In light of external bias potential separating charge carriers on the photocatalyst surface, piezo materials’ built-in electric field plays a comparable role in enhancing photocatalyst performance. The synergistic effect provided by combining piezo materials assures the future of photocatalysis in practical applications. This paper discusses the principles and mechanisms of piezo-photocatalysis and various materials and structures used for piezo-photocatalytic processes. In piezo-photocatalyst composites, the built-in electric field introduced by the piezo component provides bias potential and extracts photocatalytically generated charge carriers for their subsequent reaction to form reactive oxygen species, which crucially affects the catalytic performance. In the composites, the shape and structure of substrate materials particularly matter. The potential of this technology in other applications, such as energy generation and environmental remediation, are discussed. To shed light on the practical application and future direction of the technique, this review gives opinions on moving the technique forward in terms of material development, process optimization, pilot-scale studies, comprehensive assessment of the technology, and regulatory frameworks to advance practical applications, and by analyzing its principles, applications, and challenges, we hope to inspire further research and development in this field and promote the adoption of piezo-photocatalysis as a viable treatment method for treating emerging pollutants in wastewater.

The increasing presence of emerging contaminants (ECs) has attracted considerable attention due to their potential harm to human health and ecosystems. Graphitic carbon nitride (g-C₃N₄), a semiconductor devoid of metals, stands out due to its distinctive optical properties and strong resistance to chemical degradation, which holds significant promise in the photocatalytic degradation of ECs. However, the inherent limitations of g-C₃N₄, such as its reduced specific surface area and the swift recombination of photogenerated electron-hole pairs, have prompted extensive research on modification strategies to enhance its photocatalytic performance. Current research on g-C₃N₄-based materials is often constrained in scope, with most reviews focusing solely on modification strategies or its application in degrading a single category of emerging contaminants (ECs). In this review, a systematic overview of synthesis methods and advanced modification strategies for g-C₃N₄-based materials is discussed, highlighting their recent advances in the photocatalytic degradation of various ECs using g-C₃N₄-based materials, which underscores their potential for environmental remediation. Moreover, this article critically examines the current challenges and outlines future research directions, with particular emphasis on integrating artificial intelligence and machine learning to accelerate the development of g-C₃N₄-based photocatalysts and optimize degradation processes, thereby promoting their efficient application in the photocatalytic degradation of ECs.

Emerging contaminants, which mainly exist as organic pollutants and pose adverse biological effects, could be removed using photocatalytic degradation, resulting in a low-cost and environmentally friendly solution. Herein, BiVO₄ nanoparticles with different morphologies and photocatalytic performances were synthesized by hydrothermal treatment at different residence times. The XRD and SEM results indicate that the crystal phase of BiVO₄ gradually transformed from a single tetragonal phase to a single monoclinic crystal phase as the hydrothermal time increased, and with the extension of the hydrothermal time, the morphology of BiVO₄ nanoparticles gradually differentiated from a smooth spherical shape to flower-like shapes composed of polyhedrons; the size of the crystals also increased accordingly. Methylene blue (MB), used as a probe of organic pollutants, was degraded under visible light irradiation by all BiVO₄ samples to investigate its photocatalytic activities. The experimental results show that the longer the hydrothermal time, the better the photocatalytic performance. The optimum hydrothermal time was 24 h, at which the sample showed the highest photocatalytic activity for MB degradation. This work shows a convenient strategy for control of the crystal phase of BiVO₄-based photocatalysts based on the understanding of the crystal morphology evolution mechanism, which will benefit the researchers in designing new BiVO₄-based photocatalysts with high efficiency for emerging contaminants’ degradation.

In this study, a systematic review of algae biofuels, including their development and production process, is presented. Compared with traditional techniques, supercritical CO₂ extraction, known for its high efficiency and environmental sustainability, has been widely applied in extracting plant compounds, oils, and fats, demonstrating its role as a promising alternative in biofuel conversion. The principle and features of supercritical CO₂ extraction technology are introduced, and its applications in biofuel production in China are reviewed. The results indicate its broad applicability and substantial scientific value in biofuel production, underscored by its unique extraction mechanisms and operational flexibility and obstacles to its large-scale implementation. Against a future where supercritical CO₂ extraction will play a pivotal role in industrial biofuel production due to the technology’s advancement and policy support in China, this review offers comprehensive insights and references to guide future research into and practices of supercritical CO₂ extraction and biofuel development.

The integration of non-thermal plasma (NTP) with heterogeneous catalysis has emerged as a promising strategy for the efficient abatement of industrial volatile organic compounds (VOCs), overcoming key limitations of conventional thermal and standalone plasma technologies. This review provides a comprehensive overview of the synergistic mechanisms in NTP-catalytic systems, with particular emphasis on the bidirectional interactions between plasma and the catalyst. Specifically, plasma can activate catalysts through surface defect generation and improved metal dispersion, while catalysts, in turn, modulate plasma characteristics via localized electric field enhancement and electron energy redistribution. Furthermore, this synergy spans multiple spatiotemporal scales, linking ultrafast electron dynamics with macroscopic catalytic performance, and atomic-scale active sites with reactor-level behavior. Based on these mechanistic insights, rational catalyst design strategies are systematically discussed, including transition metal oxides, noble metals, perovskites, and metal–organic frameworks. Finally, key challenges related to catalyst deactivation, energy efficiency, and process scalability are highlighted. Future perspectives are proposed, focusing on advanced in situ diagnostics and AI-assisted material discovery to accelerate the practical implementation of NTP-catalytic technologies for sustainable VOC removal.

Air pollution, water contamination, soil degradation, solid waste accumulation, and carbon emissions are increasingly interconnected, posing common challenges to environmental engineering, including diverse monitoring targets, heterogeneous data sources, competing control objectives, and delayed management responses. This review examines the application progress of artificial intelligence in environmental fields through a three-level framework encompassing monitoring, control, and management. It systematically summarizes the roles of machine learning, deep learning, reinforcement learning, transfer learning, digital twins, and the Internet of Things in addressing multi-media environmental challenges. Current studies indicate that artificial intelligence has developed a relatively mature application basis in environmental monitoring as well as showing considerable potential for pollution-control processes. At the management level, the integration of machine learning with LCA, digital twins, and multi-objective optimization is gradually transforming environmental impact assessment from static accounting toward dynamic prediction, feedback, and decision-making. Despite these advances, key bottlenecks remain in data quality, model interpretability, field-scale validation, and the lack of cross-medium collaborative frameworks. Future research should therefore improve the reliability, generalizability, and interpretability of AI models while advancing AI from a task-specific modeling tool to a system-level decision-support technology for integrated environmental governance.