The binary metal organic framework (MOF) is composed of two heterometallic ions bonded to an organic ligand. Compared with monometallic MOFs, bimetallic MOFs have greatly improved in terms of structure, porosity, active site, adsorption, selectivity, and stability, which has attracted wide attention. At present, many effective strategies have been designed for the synthesis of bimetallic MOF-based nanomaterials with specific morphology, structure, and function. The results show that bimetallic MOF-based nanocomposites could achieve multiple synergistic effects, which will greatly improve their research in the fields of adsorption, catalysis, energy storage, sensing, and so on. In this review, the main preparation methods of bimetallic MOFs-based materials are summarized, with emphasis on their applications in adsorption, catalysis, and detection of target pollutants in water environments, and perspectives on the future development of bimetallic MOFs-based nanomaterials in the field of water are presented.

This study explores nitrogen removal performance, bioelectricity generation, and the response of microbial community in two novel tidal flow constructed wetland-microbial fuel cells (TFCW-MFCs) when treating synthetic wastewater under two different chemical oxygen demand/total nitrogen (COD/TN, or simplified as C/N) ratios (10:1 and 5:1). The results showed that they achieved high and stable COD, NH₄⁺-N, and TN removal efficiencies. Besides, TN removal rate of TFCW-MFC was increased by 5–10% compared with that of traditional CW-MFC. Molecular biological analysis revealed that during the stabilization period, a low C/N ratio remarkably promoted diversities of ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) in the cathode layer, whereas a high one enhanced the richness of nitrite-oxidizing bacteria (NOB) in each medium; the dominant genera in AOA, AOB, and NOB were Candidatus Nitrosotenuis, Nitrosomonas, and Nitrobacter. Moreover, a high C/N ratio facilitated the growth of Nitrosomonas, while it inhibited the growth of Candidatus Nitrosotenuis. The distribution of microbial community structures in NOB was separated by space rather than time or C/N ratio, except for Nitrobacter. This is caused by the differences of pH, dissolved oxygen (DO), and nitrogen concentration. The response of microbial community characteristics to nitrogen transformations and bioelectricity generation demonstrated that TN concentration is significantly negatively correlated with AOA-shannon, AOA-chao, 16S rRNA V4−V5-shannon, and 16S rRNA V4−V5-chao, particularly due to the crucial functions of Nitrosopumilus, Planctomyces, and Aquicella. Additionally, voltage output was primarily influenced by microorganisms in the genera of Nitrosopumilus, Nitrosospira, Altererythrobacter, Gemmata, and Aquicella. This study not only presents an applicable tool to treat high nitrogen-containing wastewater, but also provides a theoretical basis for the use of TFCW-MFC and the regulation of microbial community in nitrogen removal and electricity production.

Increasing numbers of emerging contaminants (ECs) detected in water environments require a detailed understanding of these chemicals’ fate, distribution, transport, and risk in aquatic ecosystems. Modeling is a useful approach for determining ECs’ characteristics and their behaviors in aquatic environments. This article proposes a systematic taxonomy of EC models and addresses gaps in the comprehensive analysis of EC models and their applications. The reviewed models include conventional water quality models, multimedia fugacity models, and machine learning (ML) models. Conventional water quality models have higher prediction accuracy and spatial resolution; nevertheless, they are limited in functionality and can only be used to predict contaminant concentrations in aquatic environments. Fugacity models are excellent at depicting how contaminants travel between different environmental media, but they cannot be used directly to analyze contaminant variations in different parts of the same environmental media because the fugacity model assumes that contaminant concentrations are constant within the same environmental compartment. Compared to other models, ML models can be applied to more scenarios, such as contaminant identification and risk assessments, rather than being confined to the prediction of contaminant concentrations. In recent years, with the rapid development of artificial intelligence, ML models have surpassed fugacity models and conventional water quality models, becoming one of the newest hotspots in the study of ECs. The primary challenge faced by ML models is that the model outcomes are difficult to interpret and understand, and this influences the practical value of an ML model to some extent.

Electrocatalytic nitrate reduction reaction (NITRR) is highly desirable for remediating nitrate (NO3-) pollution and producing ammonia (NH3) under mild conditions. To date, great efforts have been made to fabricate selective, efficient, and stable electrocatalysts for NITRR. Among the numerous strategies, single-atom catalysts (SACs) have received extensive interest and investigations due to their cost-effective and maximum atomic utilization. However, the further development of SACs-based NITRR remains hindered by a poor understanding of their in-depth mechanisms. Consequently, this review summarizes the recent advances of SACs for the NITRR, including Cu-SACs, Fe-SACs, Zn-SACs, Co-SACs, and single-atom alloys. In addition, the characterization techniques for SACs and reaction pathways of NITRR are presented to give a robust understanding of SACs-based NITRR. Finally, we analyze the current challenges in fabricating SACs for NITRR, while the key factors for further improving NITRR performances are also examined.