Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements...Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements.Deep reinforcement learning(DRL)exhibits excellent capability of real-time decision-making and adaptability to complex scenarios,and generalization abilities.However,it is arduous to guarantee complete driving safety and efficiency under the constraints of training samples and costs.This paper proposes a Mixture of Expert method(MoE)based on Soft Actor-Critic(SAC),where the upper-level discriminator dynamically decides whether to activate the lower-level DRL expert or the heuristic expert based on the features of the input state.To further enhance the performance of the DRL expert,a buffer zone is introduced in the reward function,preemptively applying penalties before insecure situations occur.In order to minimize collision and off-road rates,the Intelligent Driver Model(IDM)and Minimizing Overall Braking Induced by Lane changes(MOBIL)strategy are designed by heuristic experts.Finally,tested in typical simulation scenarios,MOE shows a 13.75%improvement in driving efficiency compared with the traditional DRL method with continuous action space.It ensures high safety with zero collision and zero off-road rates while maintaining high adaptability.展开更多
Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adja...Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.展开更多
Actor-Critic是一种强化学习方法,通过与环境在线试错交互收集样本来学习策略,是求解序贯感知决策问题的有效手段.但是,这种在线交互的主动学习范式在一些复杂真实环境中收集样本时会带来成本和安全问题离线强化学习作为一种基于数据驱...Actor-Critic是一种强化学习方法,通过与环境在线试错交互收集样本来学习策略,是求解序贯感知决策问题的有效手段.但是,这种在线交互的主动学习范式在一些复杂真实环境中收集样本时会带来成本和安全问题离线强化学习作为一种基于数据驱动的强化学习范式,强调从静态样本数据集中学习策略,与环境无探索交互,为机器人、自动驾驶、健康护理等真实世界部署应用提供了可行的解决方案,是近年来的研究热点.目前,离线强化学习方法存在学习策略和行为策略之间的分布偏移挑战,针对这个挑战,通常采用策略约束或值函数正则化来限制访问数据集分布之外(Out-Of-Distribution,OOD)的动作,从而导致学习性能过于保守,阻碍了值函数网络的泛化和学习策略的性能提升.为此,本文利用不确定性估计和OOD采样来平衡值函数学习的泛化性和保守性,提出一种基于不确定性估计的离线确定型Actor-Critic方法(Offline Deterministic Actor-Critic based on UncertaintyEstimation,ODACUE).首先,针对确定型策略,给出一种Q值函数的不确定性估计算子定义,理论证明了该算子学到的Q值函数是最优Q值函数的一种悲观估计.然后,将不确定性估计算子应用于确定型Actor-Critic框架中,通过对不确定性估计算子进行凸组合构造Critic学习的目标函数.最后,D4RL基准数据集任务上的实验结果表明:相较于对比算法,ODACUE在11个不同质量等级数据集任务中的总体性能提升最低达9.56%,最高达64.92%.此外,参数分析和消融实验进一步验证了ODACUE的稳定性和泛化能力.展开更多
In Software-Defined Networks(SDNs),determining how to efficiently achieve Quality of Service(QoS)-aware routing is challenging but critical for significantly improving the performance of a network,where the metrics of...In Software-Defined Networks(SDNs),determining how to efficiently achieve Quality of Service(QoS)-aware routing is challenging but critical for significantly improving the performance of a network,where the metrics of QoS can be defined as,for example,average latency,packet loss ratio,and throughput.The SDN controller can use network statistics and a Deep Reinforcement Learning(DRL)method to resolve this challenge.In this paper,we formulate dynamic routing in an SDN as a Markov decision process and propose a DRL algorithm called the Asynchronous Advantage Actor-Critic QoS-aware Routing Optimization Mechanism(AQROM)to determine routing strategies that balance the traffic loads in the network.AQROM can improve the QoS of the network and reduce the training time via dynamic routing strategy updates;that is,the reward function can be dynamically and promptly altered based on the optimization objective regardless of the network topology and traffic pattern.AQROM can be considered as one-step optimization and a black-box routing mechanism in high-dimensional input and output sets for both discrete and continuous states,and actions with respect to the operations in the SDN.Extensive simulations were conducted using OMNeT++and the results demonstrated that AQROM 1)achieved much faster and stable convergence than the Deep Deterministic Policy Gradient(DDPG)and Advantage Actor-Critic(A2C),2)incurred a lower packet loss ratio and latency than Open Shortest Path First(OSPF),DDPG,and A2C,and 3)resulted in higher and more stable throughput than OSPF,DDPG,and A2C.展开更多
In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-in...In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.展开更多
(2E,6E)-4-methyl-2,6-bis(pyridin-3-ylmethylene)cyclohexan-1-one(L_(1))and 4-methyl-2,6-bis[(E)-4-(pyridin-4-yl)benzylidene]cyclohexan-1-one(L_(2))were synthesized and combined with isophthalic acid(H_(2)IP),then under...(2E,6E)-4-methyl-2,6-bis(pyridin-3-ylmethylene)cyclohexan-1-one(L_(1))and 4-methyl-2,6-bis[(E)-4-(pyridin-4-yl)benzylidene]cyclohexan-1-one(L_(2))were synthesized and combined with isophthalic acid(H_(2)IP),then under solvothermal conditions,to react with transition metals achieving four novel metal-organic frameworks(MOFs):[Zn(IP)(L_(1))]_(n)(1),{[Cd(IP)(L_(1))]·H_(2)O}_(n)(2),{[Co(IP)(L_(1))]·H_(2)O}_(n)(3),and[Zn(IP)(L_(2))(H_(2)O)]_(n)(4).MOFs 1-4 have been characterized by single-crystal X-ray diffraction,powder X-ray diffraction,thermogravimetry,and elemental analysis.Single-crystal X-ray diffraction shows that MOF 1 crystallizes in the monoclinic crystal system with space group P2_(1)/n,and MOFs 2-4 belong to the triclinic system with the P1 space group.1-3 are 2D sheet structures,2 and 3 have similar structural characters,whereas 4 is a 1D chain structure.Furthermore,1-3 exhibited certain photocatalytic capability in the degradation of rhodamine B(Rh B)and pararosaniline hydrochloride(PH).4could be used as a heterogeneous catalyst for the Knoevenagel reaction starting with benzaldehyde derivative and malononitrile.4 could promote the reaction to achieve corresponding products in moderate yields within 3 h.Moreover,the catalyst exhibited recyclability for up to three cycles without significantly dropping its activity.A mechanism for MOF 4 catalyzed Knoevenagel condensation reaction of aromatic aldehyde and malononitrile has been initially proposed.CCDC:2356488,1;2356497,2;2356499,3;2356498,4.展开更多
Sulfur-doped iron-cobalt tannate nanorods(S-FeCoTA)derived from metal-organic frameworks(MOFs)as electrocatalysts were synthesized via a one-step hydrothermal method.The optimized S-FeCoTA was interlaced by loose nano...Sulfur-doped iron-cobalt tannate nanorods(S-FeCoTA)derived from metal-organic frameworks(MOFs)as electrocatalysts were synthesized via a one-step hydrothermal method.The optimized S-FeCoTA was interlaced by loose nanorods,which had many voids.The S-FeCoTA catalysts exhibited excellent electrochemical oxygen evolution reaction(OER)performance with a low overpotential of 273 mV at 10 mA·cm^(-2)and a small Tafel slope of 36 mV·dec^(-1)in 1 mol·L^(-1)KOH.The potential remained at 1.48 V(vs RHE)at 10 mA·cm^(-2)under continuous testing for 15 h,implying that S-FeCoTA had good stability.The Faraday efficiency of S-FeCoTA was 94%.The outstanding OER activity of S-FeCoTA is attributed to the synergistic effects among S,Fe,and Co,thus promoting electron transfer,reducing the reaction kinetic barrier,and enhancing the OER performance.展开更多
Exploring efficient microwave absorbing materials(MAMs)has gradually become a hot topic in recent years because it is crucial in both civil and military fields.Metal-organic framework(MOF)has great potential due to it...Exploring efficient microwave absorbing materials(MAMs)has gradually become a hot topic in recent years because it is crucial in both civil and military fields.Metal-organic framework(MOF)has great potential due to its unique composition and bonding mode,which has advantages such as large specific surface area,high porosity,adjustable structure,and designable composition.Herein,MOF-derived MAMs are highlighted based on morphology and structure.The synthesis strategies of MOF-derived MAMs of different dimensions are discussed.On this basis,the structure-activity relationships can be deeply explored through the precise control of material structure and property by atomic engineering.Finally,perspectives are given for the existing problems of MOF-derived MAMs,which will open a new horizon and promote the development of MAMs.展开更多
The preparation of carbon-based electromagnetic wave(EMW)absorbers possessing thin matching thickness,wide absorption bandwidth,strong absorption intensity,and low filling ratio remains a huge challenge.Metal-organic ...The preparation of carbon-based electromagnetic wave(EMW)absorbers possessing thin matching thickness,wide absorption bandwidth,strong absorption intensity,and low filling ratio remains a huge challenge.Metal-organic frameworks(MOFs)are ideal self-sacrificing templates for the construction of carbon-based EMW absorbers.In this work,bimetallic FeMn-MOF-derived MnFe_(2)O_(4)/C/graphene composites were fabricated via a two-step route of solvothermal reaction and the following pyrolysis treatment.The results re-veal the evolution of the microscopic morphology of carbon skeletons from loofah-like to octahedral and then to polyhedron and pomegran-ate after the adjustment of the Fe^(3+)to Mn^(2+)molar ratio.Furthermore,at the Fe^(3+)to Mn^(2+)molar ratio of 2:1,the obtained MnFe_(2)O_(4)/C/graphene composite exhibited the highest EMW absorption capacity.Specifically,a minimum reflection loss of-72.7 dB and a max-imum effective absorption bandwidth of 5.1 GHz were achieved at a low filling ratio of 10wt%.In addition,the possible EMW absorp-tion mechanism of MnFe_(2)O_(4)/C/graphene composites was proposed.Therefore,the results of this work will contribute to the construction of broadband and efficient carbon-based EMW absorbers derived from MOFs.展开更多
Covalent organic frameworks(COFs)have great potential as adsorbents due to their customizable functionality,low density and high porosity.However,COFs powder exists with poor processing and recycling performance.Moreo...Covalent organic frameworks(COFs)have great potential as adsorbents due to their customizable functionality,low density and high porosity.However,COFs powder exists with poor processing and recycling performance.Moreover,due to the accumulation of COFs nanoparticles,it is not conducive to the full utilization of their surface functional groups.Currently,the strategy of COFs assembling into aerogel can be a good solution to this problem.Herein,we successfully synthesize composite aerogels(CSR)by in-situ self-assembly of two-dimensional COFs and graphene based on crosslinking of sodium alginate.Sodium alginate in the composite improves the mechanical properties of the aerogel,and graphene provides a template for the in-situ growth of COFs.Impressively,CSR aerogels with different COFs and sizes can be prepared by changing the moiety of the ligand and modulating the addition amount of COFs.The prepared CSR aerogels exhibit porous,low density,good processability and good mechanical properties.Among them,the density of CSR-N-1.6 is only 5 mg/cm3,which is the lowest density among the reported COF aerogels so far.Due to these remarkable properties,CSR aerogels perform excellent adsorption and recycling properties for the efficient and rapid removal of organic pollutants(organic dyes and antibiotics)from polluted water.In addition,it is also possible to visually recognize the presence of antibiotics by fluorescence detection.This work not only provides a new strategy for synthesizing COF aerogels,but also accelerates the practical application of COF aerogels and contributes to environmental remediation.展开更多
Constructing a framework carrier to stabilize protein conformation,induce high embedding efficiency,and acquire low mass-transfer resistance is an urgent issue in the development of immobilized enzymes.Hydrogen-bonded...Constructing a framework carrier to stabilize protein conformation,induce high embedding efficiency,and acquire low mass-transfer resistance is an urgent issue in the development of immobilized enzymes.Hydrogen-bonded organic frameworks(HOFs)have promising application potential for embedding enzymes.In fact,no metal involvement is required,and HOFs exhibit superior biocompatibility,and free access to substrates in mesoporous channels.Herein,a facile in situ growth approach was proposed for the self-assembly of alcohol dehydrogenase encapsulated in HOF.The micron-scale bio-catalytic composite was rapidly synthesized under mild conditions(aqueous phase and ambient temperature)with a controllable embedding rate.The high crystallinity and periodic arrangement channels of HOF were preserved at a high enzyme encapsulation efficiency of 59%.This bio-composite improved the tolerance of the enzyme to the acid-base environment and retained 81%of its initial activity after five cycles of batch hydrogenation involving NADH coenzyme.Based on this controllably synthesized bio-catalytic material and a common lipase,we further developed a two-stage cascade microchemical system and achieved the continuous production of chiral hydroxybutyric acid(R-3-HBA).展开更多
A sp^(2) carbon-conjugated covalent organic framework (BDATN) was modified through γ-ray radiation reduction and subsequent acidification with hydrochloric acid to yield a novel functional COF (named rBDATN-HCl) for ...A sp^(2) carbon-conjugated covalent organic framework (BDATN) was modified through γ-ray radiation reduction and subsequent acidification with hydrochloric acid to yield a novel functional COF (named rBDATN-HCl) for Cr(Ⅵ) removal.The morphology and structure of rBDATN-HCl were analyzed and identified by SEM,FTIR,XRD and solid-state13C NMR.It is found that the active functional groups,such as hydroxyl and amide,were introduced into BDATN after radiation reduction and acidification.The prepared rBDATN-HCl demonstrates a photocatalytic reduction removal rate of Cr(Ⅵ) above 99%after 60min of illumination with a solid-liquid ratio of 0.5 mg/mL,showing outstanding performance,which is attributed to the increase of dispersibility and adsorption sites of r BDATN-HCl.In comparison to the cBDATN-HCl synthesized with chemical reduction,rBDATN-HCl exhibits a better photoreduction performance for Cr(Ⅵ),demonstrating the advantages of radiation preparation of rBDATN-HCl.It is expected that more functionalized sp^(2) carbon-conjugated COFs could be obtained by this radiation-induced reduction strategy.展开更多
基金Supported by National Key R&D Program of China(Grant No.2022YFB2503203)National Natural Science Foundation of China(Grant No.U1964206).
文摘Decision-making of connected and automated vehicles(CAV)includes a sequence of driving maneuvers that improve safety and efficiency,characterized by complex scenarios,strong uncertainty,and high real-time requirements.Deep reinforcement learning(DRL)exhibits excellent capability of real-time decision-making and adaptability to complex scenarios,and generalization abilities.However,it is arduous to guarantee complete driving safety and efficiency under the constraints of training samples and costs.This paper proposes a Mixture of Expert method(MoE)based on Soft Actor-Critic(SAC),where the upper-level discriminator dynamically decides whether to activate the lower-level DRL expert or the heuristic expert based on the features of the input state.To further enhance the performance of the DRL expert,a buffer zone is introduced in the reward function,preemptively applying penalties before insecure situations occur.In order to minimize collision and off-road rates,the Intelligent Driver Model(IDM)and Minimizing Overall Braking Induced by Lane changes(MOBIL)strategy are designed by heuristic experts.Finally,tested in typical simulation scenarios,MOE shows a 13.75%improvement in driving efficiency compared with the traditional DRL method with continuous action space.It ensures high safety with zero collision and zero off-road rates while maintaining high adaptability.
基金supported by National Natural Science Foundation of China(52222215, 52272420, 52072051)。
文摘Parking in a small parking lot within limited space poses a difficult task. It often leads to deviations between the final parking posture and the target posture. These deviations can lead to partial occupancy of adjacent parking lots, which poses a safety threat to vehicles parked in these parking lots. However, previous studies have not addressed this issue. In this paper, we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL) on the automatic ego vehicle(AEV) parking, in terms of safety, comfort, accuracy, and efficiency of parking. A segmented parking training framework(SPTF) based on soft actor-critic(SAC) is proposed to improve parking performance. In the proposed method, the SAC algorithm incorporates strategy entropy into the objective function, to enable the AEV to learn parking strategies based on a more comprehensive understanding of the environment. Additionally, the SPTF simplifies complex parking tasks to maintain the high performance of deep reinforcement learning(DRL). The experimental results reveal that the PDEVNTPL has a detrimental influence on the AEV parking in terms of safety, accuracy, and comfort, leading to reductions of more than 27%, 54%, and 26%respectively. However, the SAC-based SPTF effectively mitigates this impact, resulting in a considerable increase in the parking success rate from 71% to 93%. Furthermore, the heading angle deviation is significantly reduced from 2.25 degrees to 0.43degrees.
文摘Actor-Critic是一种强化学习方法,通过与环境在线试错交互收集样本来学习策略,是求解序贯感知决策问题的有效手段.但是,这种在线交互的主动学习范式在一些复杂真实环境中收集样本时会带来成本和安全问题离线强化学习作为一种基于数据驱动的强化学习范式,强调从静态样本数据集中学习策略,与环境无探索交互,为机器人、自动驾驶、健康护理等真实世界部署应用提供了可行的解决方案,是近年来的研究热点.目前,离线强化学习方法存在学习策略和行为策略之间的分布偏移挑战,针对这个挑战,通常采用策略约束或值函数正则化来限制访问数据集分布之外(Out-Of-Distribution,OOD)的动作,从而导致学习性能过于保守,阻碍了值函数网络的泛化和学习策略的性能提升.为此,本文利用不确定性估计和OOD采样来平衡值函数学习的泛化性和保守性,提出一种基于不确定性估计的离线确定型Actor-Critic方法(Offline Deterministic Actor-Critic based on UncertaintyEstimation,ODACUE).首先,针对确定型策略,给出一种Q值函数的不确定性估计算子定义,理论证明了该算子学到的Q值函数是最优Q值函数的一种悲观估计.然后,将不确定性估计算子应用于确定型Actor-Critic框架中,通过对不确定性估计算子进行凸组合构造Critic学习的目标函数.最后,D4RL基准数据集任务上的实验结果表明:相较于对比算法,ODACUE在11个不同质量等级数据集任务中的总体性能提升最低达9.56%,最高达64.92%.此外,参数分析和消融实验进一步验证了ODACUE的稳定性和泛化能力.
基金fully supported by GUET Excellent Graduate Thesis Program(Grant No.19YJPYBS03)Innovation Project of Guangxi Graduate Education(Grant No.YCBZ2022109)New Technology Research University Cooperation Project of the 34th Research Institute of China Electronics Technology Group Corporation,2021(Grant No.SF2126007)。
文摘In Software-Defined Networks(SDNs),determining how to efficiently achieve Quality of Service(QoS)-aware routing is challenging but critical for significantly improving the performance of a network,where the metrics of QoS can be defined as,for example,average latency,packet loss ratio,and throughput.The SDN controller can use network statistics and a Deep Reinforcement Learning(DRL)method to resolve this challenge.In this paper,we formulate dynamic routing in an SDN as a Markov decision process and propose a DRL algorithm called the Asynchronous Advantage Actor-Critic QoS-aware Routing Optimization Mechanism(AQROM)to determine routing strategies that balance the traffic loads in the network.AQROM can improve the QoS of the network and reduce the training time via dynamic routing strategy updates;that is,the reward function can be dynamically and promptly altered based on the optimization objective regardless of the network topology and traffic pattern.AQROM can be considered as one-step optimization and a black-box routing mechanism in high-dimensional input and output sets for both discrete and continuous states,and actions with respect to the operations in the SDN.Extensive simulations were conducted using OMNeT++and the results demonstrated that AQROM 1)achieved much faster and stable convergence than the Deep Deterministic Policy Gradient(DDPG)and Advantage Actor-Critic(A2C),2)incurred a lower packet loss ratio and latency than Open Shortest Path First(OSPF),DDPG,and A2C,and 3)resulted in higher and more stable throughput than OSPF,DDPG,and A2C.
基金supported by the Sichuan Science and Technology Program(grant number 2022YFG0123).
文摘In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.
文摘(2E,6E)-4-methyl-2,6-bis(pyridin-3-ylmethylene)cyclohexan-1-one(L_(1))and 4-methyl-2,6-bis[(E)-4-(pyridin-4-yl)benzylidene]cyclohexan-1-one(L_(2))were synthesized and combined with isophthalic acid(H_(2)IP),then under solvothermal conditions,to react with transition metals achieving four novel metal-organic frameworks(MOFs):[Zn(IP)(L_(1))]_(n)(1),{[Cd(IP)(L_(1))]·H_(2)O}_(n)(2),{[Co(IP)(L_(1))]·H_(2)O}_(n)(3),and[Zn(IP)(L_(2))(H_(2)O)]_(n)(4).MOFs 1-4 have been characterized by single-crystal X-ray diffraction,powder X-ray diffraction,thermogravimetry,and elemental analysis.Single-crystal X-ray diffraction shows that MOF 1 crystallizes in the monoclinic crystal system with space group P2_(1)/n,and MOFs 2-4 belong to the triclinic system with the P1 space group.1-3 are 2D sheet structures,2 and 3 have similar structural characters,whereas 4 is a 1D chain structure.Furthermore,1-3 exhibited certain photocatalytic capability in the degradation of rhodamine B(Rh B)and pararosaniline hydrochloride(PH).4could be used as a heterogeneous catalyst for the Knoevenagel reaction starting with benzaldehyde derivative and malononitrile.4 could promote the reaction to achieve corresponding products in moderate yields within 3 h.Moreover,the catalyst exhibited recyclability for up to three cycles without significantly dropping its activity.A mechanism for MOF 4 catalyzed Knoevenagel condensation reaction of aromatic aldehyde and malononitrile has been initially proposed.CCDC:2356488,1;2356497,2;2356499,3;2356498,4.
文摘Sulfur-doped iron-cobalt tannate nanorods(S-FeCoTA)derived from metal-organic frameworks(MOFs)as electrocatalysts were synthesized via a one-step hydrothermal method.The optimized S-FeCoTA was interlaced by loose nanorods,which had many voids.The S-FeCoTA catalysts exhibited excellent electrochemical oxygen evolution reaction(OER)performance with a low overpotential of 273 mV at 10 mA·cm^(-2)and a small Tafel slope of 36 mV·dec^(-1)in 1 mol·L^(-1)KOH.The potential remained at 1.48 V(vs RHE)at 10 mA·cm^(-2)under continuous testing for 15 h,implying that S-FeCoTA had good stability.The Faraday efficiency of S-FeCoTA was 94%.The outstanding OER activity of S-FeCoTA is attributed to the synergistic effects among S,Fe,and Co,thus promoting electron transfer,reducing the reaction kinetic barrier,and enhancing the OER performance.
基金supported by the National Natural Science Foundation of China(Nos.52373280,52177014 and 52273257).
文摘Exploring efficient microwave absorbing materials(MAMs)has gradually become a hot topic in recent years because it is crucial in both civil and military fields.Metal-organic framework(MOF)has great potential due to its unique composition and bonding mode,which has advantages such as large specific surface area,high porosity,adjustable structure,and designable composition.Herein,MOF-derived MAMs are highlighted based on morphology and structure.The synthesis strategies of MOF-derived MAMs of different dimensions are discussed.On this basis,the structure-activity relationships can be deeply explored through the precise control of material structure and property by atomic engineering.Finally,perspectives are given for the existing problems of MOF-derived MAMs,which will open a new horizon and promote the development of MAMs.
基金supported by the Natural Science Research Project of the Anhui Educational Committee,China(No.2022AH050827)the Open Research Fund Program of Anhui Province Key Laboratory of Specialty Polymers,Anhui University of Science and Technology,China(No.AHKLSP23-12)the Joint National-Local Engineering Research Center for Safe and Precise Coal Mining Fund,China(No.EC2022020)。
文摘The preparation of carbon-based electromagnetic wave(EMW)absorbers possessing thin matching thickness,wide absorption bandwidth,strong absorption intensity,and low filling ratio remains a huge challenge.Metal-organic frameworks(MOFs)are ideal self-sacrificing templates for the construction of carbon-based EMW absorbers.In this work,bimetallic FeMn-MOF-derived MnFe_(2)O_(4)/C/graphene composites were fabricated via a two-step route of solvothermal reaction and the following pyrolysis treatment.The results re-veal the evolution of the microscopic morphology of carbon skeletons from loofah-like to octahedral and then to polyhedron and pomegran-ate after the adjustment of the Fe^(3+)to Mn^(2+)molar ratio.Furthermore,at the Fe^(3+)to Mn^(2+)molar ratio of 2:1,the obtained MnFe_(2)O_(4)/C/graphene composite exhibited the highest EMW absorption capacity.Specifically,a minimum reflection loss of-72.7 dB and a max-imum effective absorption bandwidth of 5.1 GHz were achieved at a low filling ratio of 10wt%.In addition,the possible EMW absorp-tion mechanism of MnFe_(2)O_(4)/C/graphene composites was proposed.Therefore,the results of this work will contribute to the construction of broadband and efficient carbon-based EMW absorbers derived from MOFs.
基金the financial support provided by the National Natural Science Foundation of China(Nos.22175094,21971113)。
文摘Covalent organic frameworks(COFs)have great potential as adsorbents due to their customizable functionality,low density and high porosity.However,COFs powder exists with poor processing and recycling performance.Moreover,due to the accumulation of COFs nanoparticles,it is not conducive to the full utilization of their surface functional groups.Currently,the strategy of COFs assembling into aerogel can be a good solution to this problem.Herein,we successfully synthesize composite aerogels(CSR)by in-situ self-assembly of two-dimensional COFs and graphene based on crosslinking of sodium alginate.Sodium alginate in the composite improves the mechanical properties of the aerogel,and graphene provides a template for the in-situ growth of COFs.Impressively,CSR aerogels with different COFs and sizes can be prepared by changing the moiety of the ligand and modulating the addition amount of COFs.The prepared CSR aerogels exhibit porous,low density,good processability and good mechanical properties.Among them,the density of CSR-N-1.6 is only 5 mg/cm3,which is the lowest density among the reported COF aerogels so far.Due to these remarkable properties,CSR aerogels perform excellent adsorption and recycling properties for the efficient and rapid removal of organic pollutants(organic dyes and antibiotics)from polluted water.In addition,it is also possible to visually recognize the presence of antibiotics by fluorescence detection.This work not only provides a new strategy for synthesizing COF aerogels,but also accelerates the practical application of COF aerogels and contributes to environmental remediation.
基金supported by the National Key Research and Development Program of China(2019YFA0905100)the National Natural Science Foundation of China(21991102,22378227).
文摘Constructing a framework carrier to stabilize protein conformation,induce high embedding efficiency,and acquire low mass-transfer resistance is an urgent issue in the development of immobilized enzymes.Hydrogen-bonded organic frameworks(HOFs)have promising application potential for embedding enzymes.In fact,no metal involvement is required,and HOFs exhibit superior biocompatibility,and free access to substrates in mesoporous channels.Herein,a facile in situ growth approach was proposed for the self-assembly of alcohol dehydrogenase encapsulated in HOF.The micron-scale bio-catalytic composite was rapidly synthesized under mild conditions(aqueous phase and ambient temperature)with a controllable embedding rate.The high crystallinity and periodic arrangement channels of HOF were preserved at a high enzyme encapsulation efficiency of 59%.This bio-composite improved the tolerance of the enzyme to the acid-base environment and retained 81%of its initial activity after five cycles of batch hydrogenation involving NADH coenzyme.Based on this controllably synthesized bio-catalytic material and a common lipase,we further developed a two-stage cascade microchemical system and achieved the continuous production of chiral hydroxybutyric acid(R-3-HBA).
基金supported by the National Natural Science Foundation of China(No.U2067212)the National Science Fund for Distinguished Young Scholars(No.21925603).
文摘A sp^(2) carbon-conjugated covalent organic framework (BDATN) was modified through γ-ray radiation reduction and subsequent acidification with hydrochloric acid to yield a novel functional COF (named rBDATN-HCl) for Cr(Ⅵ) removal.The morphology and structure of rBDATN-HCl were analyzed and identified by SEM,FTIR,XRD and solid-state13C NMR.It is found that the active functional groups,such as hydroxyl and amide,were introduced into BDATN after radiation reduction and acidification.The prepared rBDATN-HCl demonstrates a photocatalytic reduction removal rate of Cr(Ⅵ) above 99%after 60min of illumination with a solid-liquid ratio of 0.5 mg/mL,showing outstanding performance,which is attributed to the increase of dispersibility and adsorption sites of r BDATN-HCl.In comparison to the cBDATN-HCl synthesized with chemical reduction,rBDATN-HCl exhibits a better photoreduction performance for Cr(Ⅵ),demonstrating the advantages of radiation preparation of rBDATN-HCl.It is expected that more functionalized sp^(2) carbon-conjugated COFs could be obtained by this radiation-induced reduction strategy.
