Hollow Stair-Stepping Spherical High-Entropy Prussian Blue Analogue for High-Rate Sodium Ion Batteries.

Prussian blue analogues (PBAs) are considered to be one of the most suitable sodium storage materials, especially with the introduction of the high-entropy (HE) concept into their structure to further improve their various abilities. However, severe agglomeration of the HEPBA particles still limits the fast charging capabilities. Here, an HEPBA (Nax(FeMnCoNiCu)[Fe(CN)6]y□1-y·nH2O) with a hollow stair-stepping spherical structure has been prepared through the chemical etching process of the traditional cubic structure of HEPBA. Electrochemical characterization (sodium ion battery), kinetic analysis, and COMSOL Multiphysics simulations reveal that the nature of the high-entropy and the hollow stair-stepping spherical structure can greatly improve the diffusion behavior of Na+ ions. Moreover, the hollow structure effectively mitigates the volume change of HEPBA during SIBs operation, ultimately extending the lifespan. Consequently, the as-prepared HEPBA cathode exhibits excellent rate performance (126.5 and 76.4 mAh g-1 at 0.1 and 4.0 A g-1, respectively) and stable long-term capability (maintaining its 75.6% capacity after 1000 cycles) due to its unique structure. Furthermore, the waste of the etching process can easily be recycled to prepare more HEPBA product. This processing method holds great promise for designing nanostructures of advanced high-entropy Prussian blue analogues for sodium ion batteries.

Micro-heterogeneity of transmission shapes submicroscopic malaria carriage in coastal Tanzania.

BACKGROUND: Asymptomatic carriage of malaria parasites persists even as malaria transmission declines. Low density infections are often submicroscopic, not detected by rapid diagnostic tests (RDTs) or microscopy, but detectable by PCR. METHODS: To characterize submicroscopic Plasmodium falciparum carriage in an area of declining malaria transmission, asymptomatic persons >5 years of age in rural Bagamoyo District, Tanzania, were screened using RDT, microscopy, and PCR. We investigated the size of the submicroscopic reservoir of infection across villages, determined factors associated with submicroscopic carriage, and assessed the natural history of submicroscopic malaria over four weeks. RESULTS: Among 6,076 participants, P. falciparum prevalence by RDT, microscopy, and PCR was 9%, 9%, and 28%, respectively, with roughly two-thirds of PCR-positive individuals harboring submicroscopic infection. Adult status, female gender, dry season months, screened windows, and bednet use were associated with submicroscopic carriage. Among 15 villages encompassing 80% of participants, the proportion of submicroscopic carriers increased with decreasing village-level malaria prevalence. Over four weeks, 23% (61/266) of submicroscopic carriers became RDT-positive, with half exhibiting symptoms, while half (133/266) were no longer parasitemic at the end of four weeks. Progression to RDT-positive patent malaria occurred more frequently in villages with higher malaria prevalence. CONCLUSIONS: Micro-heterogeneity in transmission observed at the village level appears to impact both the size of the submicroscopic reservoir and the likelihood of submicroscopic carriers developing patent malaria in coastal Tanzania.

Machine learning models reveal the critical role of nighttime systolic blood pressure in predicting functional outcome for acute ischemic stroke after endovascular thrombectomy.

Background: Blood pressure (BP) is a key factor for the clinical outcomes of acute ischemic stroke (AIS) receiving endovascular thrombectomy (EVT). However, the effect of the circadian pattern of BP on functional outcome is unclear. Methods: This multicenter, retrospective, observational study was conducted from 2016 to 2023 at three hospitals in China (ChiCTR2300077202). A total of 407 patients who underwent endovascular thrombectomy (EVT) and continuous 24-h BP monitoring were included. Two hundred forty-one cases from Beijing Hospital were allocated to the development group, while 166 cases from Peking University Shenzhen Hospital and Hainan General Hospital were used for external validation. Postoperative systolic BP (SBP) included daytime SBP, nighttime SBP, and 24-h average SBP. Least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), Boruta were used to screen for potential features associated with functional dependence defined as 3-month modified Rankin scale (mRS) score ≥ 3. Nine algorithms were applied for model construction and evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results: Three hundred twenty-eight of 407 (80.6%) patients achieved successful recanalization and 182 patients (44.7%) were functional independent. NIHSS at onset, modified cerebral infarction thrombolysis grade, atrial fibrillation, coronary atherosclerotic heart disease, hypertension were identified as prognostic factors by the intersection of three algorithms to construct the baseline model. Compared to daytime SBP and 24-h SBP models, the AUC of baseline + nighttime SBP showed the highest AUC in all algorithms. The XGboost model performed the best among all the algorithms. ROC results showed an AUC of 0.841 in the development set and an AUC of 0.752 in the validation set for the baseline plus nighttime SBP model, with a brier score of 0.198. Conclusion: This study firstly explored the association between circadian BP patterns with functional outcome for AIS. Nighttime SBP may provide more clinical information regarding the prognosis of patients with AIS after EVT.

End-to-End Network Intrusion Detection Based on Contrastive Learning.

The network intrusion detection system (NIDS) plays a crucial role as a security measure in addressing the increasing number of network threats. The majority of current research relies on feature-ready datasets that heavily depend on feature engineering. Conversely, the increasing complexity of network traffic and the ongoing evolution of attack techniques lead to a diminishing distinction between benign and malicious network behaviors. In this paper, we propose a novel end-to-end intrusion detection framework based on a contrastive learning approach. We design a hierarchical Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model to facilitate the automated extraction of spatiotemporal features from raw traffic data. The integration of contrastive learning amplifies the distinction between benign and malicious network traffic in the representation space. The proposed method exhibits enhanced detection capabilities for unknown attacks in comparison to the approaches trained using the cross-entropy loss function. Experiments are carried out on the public datasets CIC-IDS2017 and CSE-CIC-IDS2018, demonstrating that our method can attain a detection accuracy of 99.9% for known attacks, thus achieving state-of-the-art performance. For unknown attacks, a weighted recall rate of 95% can be achieved.

Research on a Critical Link Discovery Method for Network Security Situational Awareness.

Network security situational awareness (NSSA) aims to capture, understand, and display security elements in large-scale network environments in order to predict security trends in the relevant network environment. With the internet's increasingly large scale, increasingly complex structure, and gradual diversification of components, the traditional single-layer network topology model can no longer meet the needs of network security analysis. Therefore, we conduct research based on a multi-layer network model for network security situational awareness, which is characterized by the three-layer network structure of a physical device network, a business application network, and a user role network. Its network characteristics require new assessment methods, so we propose a multi-layer network link importance assessment metric: the multi-layer-dependent link entropy (MDLE). On the one hand, the MDLE comprehensively evaluates the connectivity importance of links by fitting the link-local betweenness centrality and mapping entropy. On the other hand, it relies on the link-dependent mechanism to better aggregate the link importance contributions in each network layer. The experimental results show that the MDLE has better ordering monotonicity during critical link discovery and a higher destruction efficacy in destruction simulations compared to classical link importance metrics, thus better adapting to the critical link discovery requirements of a multi-layer network topology.

Inducing Directional Charge Delocalization in 3D-Printable Micro-Supercapacitors Based on Strongly Coupled Black Phosphorus and ReS2 Nanocomposites.

The growing interest in so-called interface coupling strategies arises from their potential to enhance the performance of active electrode materials. Nevertheless, designing a robust coupled interface in nanocomposites for stable electrochemical processes remains a challenge. In this study, an epitaxial growth strategy is proposed by synthesizing sulfide rhenium (ReS2 ) on exfoliated black phosphorus (E-BP) nanosheets, creating an abundance of robust interfacial linkages. Through spectroscopic analysis using X-ray photoelectron spectroscopy and X-ray absorption spectroscopy, the authors investigate the interfacial environment. The well-developed coupled interface and structural stability contribute to the impressive performance of the 3D-printed E-BP@ReS2 -based micro-supercapacitor, achieving a specific capacitance of 47.3 mF cm-2 at 0.1 mA cm-2 and demonstrating excellent long-term cyclability (89.2% over 2000 cycles). Furthermore, density functional theory calculations unveil the positive impact of the strongly coupled interface in the E-BP@ReS2 nanocomposite on the adsorption of H+ ions, showcasing a significantly reduced adsorption energy of -2.17 eV. The strong coupling effect facilitates directional charge delocalization at the interface, enhancing the electrochemical performance of electrodes and resulting in the successful construction of advanced micro-supercapacitors.

FastCAT: A framework for fast routing table calculation incorporating multiple protocols.

Currently, most network outages occur because of manual configuration errors. Therefore, it is essential to verify the correctness of network configurations before deployment. Computing the network control plane is a key technology for network configuration verification. We can verify the correctness of network configurations for fault tolerance by generating routing tables, as well as connectivity. However, existing routing table calculation tools have disadvantages such as lack of user-friendliness, limited expressiveness, and slower speed of routing table generation. In this paper, we present FastCAT, a framework for computing routing tables incorporating multiple protocols. FastCAT can simulate the interaction of multiple routing protocols and quickly generate routing tables based on configuration files and topology information. The key to FastCAT's performance is that FastCAT focuses only on the final stable state of the OSPF and IS-IS protocols, disregarding the transient states during protocol convergence. For RIPv2 and BGP, FastCAT computes the current protocol routing tables based on the protocol's previous state, retaining only the most recent protocol routing tables in the latest state. Experimental evaluations have shown that FastCAT generates routing tables more quickly and accurately than the state-of-the-art routing simulation tool, in a general network of around 200 routers.

Micro-heterogeneity of transmission shapes the submicroscopic malaria reservoir in coastal Tanzania.

Background: Asymptomatic malaria may be patent (visible by microscopy) and detectable by rapid malaria diagnostic tests (RDTs), or it may be submicroscopic and only detectable by polymerase chain reaction (PCR). Methods: To characterize the submicroscopic reservoir in an area of declining malaria transmission, asymptomatic persons >5 years of age in Bagamoyo District, Tanzania, were screened using RDT, microscopy, and PCR. We investigated the size of the submicroscopic reservoir across villages, determined factors associated with submicroscopic parasitemia, and assessed the natural history of submicroscopic malaria over four weeks. Results: Among 6,076 participants, Plasmodium falciparum prevalence by RDT, microscopy, and PCR was 9%, 9%, and 28%, respectively, with roughly two-thirds of PCR-positive individuals harboring submicroscopic infection. Adult status, female gender, dry season months, screened windows, and bednet use were associated with submicroscopic carriage. Among 15 villages encompassing 80% of participants, the proportion of submicroscopic carriers increased with decreasing village-level malaria prevalence. Over four weeks, 23% (61/266) of submicroscopic carriers became RDT-positive and were treated, with half exhibiting symptoms. This occurred more frequently in villages with higher malaria prevalence. Conclusions: Micro-heterogeneity in transmission impacts the size of the submicroscopic reservoir and the likelihood of submicroscopic carriers developing patent malaria in coastal Tanzania.

Self-Assembled Peptide-Based Nanodrugs: Molecular Design, Synthesis, Functionalization, and Targeted Tumor Bioimaging and Biotherapy.

Functional nanomaterials as nanodrugs based on the self-assembly of inorganics, polymers, and biomolecules have showed wide applications in biomedicine and tissue engineering. Ascribing to the unique biological, chemical, and physical properties of peptide molecules, peptide is used as an excellent precursor material for the synthesis of functional nanodrugs for highly effective cancer therapy. Herein, recent progress on the design, synthesis, functional regulation, and cancer bioimaging and biotherapy of peptide-based nanodrugs is summarized. For this aim, first molecular design and controllable synthesis of peptide nanodrugs with 0D to 3D structures are presented, and then the functional customization strategies for peptide nanodrugs are presented. Then, the applications of peptide-based nanodrugs in bioimaging, chemotherapy, photothermal therapy (PTT), and photodynamic therapy (PDT) are demonstrated and discussed in detail. Furthermore, peptide-based drugs in preclinical, clinical trials, and approved are briefly described. Finally, the challenges and potential solutions are pointed out on addressing the questions of this promising research topic. This comprehensive review can guide the motif design and functional regulation of peptide nanomaterials for facile synthesis of nanodrugs, and further promote their practical applications for diagnostics and therapy of diseases.

The Current Developments in Medicinal Plant Genomics Enabled the Diversification of Secondary Metabolites' Biosynthesis.

Medicinal plants produce important substrates for their adaptation and defenses against environmental factors and, at the same time, are used for traditional medicine and industrial additives. Plants have relatively little in the way of secondary metabolites via biosynthesis. Recently, the whole-genome sequencing of medicinal plants and the identification of secondary metabolite production were revolutionized by the rapid development and cheap cost of sequencing technology. Advances in functional genomics, such as transcriptomics, proteomics, and metabolomics, pave the way for discoveries in secondary metabolites and related key genes. The multi-omics approaches can offer tremendous insight into the variety, distribution, and development of biosynthetic gene clusters (BGCs). Although many reviews have reported on the plant and medicinal plant genome, chemistry, and pharmacology, there is no review giving a comprehensive report about the medicinal plant genome and multi-omics approaches to study the biosynthesis pathway of secondary metabolites. Here, we introduce the medicinal plant genome and the application of multi-omics tools for identifying genes related to the biosynthesis pathway of secondary metabolites. Moreover, we explore comparative genomics and polyploidy for gene family analysis in medicinal plants. This study promotes medicinal plant genomics, which contributes to the biosynthesis and screening of plant substrates and plant-based drugs and prompts the research efficiency of traditional medicine.

Biomolecule-mimetic nanomaterials for photothermal and photodynamic therapy of cancers: Bridging nanobiotechnology and biomedicine.

Nanomaterial-based phototherapy has become an important research direction for cancer therapy, but it still to face some obstacles, such as the toxic side effects and low target specificity. The biomimetic synthesis of nanomaterials using biomolecules is a potential strategy to improve photothermal therapy (PTT) and photodynamic therapy (PDT) techniques due to their endowed biocompatibility, degradability, low toxicity, and specific targeting. This review presents recent advances in the biomolecule-mimetic synthesis of functional nanomaterials for PTT and PDT of cancers. First, we introduce four biomimetic synthesis methods via some case studies and discuss the advantages of each method. Then, we introduce the synthesis of nanomaterials using some biomolecules such as DNA, RNA, protein, peptide, polydopamine, and others, and discuss in detail how to regulate the structure and functions of the obtained biomimetic nanomaterials. Finally, potential applications of biomimetic nanomaterials for both PTT and PDT of cancers are demonstrated and discussed. We believe that this work is valuable for readers to understand the mechanisms of biomimetic synthesis and nanomaterial-based phototherapy techniques, and will contribute to bridging nanotechnology and biomedicine to realize novel highly effective cancer therapies.

Biomimetic graphene-supported ultrafine platinum nanowires for colorimetric and electrochemical detection of hydrogen peroxide.

The detection of hydrogen peroxide (H2O2) is of great significance in environmental monitoring, enzymatic reactions, and disease diagnosis. Here we present the peptide-mediated biomimetic synthesis of ultrafine platinum nanowires (PtNWs) on graphene oxide (GO) nanosheets for the formation of functional hybrids, which show high potential for the fabrication of colorimetric and electrochemical sensors for the detection of H2O2 with high performance. A multifunctional peptide with the sequence KIIIIKYWYAF was designed to create peptide nanofibers (PNFs) via a controllable self-assembly process, which serves as a bridge between GO nanosheets and PtNWs to form PtNWs-PNFs/GO hybrids. On this basis, a dual-mode sensor platform for both colorimetric and electrochemical sensing of H2O2 was fabricated successfully. The obtained results indicate that the synthesized PtNWs-PNFs/GO hybrids could catalyze the decomposition of H2O2 to generate ˙OH radicals with a significant current response, and the ˙OH radicals are capable of overoxidizing 3,3',5,5',-tetramethylbenzidine (TMB), producing a blue-colored species with a distinct color change for colorimetric sensing. In addition, due to its high catalytic activity, the fabricated PtNWs-PNFs/GO hybrid-based electrochemical sensor exhibits a wider linear detection range of 0.05 µM-15 mM and a low detection limit of 0.0206 µM, which can be applied to detect H2O2 with high selectivity and sensitivity. Our study provides a green and environmentally friendly synthetic strategy for the preparation of biomimetic materials from PtNWs, and the fabricated colorimetric/electrochemical dual-mode H2O2 sensor platform will have a great impact in bioanalysis, environmental monitoring, and biomedicine.

Effects of plastic film mulching on the spatiotemporal distribution of soil water, temperature, and photosynthetic active radiation in a cotton field.

Plastic film mulching (PFM) affects the spatiotemporal distribution of soil moisture and temperature, which in turn affects cotton growth and the spatiotemporal distribution of canopy photosynthetically active radiation (PAR). Due to the spatial heterogeneity of soil moisture, temperature and limited monitoring methods, the issues such as relatively few sampling points and long sampling intervals in most existing studies prevent the accurate quantification of spatiotemporal changes in moisture and temperature along soil profile. To investigate the effects of PFM on spatiotemporal changes in soil moisture, temperature, and canopy PAR in cotton fields, two field trials of plastic film-mulched (M) and nonmulched (NM) cultivations were performed in 2018 and 2019. The grid method was used for the soil information continuous monitoring and multiple-time fixed-site canopy PAR monitoring during the duration of cotton growth. Two-year field trial data showed that, M cultivation increased soil moisture by approximately 13.6%-25% and increased temperature by 2-4 °C in the 0-50 cm soil layer before the first irrigation (June 20) and by 1-2 °C in the 70-110 cm soil layer, compared with NM cultivation. In addition, the temperature difference between the two treatments gradually decreased with the increase in irrigation and air temperature. The M treatment reached the peak PAR interception rate 10 days earlier than the NM treatment. In 2018 and 2019, the PAR peak value under the M treatment was 4.62% and 1.8% higher than that under the NM treatment, respectively, but the PAR interception rate was decreased rapidly in the late growth stage. Overall, PFM had an effect on soil moisture retention during the whole growth period and greatly increased the soil temperature before budding stage, thus promoted the early growth of cotton. Considering this, we suggest that the irrigation quota and frequency could be appropriately decreased in the case of plastic film mulching cultivation. For nonmulching cultivation, the irrigation quota and frequency should be increased, and it is necessary to take measures to improve the soil temperature before middle July.

An Internet-Oriented Multilayer Network Model Characterization and Robustness Analysis Method.

The Internet creates multidimensional and complex relationships in terms of the composition, application and mapping of social users. Most of the previous related research has focused on the single-layer topology of physical device networks but ignored the study of service access relationships and the social structure of users on the Internet. Here, we propose a composite framework to understand how the interaction between the physical devices network, business application network, and user role network affects the robustness of the entire Internet. In this paper, a multilayer network consisting of a physical device layer, business application layer and user role layer is constructed by collecting experimental network data. We characterize the disturbance process of the entire multilayer network when a physical entity device fails by designing nodal disturbance to investigate the interactions that exist between the different network layers. Meanwhile, we analyze the characteristics of the Internet-oriented multilayer network structure and propose a heuristic multilayer network topology generation algorithm based on the initial routing topology and networking pattern, which simulates the evolution process of multilayer network topology. To further analyze the robustness of this multilayer network model, we combined a total of six target node ranking indicators including random strategy, degree centrality, betweenness centrality, closeness centrality, clustering coefficient and network constraint coefficient, performed node deletion simulations in the experimental network, and analyzed the impact of component types and interactions on the robustness of the overall multilayer network based on the maximum component change in the network. These results provide new insights into the operational processes of the Internet from a multi-domain data fusion perspective, reflecting that the coupling relationships that exist between the different interaction layers are closely linked to the robustness of multilayer networks.

High-Speed Path Probing Method for Large-Scale Network.

In large-scale network topology discovery, due to the complex network structure and dynamic change characteristics, it is always the focus of network topology measurement to obtain as many network paths as possible in a short time. In this paper, we propose a large-scale network path probing approach in order to solve the problems of low probing efficiency and high probing redundancy commonly found in current research. By improving the packet delivery order and the update strategy of time-to-live field values, we redesigned and implemented an efficient large-scale network path probing tool. The experimental results show that the method-derived tool can complete path probing for a sample of 12 million/24 network address segments worldwide within 1 hour, which greatly improves the efficiency of network path probing. Meanwhile, compared to existing methods, the proposed method can reduce the number of packets sent by about 10% with the same number of network addresses found, which effectively reduces probing redundancy and alleviates the network load.

Metabolism-Associated DNA Methylation Signature Stratifies Lower-Grade Glioma Patients and Predicts Response to Immunotherapy.

Metabolism and DNA methylation (DNAm) are closely linked. The value of the metabolism-DNAm interplay in stratifying glioma patients has not been explored. In the present study, we aimed to stratify lower-grade glioma (LGG) patients based on the DNAm associated with metabolic reprogramming. Four data sets of LGGs from three databases (TCGA/CGGA/GEO) were used in this study. By screening the Kendall's correlation of DNAm with 87 metabolic processes from KEGG, we identified 391 CpGs with a strong correlation with metabolism. Based on these metabolism-associated CpGs, we performed consensus clustering and identified three distinct subgroups of LGGs. These three subgroups were characterized by distinct molecular features and clinical outcomes. We also constructed a subgroup-related, quantifiable CpG signature with strong prognostic power to stratify LGGs. It also serves as a potential biomarker to predict the response to immunotherapy. Overall, our findings provide new perspectives for the stratification of LGGs and for understanding the mechanisms driving malignancy.

Link importance assessment strategy based on improved k-core decomposition in complex networks.

Improving the effectiveness of target link importance assessment strategy has become an important research direction within the field of complex networks today. The reasearch shows that the link importance assessment strategy based on betweenness centrality is the current optimal solution, but its high computational complexity makes it difficult to meet the application requirements of large-scale networks. The k-core decomposition method, as a theoretical tool that can effectively analyze and characterize the topological properties of complex networks and systems, has been introduced to facilitate the generation of link importance assessment strategy and, based on this, a link importance assessment indicator link shell has been developed. The strategy achieves better results in numerical simulations. In this study, we incorporated topological overlap theory to further optimize the attack effect and propose a new link importance assessment indicator link topological shell called t-shell. Simulations using real world networks and scale-free networks show that t-shell based target link importance assessment strategies perform better than shell based strategies without increasing the computational complexity; this can provide new ideas for the study of large-scale network destruction strategies.

Two-Dimensional Material-Based Electrochemical Sensors/Biosensors for Food Safety and Biomolecular Detection.

Two-dimensional materials (2DMs) exhibited great potential for applications in materials science, energy storage, environmental science, biomedicine, sensors/biosensors, and others due to their unique physical, chemical, and biological properties. In this review, we present recent advances in the fabrication of 2DM-based electrochemical sensors and biosensors for applications in food safety and biomolecular detection that are related to human health. For this aim, firstly, we introduced the bottom-up and top-down synthesis methods of various 2DMs, such as graphene, transition metal oxides, transition metal dichalcogenides, MXenes, and several other graphene-like materials, and then we demonstrated the structure and surface chemistry of these 2DMs, which play a crucial role in the functionalization of 2DMs and subsequent composition with other nanoscale building blocks such as nanoparticles, biomolecules, and polymers. Then, the 2DM-based electrochemical sensors/biosensors for the detection of nitrite, heavy metal ions, antibiotics, and pesticides in foods and drinks are introduced. Meanwhile, the 2DM-based sensors for the determination and monitoring of key small molecules that are related to diseases and human health are presented and commented on. We believe that this review will be helpful for promoting 2DMs to construct novel electronic sensors and nanodevices for food safety and health monitoring.

The Combination of Two-Dimensional Nanomaterials with Metal Oxide Nanoparticles for Gas Sensors: A Review.

Metal oxide nanoparticles have been widely utilized for the fabrication of functional gas sensors to determine various flammable, explosive, toxic, and harmful gases due to their advantages of low cost, fast response, and high sensitivity. However, metal oxide-based gas sensors reveal the shortcomings of high operating temperature, high power requirement, and low selectivity, which limited their rapid development in the fabrication of high-performance gas sensors. The combination of metal oxides with two-dimensional (2D) nanomaterials to construct a heterostructure can hybridize the advantages of each other and overcome their respective shortcomings, thereby improving the sensing performance of the fabricated gas sensors. In this review, we present recent advances in the fabrication of metal oxide-, 2D nanomaterials-, as well as 2D material/metal oxide composite-based gas sensors with highly sensitive and selective functions. To achieve this aim, we firstly introduce the working principles of various gas sensors, and then discuss the factors that could affect the sensitivity of gas sensors. After that, a lot of cases on the fabrication of gas sensors by using metal oxides, 2D materials, and 2D material/metal oxide composites are demonstrated. Finally, we summarize the current development and discuss potential research directions in this promising topic. We believe in this work is helpful for the readers in multidiscipline research fields like materials science, nanotechnology, chemical engineering, environmental science, and other related aspects.

Two-dimensional material-based functional aerogels for treating hazards in the environment: synthesis, functional tailoring, applications, and sustainability analysis.

Environmental pollution is a global problem that endangers human health and ecological balance. As a new type of functional material, two-dimensional material (2DM)-based aerogel is one of the most promising candidates for pollutant detection and environmental remediation. The porous, network-like, interconnected three-dimensional (3D) structure of 2DM-based aerogels can not only preserve the characteristics of the original 2DMs, but also bring many distinct physical and chemical properties to offer abundant active sites for adsorbing and combining pollutants, thereby facilitating highly efficient monitoring and treatment of hazardous pollutants. In this review, the synthesis methods of 2DM aerogels and their broad environmental applications, including various sensors, adsorbents, and photocatalysts for the detection and treatment of pollutants, are summarized and discussed. In addition, the sustainability of 2DM aerogels compared to other water purification materials, such as activated carbon, 2DMs, and other aerogels are analyzed by the Sustainability Footprint method. According to the characteristics of different 2DMs, special focuses and perspectives are given on the adsorption properties of graphene, MXene, and boron nitride aerogels, as well as the sensing and photocatalytic properties of transition metal dichalcogenide/oxide and carbon nitride aerogels. This comprehensive work introduces the synthesis, modification, and functional tailoring strategies of different 2DM aerogels, as well as their unique characteristics of adsorption, photocatalysis, and recovery, which will be useful for the readers in various fields of materials science, nanotechnology, environmental science, bioanalysis, and others.