reality discrepancies, fragmented data interfaces, low collaboration maturity, high implementation costs, and uncertain business incentives. The paper proposes four implementation strategies: open communication and data-interface standards, construction-specific safety guidelines for human-robot collaboration, financial and rental mechanisms for lowering the adoption threshold, and joint pilot projects involving industry, universities, research institutions, and end users. The study concludes that construction robots should not be framed as simple substitutes for human labor. Their more realistic role is to augment workers in hazardous, repetitive, precision-demanding, and data-intensive tasks while supporting a gradual transition from experience-based construction management to data-driven intelligent construction.

Show more