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Abstract Recent studies have shown that aqueous U(VI) ions can be transformed into U(VI) precipitates through electrocatalytic redox reactions for uranium recovery. However, there have been no reports of U(IV) solids, such as UO2, using electrochemical methods under ambient conditions since low-valence states of uranium are typically oxidized to U(VI) by O2 or H2O2. Here we developed a secondary metal ion-induced strategy for electrocatalytic production of U(IV) solids from U(VI) solutions using a catalyst consisting of atomically dispersed gallium on hollow nitrogen-doped carbon capsules (Ga-Nx-C). This method relies on the presence of secondary metal ions, e.g., alkaline earth metals, transition metals, lanthanide metals, and actinide metals, which promote the generation of UO2 or bimetallic U(IV)-containing oxides through a two-electron transfer process. No U(IV) solid products were generated in the presence of alkali metal ions. Mechanistic studies revealed that the strong binding affinity between U(IV) and alkaline earth metals (Ca2+/Mg2+/Sr2+/Ba2+), transition metals (Ni2+/Zn2+/Pb2+/Fe3+, etc.) and lanthanide/actinide metals (Ce4+/Eu3+/Th4+/La3+) suppressed re-oxidation of U(IV) to U(VI), leading to the generation of U(IV)O2 and Mx(M = Ce, Eu, Th, La)U(IV)yO2. This work provides fundamental insights into the electrochemical behavior of uranium in aqueous media, whilst guiding uranyl capture from nuclear waste and contaminated water.

With the rapid development of industry, agriculture, and urbanization, various organic pollutants have accumulated in natural water, posing a potential threat to both the ecological environment and human beings, and removing organic pollutants from water is an urgent priority. Piezoelectric techniques, with the advantages of green, simple operation, and high efficiency, are highly sought after in the degradation of environmental organic pollutants. Moreover, combining piezoelectric techniques with advanced oxidation processes (AOPs), photocatalysis, or electrocatalysis can further effectively promote the efficient degradation of target pollutants. Therefore, a perspective is presented on the recent progress of piezoelectric techniques for the degradation of various organic pollutants from aqueous solutions. The classification of various piezoelectric materials, as well as modification strategies for improving piezocatalysis, are first systematically summarized. Furthermore, the latest research on piezocatalysis and its combination with other technologies, such as AOPs, photocatalysis, and electrocatalysis, in the degradation of environmental pollutants is discussed. The potential mechanisms of piezocatalysis are also analyzed in depth. Finally, the urgent challenges and future opportunities for piezoelectric techniques in the degradation of organic pollutants are provided.

Abstract Consecutive uranium extraction from seawater is a promising approach to secure the long-term supply of uranium and the sustainability of nuclear energy. Here, we report an ultra-highly efficient strategy via studtite nanodots growth with impressive uranyl uptake capacity of ~ 154.50 mg/g from natural seawater in 12 consecutive days (i.e., average for ~ 12.875 mg/g/day). Uranyl can be extracted as studtite under visible light via the reaction between the adsorbed uranyl and the photogenerated H2O2 with imine-based Covalent-Organic Framework photocatalysts. In detail, over Tp-Bpy, Tp-Bpy-2 and Tp-Py with multiple uranyl chelating sites, uranyl is found extracted as studtite nanodots which can be eluted readily, while over Tp-Bd and Tb-Bpy, uranyl is transformed into studtite nanorods that is more inert for elution. Abundant chelating sites of uranyl via structural regulation of COF photocatalysts are proved to facilitate the formation and efficient elution of studtite nanodots.

Abstract Extraction of U(VI) in water is of great significance in energy and environmental fields. However, the traditional methods usually fail due to the indispensable extra addition of catalyst, adsorbent, precipitant, or sacrificial agents, which may lead to enhanced extraction costs and secondary pollution. Here, a new efficient uranium extraction strategy is proposed based on triboelectricity without adding a catalyst or other additives. It is found only under the friction between the microbubbles (generated under ultrasonication) and the water flow, that reactive oxygen species (ROS) can largely be generated, which thus contributes to the solidification of U(VI) from water. In addition, the magnetic field can affect the phase of the product. Under mechanical stirring, the product contains (UO2)O2·2H2O, while which contains UO2(OH)2 and (UO2)O2·4H2O under the magnetic stirring. Quenching experiments are also carried out to explore the influence of environmental factors. Most importantly, it shows great potential in the extraction of U(VI) from seawater. This work proposes a catalyst‐free and light‐free strategy toward the solidification of U(VI) from water, which avoids the secondary pollution of the catalyst to the environment and is low‐cost, and has great potential in the real application.

Abstract The utilization of nuclear energy power and nuclear weapon tests not only releases large amounts of radionuclides into environment, but also needs 235U as nuclear fuel for nuclear energy generation. Covalent organic frameworks (COFs) have the advantages of tunable porous structures, adjustable active sites and enough special functional groups, which assure the high selective preconcentration of target radionuclides from complex solutions. In this perspective, the selective extraction of radionuclides (U(VI) as representative cationic ion, Tc(VII) as representative anionic ion, I2 as gaseous nuclide and other nuclides) by COFs through sorption, and photocatalytic strategies are described, and the results show the high efficiency of COFs in target radionuclides removal. The perspective and challenges for the real applications of COFs in future are discussed in the end. Graphical Abstract

Abstract Radioiodine capture from nuclear fuel waste and contaminated water sources is of enormous environmental importance, but remains technically challenging. Herein, we demonstrate robust covalent organic frameworks (COFs) with antiparallel stacked structures, excellent radiation resistance, and high binding affinities toward I2, CH3I, and I3 − under various conditions. A neutral framework (ACOF-1) achieves a high affinity through the cooperative functions of pyridine-N and hydrazine groups from antiparallel stacking layers, resulting in a high capacity of ~2.16 g/g for I2 and ~0.74 g/g for CH3I at 25 °C under dynamic adsorption conditions. Subsequently, post-synthetic methylation of ACOF-1 converted pyridine-N sites to cationic pyridinium moieties, yielding a cationic framework (namely ACOF-1R) with enhanced capacity for triiodide ion capture from contaminated water. ACOF-1R can rapidly decontaminate iodine polluted groundwater to drinking levels with a high uptake capacity of ~4.46 g/g established through column breakthrough tests. The cooperative functions of specific binding moieties make ACOF-1 and ACOF-1R promising adsorbents for radioiodine pollutants treatment under practical conditions.

Technetium-99 (99Tc), predominantly present as pertechnetate (99TcO4–), is a challenging contaminant in nuclear waste from artificial nuclear fission. The selective removal of 99TcO4– from nuclear waste and contaminated groundwater is complex due to (i) the acidic and intricate nature of high-level liquid wastes; (ii) the highly alkaline environment in low-activity level tank wastes, such as those at Hanford, and in high-level wastes at locations like Savannah River; and (iii) the potential for 99TcO4– to leak into groundwater, risking severe water pollution due to its high mobility. This Review focuses on recent developments in advanced porous materials, including metal–organic frameworks (MOFs), covalent organic frameworks (COFs), and their amorphous counterparts, porous organic polymers (POPs). These materials have demonstrated exceptional effectiveness in adsorbing 99TcO4– and similar oxyanions. We comprehensively review the adsorption mechanisms of these anions with the adsorbents, employing macroscopic batch/column experiments, microscopic spectroscopic analyses, and theoretical calculations. In conclusion, we present our perspectives on potential future research directions, aiming to overcome current challenges and explore new opportunities in this area. Our goal is to encourage further research into the development of advanced porous materials for efficient 99TcO4– management.

Abstract The use of machine learning (ML) in the field of predicting heavy metals interaction with biochar is a promising field of research, mainly because of the growing understanding of how removal efficiency is affected by characteristic variables, reaction conditions and biochar properties. The practical application in biochar still faces large challenges, such as difficulties in data collection, inadequate algorithm development, and insufficient information. However, the quantity, quality, and representation of data have a large impact on the accuracy, efficiency, and generalizability of machine learning tasks. From this perspective, the present data descriptors, the efficiency of machine learning-aided property and performance prediction, the interpretation of underlying mechanisms and complicated relationships, and some potential ways to augment the data are discussed regarding the interactions of heavy metals with biochar. Finally, future perspectives and challenges are discussed, and an enhanced model performance is proposed to reinforce the feasibility of a particular perspective. Graphical Abstract

Japan recently announced the plan to discharge over 1.2 million tons of radioactive water into the Pacific Ocean, which contained hazardous radionuclides such as 60Co, 90Sr, 125Sb, 129I, 3H, 137Cs, and 99TcO4−, etc. The contaminated water will pose an enormous threat to global ecosystems and human health. Developing materials and technologies for efficient radionuclide removal is highly desirable and arduous because of the extreme conditions, including super acidity or alkalinity, high ionic strength, and strong ionizing radiation. Recently, advanced porous material, such as porous POPs, MOFs, COFs, PAFs, etc., has shown promise of improved separation of radionuclides due to their intrinsic structural advantages. Furthermore, emerging technologies applied to radionuclide removal have also been summarized. In order to better deal with radionuclide contamination, higher requirements for the design of nanomaterials and technologies applied to practical radionuclide removal are proposed. Finally, we call for comprehensive implementation of strategies and strengthened cooperation to mitigate the harm caused by radioactive contamination to oceans, atmosphere, soil, and human health.

To date, significant efforts have been devoted to eliminating hazardous components to purify wastewater through the development of various nanomaterials. Covalent organic frameworks (COFs), an important branch of the porous crystalline family, possess the peculiarity of ultrahigh surface area, adjustable pore size, and facile functionality. Exciting studies from design fabrication to potential applications in water treatment by COF-based membranes (COMs) have emerged. This review summarizes various preparation strategies and synthesis mechanisms for COMs, including layer-by-layer stacking, in situ growth, interfacial polymerization, and electrochemical synthesis, and briefly describes the advanced characterization techniques for COMs. Moreover, the application of COMs in heavy metal removal, dye separation, purification of radionuclides, pollutant detection, sea water desalination, and so on, is described and discussed. Finally, the perspectives on future opportunities for designing COMs in water purification have been proposed.

Model-based methods require an accurate dynamic model to design the controller. However, the hydraulic parameters of nonlinear systems, complex friction, or actuator dynamics make it challenging to obtain accurate models. In this case, using the input-output data of the system to learn a dynamic model is an alternative approach. Therefore, we propose a dynamic model based on the Gaussian process (GP) to construct systems with control constraints. Since GP provides a measure of model confidence, it can deal with uncertainty. Unfortunately, most GP-based literature considers model uncertainty but does not consider the effect of constraints on inputs in closed-loop systems. An auxiliary system is developed to deal with the influence of the saturation constraints of input. Meanwhile, we relax the nonsingular assumption of the control coefficients to construct the controller. Some numerical results verify the rationality of the proposed approach and compare it with similar methods.

Abstract Biochar and biochar-based materials have been studied extensively in multidisciplinary areas because of their outstanding physicochemical properties. In this review article, biochar and biochar-based materials in the removal of environmental pollutants, hydrogen generation and carbon dioxide capture were summarized and compared. The interaction mechanisms were discussed from the experimental results and characterization analysis. The high porous structures, active surface sites, (co)doping of single metals/nonmetals, and incorporation of metal oxides or other materials improved the high activity of biochar-based materials in their applications. However, there are still some challenges such as: (1) the fact that H2 generation with high selectivity or the produced syngas to meet the real application requirement in industrial is the main challenge in H2 production; (2) the fact that the selective capture of CO2 with high stability, high adsorption capacity and recyclability at low-cost should be considered and focused on; (3) the sorption-(photo)degradation of the organic chemicals; and (4) the fact that the sorption-reduction-extraction/solidification of metals/radionuclides are efficient methods for the elimination of environmental pollutants. In the end, the perspectives, challenges and possible techniques for biochar-based materials’ real application in future were described. Graphical Abstract

Removing trace amounts of acetylene (C2H2) from ethylene (C2H4)-rich gas mixtures is vital for the supply of high-purity C2H4 to the chemical industry and plastics sector. However, selective removal of C2H2 is challenging due to the similar physical and chemical properties of C2H2 and C2H4. Here, we report a “single-molecule trap” strategy that utilizes electrostatic interactions between the one-dimensional (1D) channel of a covalent organic framework (denoted as COF-1) and C2H2 molecules to massively enhance the adsorption selectivity toward C2H2 over C2H4. C2H2 molecules are immobilized via interactions with the O atom of C=O groups, the N atom of C≡N groups, and the H atom of phenyl groups in 1D channels of COF-1. Due to its exceptionally high affinity for C2H2, COF-1 delivered a remarkable C2H2 uptake of 7.97 cm3/g at 298 K and 0.01 bar, surpassing all reported COFs and many other state-of-the-art adsorbents under similar conditions. Further, COF-1 demonstrated outstanding performance for the separation of C2H2 and C2H4 in breakthrough experiments under dynamic conditions. COF-1 adsorbed C2H2 at a capacity of 0.17 cm3/g at 2,000 s/g when exposed to 0.5 ml/min C2H4-rich gas mixture (99% C2H4) at 298 K, directly producing high-purity C2H4 gas at a rate of 3.95 cm3/g. Computational simulations showed that the strong affinity between C2H2 and the single-molecule traps of COF-1 were responsible for the excellent separation performance. COF-1 is also robust, providing a promising new strategy for the efficient removal of trace amounts of C2H2 in practical C2H4 purification.

This paper proposes an online predictive control method for fixed-wing unmanned aerial vehicles (UAVs) with a pan-tilt camera in target tracking. It aims to achieve long-term tracking while concurrently maintaining the target near the image center. Particularly, this work takes the UAV and pan-tilt camera as an overall system and deals with the target tracking problem via joint optimization, so that the tracking ability of the UAV can be improved. The image captured by the pan-tilt camera is the unique input associated with the target, and model predictive control (MPC) is used to solve the optimization problem with constraints that cannot be performed by the classic image-based visual servoing (IBVS). In addition to the dynamic constraint of the UAV, the perception constraint of the camera is also taken into consideration, which is described by the maximum distance between the target and the camera. The accurate detection of the target depends on the amount of its feature information contained in the image, which is highly related to the relative distance between the target and the camera. Moreover, considering the real-time requirements of practical applications, an MPC strategy based on soft constraints and a warm start is presented. Furthermore, a switching-based approach is proposed to return the target back to the perception range quickly once it exceeds the range, and the exponential asymptotic stability of the switched controller is proven as well. Both numerical and hardware-in-the-loop (HITL) simulations are conducted to verify the effectiveness and superiority of the proposed method compared with the existing method.

Abstract The selective removal of the β‐emitting pertechnetate ion (99TcO4−) from nuclear waste streams is technically challenging. Herein, a practical approach is proposed for the selective removal of 99TcO4− (or its surrogate ReO4−) under extreme conditions of high acidity, alkalinity, ionic strength, and radiation field. Hollow porous N‐doped carbon capsules loaded with ruthenium clusters (Ru@HNCC) are first prepared, then modified with a cationic polymeric network (R) containing imidazolium‐N+ units (Ru@HNCC‐R) for selective 99TcO4− and ReO4− binding. The Ru@HNCC‐R capsules offer high binding affinities for 99TcO4−/ReO4− under wide‐ranging conditions. An electrochemical redox process then transforms adsorbed ReO4− to bulk ReO3, delivering record‐high removal capacities, fast kinetics, and excellent long‐term durability for removing ReO4− (as a proxy for 99TcO4−) in a 3 m HNO3, simulated nuclear waste‐Hanford melter recycle stream and an alkaline high‐level waste stream (HLW) at the U.S. Savannah River Site (SRS). In situ Raman and X‐ray absorption spectroscopy (XAS) analyses showed that adsorbed Re(VII) is electrocatalytically reduced on Ru sites to a Re(IV)O2 intermediate, which can then be re‐oxidized to insoluble Re(VI)O3 for facile collection. This approach overcomes many of the challenges associated with the selective separation and removal of 99TcO4−/ReO4− under extreme conditions, offering new vistas for nuclear waste management and environmental remediation.

Covalent organic frameworks (COFs) represent an emerging class of organic photocatalysts but it remains challenging to gain insight into photocatalytic active sites and reaction mechanisms. Herein, the authors construct a family of isoreticular crystalline hydrazide-based COF photocatalysts, with the optoelectronic properties and local pore characteristics of the COFs modulated using different linkers

Abstract The rational design of large‐area exposure, nonagglomeration, and long‐range dispersion of metal nanoparticles (NPs) in the catalysts is critical for the development of energy storage and conversion systems. Little attention has been focused on modulating and developing catalyst interface contact engineering between a carbon substrate and dispersed metal. Here, a highly dispersed ultrafine ruthenium (Ru) NP strategy by double spatial confinement is proposed, that is, incorporating directed growth of metal–organic framework crystals into a bacterial cellulose templating substrate to integrate their respective merits as an excellent electrocatalytic cathode catalyst for a quasi‐solid‐state Li–O2 battery. The porous carbon matrix with highly dispersed ultrafine Ru NPs is well designed and used as cathode catalysts in a Li–O2 battery, demonstrating a high discharge areal capacity of 6.82 mAh cm–2 at 0.02 mA cm–2, a high‐rate capability of 4.93 mAh cm–2 at 0.2 mA cm–2, and stable discharge/charge cycling for up to 500 cycles (2000 h) with low overpotentials of ~1.4 V. This fundamental understanding of the structure–performance relationship demonstrates a new and promising approach to optimize highly efficient cathode catalysts for solid‐state Li–O2 batteries.

Fault detection and localization are vital for ensuring the stability of data center networks (DCNs). Specifically, adaptive fault diagnosis is deemed a fundamental technology in achieving the fault tolerance of systems. The highly scalable data center network (HSDC) is a promising structure of server-centric DCNs, as it exhibits the capacity for incremental scalability, coupled with the assurance of low cost and energy consumption, low diameter, and high bisection width. In this paper, we first determine that both the connectivity and diagnosability of the m-dimensional complete HSDC, denoted by HSDCm(m), are m. Further, we propose an efficient adaptive fault diagnosis algorithm to diagnose an HSDCm(m) within three test rounds, and at most N+4m(m−2) tests with m≥3 (resp. at most nine tests with m=2), where N=m·2m is the total number of nodes in HSDCm(m). Our experimental outcomes demonstrate that this diagnosis scheme of HSDC can achieve complete diagnosis and significantly reduce the number of required tests.

Highlights 1. Adsorption-photocatalytic degradation of organic pollutants by biochar-based catalysts is summarized. 2. Adsorption-(photo)catalytic reduction-solidification of heavy metal ions is discussed. 3. Mechanism and active free radicals on the degradation/reduction of contaminants are compared and described. 4. The methods to improve the photocatalysis performance of biochar-based catalysts are introduced. 5. The challenges for the real application of biochar-based materials are provided.

Abstract For the robot control problems, the input constraint is an issue that must be carefully considered. Considering the control inputs of the robots in the multi‐robot system are constrained by different saturations in reality, we address the cooperative circumnavigation control problem of multiple unicycle‐type robots with non‐identical and unknown input constraints in this paper. A distributed control law is designed to drive the robots constrained by different and unknown input saturations to achieve the cooperative circumnavigation around a moving target. The algorithm proposed in this paper largely reduces the requirement for the maneuverability of the robots to achieve the cooperative circumnavigation. As long as the robots owns the basic maneuverability of circumnavigating around the target, it is proved rigorously that the proposed control algorithm can achieve the desired cooperation circumnavigation of the multi‐robot system. The effectiveness of the proposed method is validated by a numerical simulation.

As microplastic pollution has become an emerging environmental issue of global concern, microplastics in aquaculture have become a research hotspot. For environmental safety, economic efficiency and food safety considerations, a comprehensive understanding of microplastic pollution in aquaculture is necessary. This review outlines an overview of sources and effects of microplastics in aquaculture. External environmental inputs and aquaculture processes are sources of microplastics in aquaculture. Microplastics may release harmful additives and adsorb pollutants in aquaculture environment, cause deterioration of aquaculture environment, as well as cause toxicological effects, affect the behavior, growth and reproduction of aquaculture products, ultimately reducing the economic benefits of aquaculture. Microplastics entering the human body through aquaculture products also pose potential health risks at multiple levels. Microplastic pollution removal strategies used in aquaculture in various countries are also reviewed. Ecological interception and purification are considered to be effective methods. In addition, strengthening aquaculture management and improving fishing gear and packaging are also currently feasible solutions. As proactive measures, new portable microplastic monitoring system and remote sensing technology are considered to have broad application prospects. And it was encouraged to comprehensively strengthen the supervision of microplastic pollution in aquaculture through talent exchange and strengthening the construction of laws and regulations.

The large-scale synthesis of platinum-free electrocatalysts for the oxygen reduction reaction (ORR) remains a grand challenge. We report the large-scale production of stable and active ORR electrocatalysts based on iron, an earth-abundant element. A core–shell zeolitic imidazolate framework–tannic acid coordination polymer composite (ZIF-8@K-TA) was utilized as the catalyst precursor, which was transformed into iron atoms dispersed in hollow porous nitrogen-doped carbon capsules (H-Fe-Nx-C) through ion exchange and pyrolysis. H-Fe-Nx-C features site-isolated single-atom iron centers coordinated to nitrogen in graphitic layers, high levels of nitrogen doping, and high permeability to incoming gases. Benefiting from these characteristics, H-Fe-Nx-C demonstrated efficient electrocatalytic activity (E1/2 ​= ​0.92 ​V, vs. RHE) and stability towards the ORR in both alkaline and acidic media. In ORR performance, it surpassed the majority of recently reported Fe-N-C catalysts and the standard Pt/C catalyst. In addition, H-Fe-Nx-C showed outstanding tolerance to methanol.

As a result of increasing urbanization, traffic monitoring in cities has become a challenging task. The use of Unmanned Aerial Vehicles (UAVs) provides an attractive solution to this problem. Multi-Object Tracking (MOT) for UAVs is a key technology to fulfill this task. Traditional detection-based-tracking (DBT) methods begin by employing an object detector to retrieve targets in each image and then track them based on a matching algorithm. Recently, the popular multi-task learning methods have been dominating this area, since they can detect targets and extract Re-Identification (Re-ID) features in a computationally efficient way. However, the detection task and the tracking task have conflicting requirements on image features, leading to the poor performance of the joint learning model compared to separate detection and tracking methods. The problem is more severe when it comes to UAV images due to the presence of irregular motion of a large number of small targets. In this paper, we propose using a balanced Joint Detection and Re-ID learning (JDR) network to address the MOT problem in UAV vision. To better handle the non-uniform motion of objects in UAV videos, the Set-Membership Filter is applied, which describes object state as a bounded set. An appearance-matching cascade is then proposed based on the target state set. Furthermore, a Motion-Mutation module is designed to address the challenges posed by the abrupt motion of UAV. Extensive experiments on the VisDrone2019-MOT dataset certify that our proposed model, referred to as SMFMOT, outperforms the state-of-the-art models by a wide margin and achieves superior performance in the MOT tasks in UAV videos.

The multi-UAV task assignment problem in large-scale group-to-group interception scenarios presents challenges in terms of large computational complexity and the lack of accurate evaluation models. This paper proposes an effective evaluation model and hierarchical task assignment framework to address these challenges. The evaluation model incorporates the dynamics constraints specific to fixed-wing UAVs and improves the Apollonius circle model to accurately describe the cooperative interception effectiveness of multiple UAVs. By evaluating the interception effectiveness during the interception process, the assignment scheme of the multiple UAVs could be given based on the model. To optimize the configuration of UAVs and targets, a hierarchical framework based on the network flow algorithm is employed. This framework utilizes a clustering method based on feature similarity and interception advantage to decompose the large-scale task assignment problem into smaller, complete submodels. Following the assignment, Dubins curves are planned to the optimal interception points, ensuring the effectiveness of the interception task. Simulation results demonstrate the feasibility and effectiveness of the proposed scheme. With the increase in the model scale, the proposed scheme has a greater descending rate of runtime. In a large-scale scenario involving 200 UAVs and 100 targets, the runtime is reduced by 84.86%.

Abstract Ultrasmall metal nanoparticles (MNPs) and atomically dispersed metal sites (ADMSs) exhibit excellent catalytic activity and selectivity. However, they tend to aggregate into large particles during synthesis and catalysis due to high surface free energy. Aiming to break out of this dilemma, zeolite imidazolate frameworks (ZIFs) have proved to be ideal substrates for stabilizing the ultrafine metal nanostructures. The stabilizing mode includes confining MNPs inside their pore structures and converting intrinsic metal nodes into coordinately unsaturated metal sites (i.e., ZIF‐confined metal catalysts). Furthermore, various MNPs and ADMSs can also be immobilized into ZIF‐derived porous carbon through high‐temperature pyrolysis (i.e., ZIF‐derived carbon‐supported metal catalysts). In brief, ZIFs and their derivatives have gradually emerged as good platforms for designing metal catalysts at nanoscale and even atomic level. In this review, recent progress in the structural analysis and synthetic strategies of metal catalysts supported by ZIFs and their derivatives is presented. Besides, current challenges and future directions of atomically dispersed metal catalysts are discussed. This article will provide valuable insights into rational design of metal catalysts with exquisite nanostructure and high catalytic performance to meet further material requirements in the catalytic field.

Metal-organic frameworks (MOFs) have garnered multidisciplinary attention due to their structural tailorability, controlled pore size, and physicochemical functions, and their inherent properties can be exploited by applying them as precursors and/or templates for fabricating derived hollow porous nanomaterials. The fascinating, functional properties and applications of MOF-derived hollow porous materials primarily lie in their chemical composition, hollow character, and unique porous structure. Herein, a comprehensive overview of the synthetic strategies and emerging applications of hollow porous materials derived from MOF-based templates and/or precursors is given. Based on the role of MOFs in the preparation of hollow porous materials, the synthetic strategies are described in detail, including (1) MOFs as removable templates, (2) MOF nanocrystals as both self-sacrificing templates and precursors, (3) MOF@secondary-component core-shell composites as precursors, and (4) hollow MOF nanocrystals and their composites as precursors. Subsequently, the applications of these hollow porous materials for chemical catalysis, electrocatalysis, energy storage and conversion, and environmental management are presented. Finally, a perspective on the research challenges and future opportunities and prospects for MOF-derived hollow materials is provided. Public summary: • MOFs have garnered multi-disciplinary attention due to their unique inherent properties • Various synthetic strategies of MOFs-derived hollow porous materials are summarized • Emerging applications of MOFs-derived hollow porous materials are reviewed

Abstract Seawater contains uranium at a concentration of ≈3.3 ppb, thus representing a rich and sustainable nuclear fuel source. Herein, an adsorption–electrocatalytic platform is developed for uranium extraction from seawater, comprising atomically dispersed indium anchored on hollow nitrogen‐doped carbon capsules functionalized with flexible amidoxime moieties (In–Nx–C–R, where R denotes amidoxime groups). In–Nx–C–R exhibits excellent uranyl capture properties, enabling a uranium removal rate of 6.35 mg g−1 in 24 h, representing one of the best uranium extractants reported to date. Importantly, In–Nx–C–R demonstrates exceptional selectivity for uranium extraction relative to vanadium in seawater (8.75 times more selective for the former). X‐ray absorption spectroscopy (XAS) reveals that the amidoxime groups serve as uranyl chelating sites, thus allowing selective adsorption over other ions. XAS and in situ Raman results directly indicate that the absorbed uranyl can be electrocatalytically reduced to an unstable U(V) intermediate, then re‐oxidizes to U(VI) in the form of insoluble Na2O(UO3·H2O)x for collection, through reversible single electron transfer processes involving InNx sites. These results provide detailed mechanistic understanding of the uranium extraction process at a molecular level. This work provides a roadmap for the adsorption–electrocatalytic extraction of uranium from seawater, adding to the growing suite of technologies for harvesting valuable metals from the earth's oceans.

In this work, a CdS/BiVO4 step-scheme (S-scheme) heterojunction with self-photothermally enhanced photocatalytic effect was synthesized and applied for efficient U(VI) photoextraction. Characterizations such as transient absorption spectroscopy and Tafel test together confirmed the formation of S-scheme heterojunctions, which allows CdS/BiVO4 to avoid photocorrosion while retaining the strong reducing capacity of CdS and the oxidizing capacity of BiVO4. Experimental results such as radical quenching experiments and electron spin resonance show that U(VI) is rapidly oxidized by photoholes/•OH to insoluble UO2(OH)2 after being reduced to U(IV) by photoelectrons/•O2-, which precisely avoids the depletion of electron sacrificial agents. The rapid recombination of electron-hole pairs triggered by the S-scheme heterojunction is found to release large amounts of heat and accelerate the photocatalysis. This work offers a new enhanced strategy for photocatalytic uranium extraction and presents a direction for the design and development of new photocatalysts.

Clustering is an effective solution to improve the management efficiency of large-scale systems. One one hand, UAVs performing the same task have a similar moving tendency. One the other hand, the network topology of the UAV swarms is dynamically changing. Considering the above two aspects, our main contributrons are designing a group-oriented distributed clustering algorithm based on the coalition game that couples task attributes and communication attributes. The clustering goal is to divide UAVs performing the same task into one cluster and make each cluster have more UAVs under the cluster size limits, thus improving communication efficiency. The proposed algorithm comprehensively considers the task group information and communication link stability as the coalition value. UAVs decide whether to leave their current coalition based on coalition values. Through periodic parallel switch operations selection, UAVs are divided into the desired clustering structure. Simulations verify that our clustering algorithm is effective and better than the existing ones, especially in communication link stability, cluster numbers, and load balance.

This paper proposes an approximate optimal curve-path-tracking control algorithm for partially unknown nonlinear systems subject to asymmetric control input constraints. Firstly, the problem is simplified by introducing a feedforward control law, and a dedicated design for optimal control with asymmetric input constraints is provided by redesigning the control cost function in a non-quadratic form. Then, the optimality and stability of the derived optimal control policy is demonstrated. To solve the underlying tracking Hamilton–Jacobi–Bellman (HJB) equation in consideration of partially unknown systems, an integral reinforcement learning (IRL) algorithm is utilized using the neural network (NN)-based value function approximation. Finally, the effectiveness and generalization of the proposed method is verified by experiments carried out on a high-fidelity hardware-in-the-loop (HIL) simulation system for fixed-wing unmanned aerial vehicles (UAVs) in comparison with three other typical path-tracking control algorithms.

Taking advantage of faster speed, less resource consumption and better biological interpretability of spiking neural networks, this paper developed a novel spiking neural network reinforcement learning method using actor-critic architecture and temporal coding. The simple improved leaky integrate-and-fire (LIF) model was used to describe the behavior of a spike neuron. Then the actor-critic network structure and the update formulas using temporally encoded information were provided. The current model was finally examined in the decision-making task, the gridworld task, the UAV flying through a window task and the avoiding a flying basketball task. In the 5 × 5 grid map, the value function learned was close to the ideal situation and the quickest way from one state to another was found. A UAV trained by this method was able to fly through the window quickly in simulation. An actual flight test of a UAV avoiding a flying basketball was conducted. With this model, the success rate of the test was 96% and the average decision time was 41.3 ms. The results show the effectiveness and accuracy of the temporal coded spiking neural network RL method. In conclusion, an attempt was made to provide insights into developing spiking neural network reinforcement learning methods for decision-making and autonomous control of unmanned systems.

This paper proposes a new algorithm to solve the control problem for a special class of distance-based directed formations, namely directed-triangulated Laman graphs. The central idea of the algorithm is to construct a virtual point for the agents who have more than two neighbors by employing the information of the desired formation. Compared with the existing methods, the proposed algorithm can make the distance error between the agents converge faster and the path consumption is less. Furthermore, the proposed algorithm is modified to be operable for the small fixed-wing UAV model with nonholonomic and input constraints. Finally, the effectiveness of the proposed method is verified by a series of simulation experiments.

This paper presents an optimized image-based visual servo (IBVS) control scheme for tracking a ground target by using a fixed-wing unmanned aerial vehicle (UAV) with a monocular camera fixed on it. Unlike the widely used rotor UAVs, the fixed-wing UAV has much more dynamical constraints such as it cannot move omni-direction and its minimum speed is limited by the stalling speed. This makes the target tracking problem more challenging. The proposed scheme leverages the image Jacobian matrix to build a connection between the velocity of the feature point and that of the UAV. Afterward, considering the camera is fixed on the body of the UAV, an “ideal camera” model is proposed to compensate the shifts of the feature point caused by the changes of the UAV's attitude. Then, an optimized control law without solving the pseudo-inverse of the image Jacobian matrix is proposed with the aid of the least square method from the target center in the image coordinate system. This control law takes the velocity of the feature point as inputs and the yaw angular velocity of the UAV as outputs. The stability of the proposed law is analyzed with the Lyapunov method, showing that the UAV will circle around the ground target asymptotically. Finally, the proposed scheme is evaluated by a hardware-in-the-loop (HIL) simulation based on the Gazebo simulator and the off-the-shelf autopilot hardware.

In multiple unmanned aerial vehicle (UAV) formation control systems, high scalability can guarantee the formation stability when UAVs join in or leave the system, and thus improves the robustness of the formation flight. This paper investigates the scalability problem for multi-UAV formation control with double-integrator dynamics. To be more specific, we focus how to build communication links with fixed control parameters such that the formation can always keep stable when adding/removing arbitrary number of UAVs. A bio-inspired method - Veteran Rule is proposed to solve this problem. Compared to the existing methods, our proposed method does not require to re-design or adaptively adjust the control parameters/gains for the changed Laplacian matrix. Furthermore, the convergence rate of the system under the Veteran Rule is analyzed. Surprisingly, the convergence rate of the system reaches the maximum value when all the in-degrees equal a particular value, rather than goes to infinity. Moreover, to guarantee the robustness of the formation system, we study the tolerance on undesired communication links (which break our proposed Veteran Rule). An upper bound for the coupling strength of the undesired communication links is provided by using Gershgorin circle theorem. Finally, simulation results corroborate the effectiveness of our results.

Due to the nonlinear and asymmetric input constraints of the fixed-wing UAVs, it is a challenging task to design controllers for the fixed-wing UAV formation control. Distance-based formation control does not require global positions as well as the alignment of coordinates, which brings in great convenience for designing a distributed control law. Motivated by the facts mentioned above, in this paper, the problem of distance-based formation of fixed-wing UAVs with input constraints is studied. A low-gain formation controller, which is a generalized gradient controller of the potential function, is proposed. The desired formation can be achieved by the designed controller under the input constraints of the fixed-wing UAVs with proven stability. Finally, the effectiveness of the proposed method is verified by the numerical simulation and the semi-physical simulation.

Summary: Covalent organic frameworks (COFs) are a new type of crystalline porous polymers known for chemical stability, excellent structural regularity, robust framework, and inherent porosity, making them promising materials for capturing various types of pollutants from aqueous solutions. This review thoroughly presents the recent progress and advances of COFs and COF-based materials as superior adsorbents for the efficient removal of toxic heavy metal ions, radionuclides, and organic pollutants. Information about the interaction mechanisms between various pollutants and COF-based materials are summarized from the macroscopic and microscopic standpoints, including batch experiments, theoretical calculations, and advanced spectroscopy analysis. The adsorption properties of various COF-based materials are assessed and compared with other widely used adsorbents. Several commonly used strategies to enhance COF-based materials’ adsorption performance and the relationship between structural property and sorption ability are also discussed. Finally, a summary and perspective on the opportunities and challenges of COFs and COF-based materials are proposed to provide some inspiring information on designing and fabricating COFs and COF-based materials for environmental pollution management.

Metal-enhanced photocatalysis has recently received increasing interest, mainly due to the ability of metal to directly or indirectly degrade pollutants. In this review, we briefly review the recent breakthroughs in metal-enhanced photocatalysis. We discussed the recent progress of surface plasmon resonance (SPR) effect and small size effect of metal nanoparticles on photocatalysis; in particular, we focus on elucidating the mechanism of energy transfer and hot electron injection/transfer effect of metal nanoparticles and clusters while as photocatalysts or as cophotocatalysts. Finally, we discuss the potential applications of metal-enhanced photocatalysis, and we also offer some perspectives for further investigations.

With the fast development of industry and nuclear energy, large amounts of different radionuclides are inevitably released into the environment. The efficient solidification or elimination of radionuclides is thereby crucial to environmental pollution and human health because of the radioactive hazardous of long-lived radionuclides. The properties of negatively or positively charged radionuclides are quite different, which informs the difficulty of simultaneous elimination of the radionuclides. Herein, we summarized recent works about the selective sorption or catalytic reduction of target radionuclides using different kinds of nanomaterials, such as carbon-based nanomaterials, metal–organic frameworks, and covalent organic frameworks, and their interaction mechanisms are discussed in detail on the basis of batch sorption results, spectroscopy analysis and computational calculations. The sorption-photocatalytic/electrocatalytic reduction of radionuclides from high valent to low valent is an efficient strategy for in situ solidification/immobilization of radionuclides. The special functional groups for the high complexation of target radionuclides and the controlled structures of nanomaterials can selectively bind radionuclides from complicated systems. The challenges and future perspective are finally described, summarized, and discussed.

The tilt trirotor unmanned aerial vehicle (UAV) is a novel aircraft that has broad application prospects in transportation. However, the development progress of the aircraft is slow due to the complicated control system and the high cost of the flight experiment. This work attempts to overcome the problem by developing a hardware-in-the-loop (HIL) simulation system based on a heavily developed and commercially available flight simulator X-Plane. First, the tilt trirotor UAV configuration and dynamic model are presented, and the parameters are obtained by conducting identification experiments. Second, taking the configuration of the aircraft into account, a control scheme composed of the mode transition strategy, hierarchical controller, and control allocation is proposed. Third, a full-scale flight model of the prototype is designed in X-Plane, and an interface program is completed for connecting the autopilot and X-Plane. Then, the HIL simulation system that consists of the autopilot, ground control station, and X-Plane is developed. Finally, the results of the HIL simulation and flight experiments are presented and compared. The results show that the HIL simulation system can be an efficient tool for verifying the performance of the proposed control scheme for the tilt trirotor UAV. The work contributes to narrowing the gap between theory and practice and provides an alternative verification method for the tilt trirotor UAV.

The selective elimination of long-lived radioactive actinides from complicated solutions is crucial for pollution management of the environment. Knowledge about the species, structures and interaction mechanism of actinides at solid–water interfaces is helpful to understand and to evaluate physicochemical behavior in the natural environment. In this review, we summarize recent works about the sorption and interaction mechanism of actinides (using U, Np, Pu, Cm and Am as representative actinides) on natural clay minerals and man-made nanomaterials. The species and microstructures of actinides on solid particles were investigated by advanced spectroscopy techniques and computational theoretical calculations. The reduction and solidification of actinides on solid particles is the most effective way to immobilize actinides in the natural environment. The contents of this review may be helpful in evaluating the migration of actinides in near-field nuclear waste repositories and the mobilization properties of radionuclides in the environment.

In this paper, we consider a control problem where a group of fixed-wing unmanned aerial vehicles (UAVs) with uncertain dynamics tracks the target vehicle cooperatively in the case of external disturbance. Based on the Gaussian process regression, a data-driven model is established, whose uniform error is bounded with probability. Then a learning-based consensus protocol for multi-UAVs is designed. The stability of the system is proven via Lyapunov function, and the tracking error is guaranteed to be bounded with a high probability. Finally, the effectiveness of the proposed method is shown in the numerical simulation.

The tilt tri-rotor unmanned aerial vehicle (UAV) has three flight modes: the hover mode, the transition mode, and the fixed-wing mode. Controller design in the hover mode is the premise of realizing stable flight of this kind of UAV. Due to the particular structure with odd rotors and strong nonlinearity, the modeling and control of the tilt tri-rotor UAV remain an active and ongoing research topic. To overcome these problems and achieve stable flight control, this paper proposes a sliding mode-based nonlinear control scheme for the hovering flight of a tilt tri-rotor UAV, consisting of position control, attitude control, and control allocation. First, the mathematical model of the UAV is given by using the Newton–Euler formulation. Second, a cascade flight controller consisting of the position controller and the attitude controller is developed based on sliding mode control (SMC). For the position controller, an auxiliary dynamic system composed of the hyperbolic tangent functions is introduced to the SMC approach for constraining the output magnitude of the thrust and the reference attitudes. Besides, a disturbance observer is applied to the attitude controller to alleviate the chattering and improve robustness. Furthermore, according to the structural characteristics of the tilt tri-rotor UAV, a control allocation algorithm is developed to map the virtual control quantities calculated by the cascade flight controller to the actual actuators. Simulations are conducted to verify the robustness against the external disturbances and parameter variations, and the performance comparisons with two other control schemes are also given. Finally, the experiment is also carried out to validate the performance of the proposed control scheme.

In this article, we study the ground moving target tracking problem for a fixed-wing unmanned aerial vehicle equipped with a radar. This problem is formulated in a partially observable Markov process framework, which contains the following two parts: in the first part, the unmanned aerial vehicle utilizes the measurements from its radar and employs a Kalman filter to estimate the target’s real-time location; in the second part, the unmanned aerial vehicle optimizes its trajectory in a real-time manner so that the radar’s measurements can include more useful information. To solve the trajectory optimization problem, we proposed an information geometry-based partially observable Markov decision process method. Specifically, the cumulative amount of information in the observation is represented by Fisher information of information geometry, and acts as the criterion of the partially observable Markov decision process problem. Furthermore, to guarantee the real-time performance, an important trade-off between the optimality and computation cost is made by an approximate receding horizon approach. Finally, simulation results corroborate the accuracy and time-efficiency of our proposed method and also show our advantage in computation time compared to existing methods.

In order to explore the effect of –OH functional groups in Cs+ adsorption, we herein used the low temperature plasma-induced grafting method to graft chitosan onto carbon nanotubes (denoted as CTS-g-CNTs), as raw-CNTs have few functional groups and chitosan has a large number of –OH functional groups. The synthesized CTS-g-CNT composites were characterized using different techniques. The effect of –OH functional groups in the Cs+ adsorption process was evaluated by comparison of the adsorption properties of raw-CNTs with and without grafting chitosan. The variation of environmental conditions such as pH and contact time was investigated. A comparison of contaminated seawater and simulated groundwater was also evaluated. The results indicated that: (1) the adsorption of Cs+ ions was strongly dependent on pH and the competitive cations; (2) for CNT-based material, the –OH functional groups have a positive effect on Cs+ removal; (3) simulated contaminated groundwater can be used to model contaminated seawater to evaluate the adsorption property of CNTs-based material. These results showed direct observational evidence on the effect of –OH functional groups for Cs+ adsorption. Our findings are important in providing future directions to design and to choose effective material to remedy the removal of radioactive cesium from contaminated groundwater and seawater, crucial for public health and the human social environment.

It is highly desirable to design optical devices with diverse optomechanical functions. Here, we investigate lateral optical force exerted on subwavelength-diameter (SD) optical fibers harnessed by input light modes with different polarizations. It is interesting to find that input light modes of circular or elliptical polarizations would bring about lateral optical force in new directions, which has not been observed in previous studies. By means of finite-difference time-domain (FDTD) simulations, detailed spatial distributions of the asymmetric transverse force density are revealed, meanwhile dependence of optical force on input light polarizations, fiber diameters, and inclination angles of fiber endfaces are all carefully discussed. It is believed that polarization-sensitive reflection, refraction, and diffraction of optical fields occur at the interface, i.e., fiber oblique endfaces, resulting in asymmetrically distributed optical fields and thereafter non-zero transverse optical force. We believe our new findings could be helpful for constructing future steerable optomechanical devices with more flexibility.

This paper focuses on one of the collision avoidance scenarios for unmanned aerial vehicles (UAVs), where the UAV needs to avoid collision with the enemy UAV during its flying path to the goal point. Such a type of problem is defined as the enemy avoidance problem in this paper. To deal with this problem, a learning based framework is proposed. Under this framework, the enemy avoidance problem is formulated as a Markov Decision Process (MDP), and the maneuver policies for the UAV are learned based on a temporal-difference reinforcement learning method called Sarsa. To handle the enemy avoidance problem in continuous state space, the Cerebellar Model Arithmetic Computer (CMAC) function approximation technique is embodied in the proposed framework. Furthermore, a hardware-in-the-loop (HITL) simulation environment is established. Simulation results show that the UAV agent can learn a satisfying policy under the proposed framework. Comparing with the random policy and the fixed-rule policy, the learned policy can achieve a far higher possibility in reaching the goal point without colliding with the enemy UAV.

Transfer Learning (TL) has received a great deal of attention because of its ability to speed up Reinforcement Learning (RL) by reusing learned knowledge from other tasks. This paper proposes a new transfer learning framework, referred to as Transfer Learning via Artificial Neural Network Approximator (TL-ANNA). It builds an Artificial Neural Network (ANN) transfer approximator to transfer the related knowledge from the source task into the target task and reuses the transferred knowledge with a Probabilistic Policy Reuse (PPR) scheme. Specifically, the transfer approximator maps the state of the target task symmetrically to states of the source task with a certain mapping rule, and activates the related knowledge (components of the action-value function) of the source task as the input of the ANNs; it then predicts the quality of the actions in the target task with the ANNs. The target learner uses the PPR scheme to bias the RL with the suggested action from the transfer approximator. In this way, the transfer approximator builds a symmetric knowledge path between the target task and the source task. In addition, two mapping rules for the transfer approximator are designed, namely, Full Mapping Rule and Group Mapping Rule. Experiments performed on the RoboCup soccer Keepaway task verified that the proposed transfer learning methods outperform two other transfer learning methods in both jumpstart and time to threshold metrics and are more robust to the quality of source knowledge. In addition, the TL-ANNA with the group mapping rule exhibits slightly worse performance than the one with the full mapping rule, but with less computation and space cost when appropriate grouping method is used.

Lanthanides/actinides sorption speciation on minerals and oxides by means of time resolved laser fluorescence spectroscopy (TRLFS), extended X-ray absorption fine structure spectroscopy (EXAFS) and density functional theory (DFT) is reviewed in the field of nuclear disposal safety research. The theoretical aspects of the methods are concisely presented. Examples of recent research results of lanthanide/actinide speciation and local atomic structures using TRLFS, EXAFS and DFT are discussed. The interaction of lanthanides/actinides with oxides and minerals as well as their uptake are also of common interest in radionuclide chemistry. Especially the sorption and inclusion of radionuclides into several minerals lead to an improvement in knowledge of minor components in solids. In the solid-liquid interface, the speciation and local atomic structures of Eu(III), Cm(III), U(VI), and Np(IV/VI) in several natural and synthetic minerals and oxides are also reviewed and discussed. The review is important to understand the physicochemical behavior of lanthanides/actinides at a molecular level in the natural environment.