Aquaculture is the world's fastest-growing food sector, currently contributing approximately 0.49% of global anthropogenic greenhouse gas (GHG) emissions, yet its rapid expansion raises significant climate concerns. A holistic understanding of its footprint is imperative for sustainable development. This review provides a comprehensive synthesis and critical analysis of the GHG footprint of global aquaculture. We deconstruct emission sources across the entire value chain and reveal that feed production is the dominant driver in intensive systems, often accounting for over 50% of the total carbon footprint. In contrast, land-use change (LUC), particularly mangrove conversion, can constitute nearly 70% of emissions in specific extensive systems. Furthermore, direct on-farm fluxes of CH4 and N2O represent a significant uncertainty, driven largely by episodic events like ebullition and drainage which are frequently underestimated in current inventories. We identify critical inconsistencies in life cycle assessment boundaries—specifically regarding LUC—that hinder robust cross-system comparisons. By contrasting different production systems, we highlight pathways for substantial emission reductions through feed innovation, energy transition, and improved farm management. Finally, we critically evaluate the potential of extractive aquaculture, such as seaweed and shellfish farming, to act as carbon sinks, emphasizing that their net climate benefit is highly context-dependent. This review establishes a foundational roadmap for the research, policy, and innovation needed to guide a climate-smart blue transformation.

Rosa roxburghii Tratt (Cili) has emerged as a premier functional food resource endemic to Southwest China, distinguished by its extraordinary ascorbate accumulation and complex phytochemical matrix; however, the translational pathway from its unique genetic resources to standardized therapeutic applications remains underexplored. Here, a comprehensive review is presented to bridge the critical gaps among agricultural quality control, phytochemical characterization, molecular nutrition, and industrial processing. Through the synthesis of recent chromosome-scale genomic and integrated multi-omics datasets, the biosynthetic regulation of its hallmark nutrients—particularly the L-galactose pathway for ascorbic acid and gene families controlling flavonoid diversity—is elucidated. Critical emphasis is placed on the molecular mechanisms underlying its pharmacological activities, to elucidate how bioactive fractions modulate the Nrf2/Keap1 antioxidant signaling pathway, regulate systemic glucolipid metabolism, and restore the intestinal barrier via the gut-liver axis. Furthermore, advanced green extraction techniques and fermentation engineering strategies that resolve inherent sensory challenges and enhance nutrient bioavailability are evaluated. This review provides a holistic scientific framework for valorizing R. roxburghii, offering strategic insights for its development into next-generation functional foods targeting metabolic and immune health.

The catalytic co-pyrolysis of agricultural and plastic wastes represents a promising strategy for advancing circular bioeconomy by converting waste into valuable products. This study introduces iron tailing-derived catalysts, denoted as TT1, for the synergistic co-processing of wheat straw and polyethylene, enabling the simultaneous production of bio-oil and hydrogen-rich syngas. At the optimal 50

Graphitic carbon nitride (g-C3N4) is a material with a graphite-like layered structure. Due to its non-toxic and harmless nature, stable performance, and visible light response capability, g-C3N4 holds significant application prospects in the field of photocatalysis. In this study, the Sb2(S,Se)3 was coupled with g-C3N4 to construct a heterojunction structure, and the photocatalytic degradation performance of the g-C3N4/Sb2(S,Se)3 heterojunction materials on organic dye pollutants was investigated. Firstly, the g-C3N4/Sb2S3 composite samples were prepared using a hydrothermal reaction. Subsequently, the g-C3N4/Sb2(S,Se)3 heterojunction structures were constructed through selenization, and then they were used in the efficient photocatalytic degradation process of organic pollutants methylene blue (MB) and rhodamine B (RhB). The results indicate that due to the formation of the heterojunction structure, the light absorption performance of the g-C3N4/Sb2S3 and g-C3N4/Sb2(S,Se)3 composite materials is significantly strengthened, and the separation ability of the photo-generated carriers is remarkably improved. Therefore, compared with pure g-C3N4, the photocatalytic degradation activity of the composite system on MB and RhB is outstandingly enhanced. Furthermore, after partially replacing S with Se through selenization, the resulting heterojunction structure exhibits stronger photocatalytic degradation performance. For example, the 4-GSe sample obtained at a selenization temperature of 140 °C can achieve a degradation rate of 96

The co-pyrolysis of biomass and plastic waste presents a promising route for sustainable hydrogen-rich syngas and liquid fuel production, yet the development of low-cost and stable catalysts remains a challenge. In this study, hematite-rich mining tailings CT1 are employed as a catalyst in the co-pyrolysis of wheat straw (WS) and polyethylene (PE) to investigate synergistic effects on product distribution and reaction mechanisms. Results show that CT1 significantly enhances H-2 purity, up to 96.76 vol% at 75% PE, and completely suppresses H2S and heavy hydrocarbons (C6+). The catalyst also directs liquid product selectivity toward middle-chain hydrocarbons, achieving over 60% selectivity at 50% PE, suitable for biodiesel and aviation fuel applications. Catalyst characterization reveals that Fe species dynamically evolve under different WS/PE ratios, influencing dehydrogenation, cracking, and deoxygenation pathways. A synergistic mechanism is proposed, wherein PE acts as a hydrogen donor, WS provides the carbon skeleton, and CT1 facilitates selective cracking and desulfurization. This work establishes a novel "waste-to-resource" strategy using iron tailings as a synergistic catalyst, enabling high-value conversion of solid wastes into clean energy and chemicals.

Carbon capture, utilization, and storage (CCUS) is a suite of technologies designed to separate CO2 from industrial sources or the atmosphere, convert it into value-added products, or permanently sequester it in geological formations. Although essential for achieving global carbon neutrality, the gigatonne-scale deployment of CCUS is currently constrained by high energy penalties in capture, thermodynamic limitations in utilization, and rigorous monitoring requirements to ensure storage security. Artificial intelligence (AI), which encompasses machine learning (ML), deep learning (DL), and generative algorithms, offers a data-driven approach to addressing multiscale physicochemical challenges by identifying complex correlations in high-dimensional datasets. This review synthesizes AI applications across the CCUS value chain. In the capture domain, we discuss how generative models and high-throughput screening accelerate the discovery of high-performance sorbents and solvents, while surrogate models optimize process dynamics to improve energy efficiency. For utilization, we highlight AI’s role in navigating the vast chemical space of catalysts to overcome thermodynamic scaling relations and optimize biochemical pathways. In geological storage, we analyze how DL architectures, from computer vision for seismic interpretation to Fourier neural operators for rapid plume forecasting, automate site characterization and enhance real-time leakage detection. Furthermore, this review elucidates AI-facilitated system-level integration, enabling techno-economic optimization of capture-transport-storage networks under policy and market uncertainties. Finally, we address critical challenges regarding data standardization, model interpretability, and generalizability, and project a future in which large language models and digital twins drive autonomous, self-optimizing CCUS operations.

CuS is a typical p-type narrow band gap semiconductor that can absorb solar energy in the visible and even nearinfrared regions, exhibiting significant application potential in the field of photocatalysis. This study focuses on the regulatory effect of Mn doping on the photocatalytic performance of CuS nanomaterials. The CuS-Mn photocatalytic materials with different Mn doping contents were prepared via a facile one-step precipitation method, and the effects of Mn doping on the morphology, crystal structure, optical properties, and photocatalytic activity of CuS nanocrystals were systematically investigated. The results demonstrate that appropriate Mn doping can endow CuS photocatalysts with a more uniform particle distribution and richer pore structure, effectively enhancing the separation and transport efficiency of photo-generated electron-hole pairs, thereby significantly improving their photocatalytic degradation performance toward organic pollutants. For instance, the CuS-Mn-3 product exhibits excellent decomposition activity toward both MB and RhB, with removal efficiencies of respectively 97.1% and 94.2% within 30 min, and first-order kinetic rate constants of 0.1143 and 0.0931 min(-1), respectively. Meanwhile, the photocatalyst also shows outstanding degradation performance toward the MB + RhB mixed dyes, reflecting good adaptability to the complex dye-contaminated environments. Moreover, the cyclic degradation tests reveal that the CuS-Mn-3 sample can still maintain good photocatalytic performance after three cycles of decomposition process, with no obvious change in its crystal structure, indicating excellent stability and reusability. Furthermore, the possible reaction mechanism of the photocatalytic degradation on organic dyes by the Mn-doped CuS photocatalyst was explored.

Carbon source deficiency is a critical bottleneck limiting nitrogen removal efficiency and exacerbating nitrous oxide (N₂O) emissions in constructed wetlands (CWs) treating low carbon-to-nitrogen (C/N) tailwater. Biodegradable plastics and wetland plant residues are the autochthonous carbon sources in water; however, the denitrification enhancement effect of their composite remains unclear. Herein, the PHBV/reed composite was prepared, its performance in CWs was evaluated. Results showed that, the presence of PHBV/reed significantly enhanced the NO₃⁻ removal efficiency to 89.98%, while simultaneously reducing N₂O, CH₄, and CO₂ emissions by 50–64%. Mechanistically, the composite released DOM rich in readily biodegradable components, which selectively enriched key denitrifying genera (Dechloromonas and Hydrogenophaga). This shift enhanced the electron transport system activity (ETSA) by 78.87%. Metagenomic analysis revealed that the PHBV/reed composite activated glycolysis and the tricarboxylic acid cycle, promoting NADH/ATP synthesis, with the generated electrons preferentially allocated to nitrate reduction. Furthermore, the composite mitigated N₂O emissions through the way of down-regulating norB/norC genes (EC 1.7.2.5) to suppress N₂O production, and up-regulating nosZ gene (EC 1.7.2.4) to enhance N₂O consumption. This study unveils a novel paradigm of in-situ denitrification driven by autochthonous carbon sources in CWs, offering a green and sustainable technological solution for low C/N tailwater treatment.

The pervasive occurrence of steroidal sex hormones in aquatic ecosystems poses a substantial challenge to aquatic ecosystem health and may also have implications for public health. Some of these potent endocrine-disrupting chemicals act at ultra-trace concentrations, challenging traditional single-substance toxicological paradigms, and are often incompletely removed by conventional wastewater treatment. This review critically synthesizes the biogeochemical dynamics of aquatic sex hormones, tracing their trajectories from anthropogenic sources through environmental transport, transformation, and persistence. It highlights the need to expand monitoring beyond targeted quantification toward comprehensive steroidal profiling, with particular attention to the potentially overlooked contributions of conjugated forms and uncharacterized transformation products. Furthermore, this review evaluates biological effects through the lens of adverse outcome pathways and the One Health framework, integrating well-established ecological effects in aquatic organisms with the more limited and emerging evidence relevant to human health under real-world mixture exposures. To address these threats, the efficacy and sustainability of advanced and emerging removal technologies are critically assessed, including catalytic oxidation, enzymatic biotransformation, hybrid systems, and nature-based solutions. Ultimately, safeguarding aquatic ecosystems requires an integrated, multi-barrier strategy that combines proactive source control, mixture-aware risk assessment, fit-for-purpose wastewater treatment, and sustainable mitigation aligned with circular economy principles.

Crayfish shell waste, generated in large quantities from the processing of Procambarus clarkii, represents an underutilized biogenic resource rich in chitin, chitosan precursors, calcium carbonate, proteins, pigments, and naturally doped heteroatoms. Its conversion into functional materials provides a promising route for waste valorization, although the environmental and economic advantages of different strategies still require critical evaluation. This review summarizes recent progress in the extraction, conversion, modification, and application of crayfish shell-derived materials, with emphasis on the links between processing routes, material structures, interfacial mechanisms, and practical performance. Conventional acid–alkali extraction, deep eutectic solvent-assisted methods, enzymatic hydrolysis, microbial fermentation, pyrolysis, and activation strategies are compared in terms of efficiency, product quality, scalability, and environmental trade-offs. Functionalization approaches, including heteroatom self-doping, metal or metal oxide coupling, magnetic modification, and hierarchical porous structure construction, are discussed in relation to specific application scenarios. Particular attention is given to micro-interfacial mechanisms governing pollutant removal and catalytic reactions, such as precipitation, ion exchange, surface complexation, electrostatic interaction, hydrogen bonding, π–π interaction, redox transformation, electron transfer, and reactive oxygen species generation. Current applications span water and soil remediation, adsorption, catalysis, energy storage, food packaging, sensing, bioactive compounds, and biomedical materials. Finally, key challenges are identified, including feedstock heterogeneity, seasonal supply, storage-related degradation, contaminant risks, limited life-cycle and techno-economic assessments, insufficient comparison with other crustacean wastes, and regulatory barriers. This review highlights knowledge gaps and future directions for advancing crayfish shell waste from laboratory-scale valorization toward and scalable functional materials.

Photocatalysis is a technology that uses solar energy to drive chemical reactions, and it promotes oxidation-reduction reactions under the condition of light irradiation by photocatalysts, achieving the purification of pollutants, synthesis and transformation of substances, and so on. The essence of photocatalytic technology centers on photocatalysts. Nevertheless, the traditional trial-and-error development process of photocatalytic materials cannot meet the development needs of modern society due to unfavorable factors such as high cost, low efficiency, and lengthy research and development periods. In recent times, the fast advancement of machine learning (ML) technology has opened up new avenues for the design of photocatalysts. With the continuous deep integration of big data and artificial intelligence (AI), machine learning, which is data-driven, has made tremendous progress in the design, screening, and performance prediction of new materials, greatly promoting the research and application of novel materials. This review first introduces the basic process of ML and its commonly used algorithms in materials science, and then focuses on the latest research progress in the use of ML in photocatalytic water splitting for hydrogen production, photocatalytic pollutant degradation, and photocatalytic harmful gas conversion in recent years. Furthermore, it provides a prospect on the existing problems and development prospects of ML in the screening and design of photocatalysts, the prediction of photocatalytic performance, the parameter optimization of photocatalytic processes, and so on.

Artificial Intelligence (AI) is progressively reshaping the landscape of minerals engineering, driving advancements across exploration, mining, and processing. This review systematically examines the current applications of AI in these domains, highlighting its role in optimizing resource estimation, enhancing safety, and improving operational efficiency. Through a bibliometric analysis, trends, key contributors, and geographical distributions in AI-related research within minerals engineering are explored, revealing a significant rise in AI-focused studies and a global shift towards integrating these technologies. In exploration, AI techniques such as machine learning (ML) and data analytics are utilized for mineral prospectivity mapping (MPM) and anomaly detection, facilitating more precise resource identification. In mining operations, AI aids in optimizing extraction processes, predicting equipment failures, and enabling autonomous systems for increased safety. Within mineral processing, AI contributes to real-time monitoring, process optimization, and product quality improvement through advanced modeling and control systems. Despite these advancements, challenges persist, including data quality, integration complexities, and the need for interdisciplinary expertise. This review underscores AI's transformative impact on the sector and outlines the need for continued research and collaboration to overcome existing barriers and unlock AI's full potential in minerals engineering.

Graphitic carbon nitride (g-C3N4) is a typical non-metallic n-type semiconductor. Due to its good chemical stability, low cost, no secondary pollution, and ability to respond to visible light, the g-C3N4 material has important application prospects in the field of photocatalysis. This study constructed a complex system of photocatalysis and advanced oxidation processes by combining g-C3N4 with hydrogen peroxide (H2O2) for efficient photocatalytic degradation of organic pollutants. Firstly, using urea as raw material, the g-C3N4 photocatalytic products were prepared via thermal polymerization reaction, and the effect of calcination temperature on the properties of g-C3N4 materials was studied. Subsequently, the photocatalytic degradation performances of the g-C3N4 photocatalytic system on organic pollutants with and without the H2O2 addition were investigated by using methylene blue (MB) and rhodamine B (RhB) as the target pollutants. The research results indicate that the g-C3N4 product obtained at 580 ℃ exhibited excellent degradation ability towards organic dyes. With the addition of H2O2, the degradation rates of MB and RhB can reach about 94.2

The alkali fusion-magnesium modified oil-based drilling cuttings ash (AM-Mg-OBDCA) was synthesized and characterized for its capacity to remove Cd(II) from aqueous solutions. Adsorption kinetics conformed to the pseudo-second-order model, underscoring a chemisorption-dominated mechanism, while Langmuir isotherm analysis determined a maximum adsorption capacity of 440.2 mg/g. Post-adsorption structural and chemical transformations, confirmed by SEM, FT-IR, XRD, and XPS analyses, revealed the contributions of electrostatic attraction, ligand exchange, surface precipitation, and cation exchange. Photocatalytic tests demonstrated that the CdS-A/M-g-C3N4 (2:1) composite exhibited superior degradation of oxytetracycline hydrochloride (OTC) under visible light, suggesting potential applications in environmental remediation. However, reusability tests indicated a marked decline in adsorption capacity after initial use, pointing to the need for further material optimization. Overall, AM-Mg-OBDCA shows promise as an effective adsorbent for Cd(II) removal and a precursor for photocatalytic materials, offering a sustainable approach to wastewater treatment and resource recovery.

The integration of artificial intelligence (AI) in the food industry has driven significant advancements in efficiency, safety, and sustainability. This review assesses the current state and future prospects of AI applications in key areas such as food traceability, safety, quality control, supply chain optimization, and intelligent packaging solutions. AI technologies, including machine learning (ML) algorithms and computer vision systems, are widely used to optimize supply chains, predict demand, reduce waste, and enhance food safety and quality monitoring. Advanced ML models are employed to analyze production data, monitor quality parameters, and predict shelf life, ensuring compliance with stringent regulatory standards. Despite these advancements, challenges related to data quality, system integration, computational demands, and ethical considerations remain, necessitating further research and collaboration among stakeholders. This review aims to elucidate these challenges while highlighting the transformative potential of AI in the food industry. By synthesizing recent developments and trends, this paper provides valuable insights for researchers, industry professionals, and policymakers, underscoring the pivotal role of AI in driving innovation and sustainability in the food sector.

Copper sulfide (CuS) is a p-type semiconductor material with narrow band gap and easy to be excited by visible light, which makes it has broad application prospect in the field of organic pollutant treatment. This work provides some significant insights into the photo-Fenton catalytic performance of the CuS materials in the degradation of organic dye MB. Via changing the copper source, the catalytic and photocatalytic performances of the CuS products prepared by using different copper sources such as CuSO4, Cu(NO3)2, CuCl2, Cu(C2H3O2)2 were studied. The results indicated that due to the difference of copper sources, the catalytic performance of the obtained CuS samples varied greatly. For example, the catalytic properties of the CuS specimens prepared via CuCl2 and CuSO4 were significantly better than those of the products prepared using Cu(C2H3O2)2 and Cu(NO3)2. Meanwhile, the degradation performance of MB by the same CuS specimen is better in a light irradiation condition than in a dark environment. Specifically, the catalytic performance of the CuS sample prepared via CuCl2 as copper source was the best, and when H2O2 was added to form a photo-Fenton catalytic system, it has excellent degradation ability for MB in both dark and light irradiation environments.

Heavy metal pollution in aquatic sediments is a persistent global challenge. While phytoremediation presents a green solution, its efficiency is often crippled by the very toxicity of the contaminants to the plants. Here, we demonstrate a synergistic strategy that overcomes this paradox by augmenting the submerged plant Vallisneria natans with magnesium hydroxide-modified diatomite (Mg(OH)2-DE). In a year-long microcosm study, this approach dramatically stimulated plant biomass by over 229% under high cadmium (Cd) stress, underpinning a system that achieved up to 92.22% Cd removal from sediments. Mechanistic investigations reveal a dual-action synergy: the amendment simultaneously enhances the bioavailability of essential nutrients (Fe, Mn, Zn, and P) to fuel vigorous plant growth and activates a potent antioxidant defense network, evidenced by a 51.25% reduction in the stress marker malondialdehyde. This enhanced physiological resilience directly translated to superior phytoextraction performance by promoting the upregulation of NRAMP metal transporters. Our work, where engineered minerals unlock a plant's intrinsic detoxification capabilities, establishes a robust and sustainable paradigm for in situ remediation of heavy-metal-contaminated aquatic environments.