The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches...The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks.This research proposes molecular properties prediction with parallel-view and collaborative learning(MolP-PC),a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints(MFs),2D molecular graphs,and 3D geometric representations,incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions.Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks,with its multi-task learning(MTL)mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks.Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization.A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC’s effective generalization in predicting key pharmacokinetic parameters such as half-life(T0.5)and clearance(CL),indicating its practical utility in drug modeling.However,the model exhibits a tendency to underestimate volume of distribution(VD),indicating potential for improvement in analyzing compounds with high tissue distribution.This study presents an efficient and interpretable approach for ADMET property prediction,establishing a novel framework for molecular optimization and risk assessment in drug development.展开更多
Entity linking(EL)plays a crucial role in natural language processing(NLP)NLP tasks by linking ambiguous entity mentions to relevant entities in a knowledge base.Due to the inconsistency in data distribution across di...Entity linking(EL)plays a crucial role in natural language processing(NLP)NLP tasks by linking ambiguous entity mentions to relevant entities in a knowledge base.Due to the inconsistency in data distribution across diverse domains,it is difficult to accurately estimate the overall data distribution of the target domain,resulting in the zero-shot scenarios with a significant decrease in generalization performance.Currently,existing works primarily focus on sampling and incorporating fine-grained information to deal with above issue.Unfortunately,they may face either significant computational cost of negative samples for sampling strategy,or shortcomings in interaction between coarse and fine-grained information.To tackle these challenges,in this paper,we propose a Multi-Task Framework with Anchor Point Sampling(MAPS).Specifically,for the anchor point sampling(APS)part,with considering fine-grained information,we pre-bind mention-entity pairs based on prior conditions(e.g.,entity type)to introduce challenging negative samples and modifies the conditional distribution.In this way,the optimal trade-off between computational effectiveness and efficiency will be reached.Moreover,we propose a novel multi-task framework that shares coarse-grained information at a lower level,and utilizes multiple extractors to extract fine-grained information at a higher level.By combining the multi-task framework and various APS approaches,comprehensive fusion of coarse and fine-grained information will be finally achieved.Experimental results on the benchmark dataset ZESHEL demonstrate that MAPS significantly outperforms the competitive baselines.展开更多
In this study,a nickel-based MOF{(NH_(2)(CH_(3))_(2))_(2)[Ni_(3)(O)(L)3(NH(CH_(3))_(2))_(3)]}_(n)(Ni_(3)-MOF),with pore sizes of approximately 1.6 nm×1.6 nm,was synthesized by reacting 4,4′-biphenyldicarboxylic ...In this study,a nickel-based MOF{(NH_(2)(CH_(3))_(2))_(2)[Ni_(3)(O)(L)3(NH(CH_(3))_(2))_(3)]}_(n)(Ni_(3)-MOF),with pore sizes of approximately 1.6 nm×1.6 nm,was synthesized by reacting 4,4′-biphenyldicarboxylic acid(H_(2)L)with Ni(NO_(3))_(2)·6H_(2)O in an N,N-dimethylformamide(DMF)solution.The nanoscale adsorbent Ni_(3)-MOF-N with a particle diameter of approximately 200 nm was prepared using Ni_(3)-MOF.It exhibited a maximum equilibrium tetracycline(TC)adsorption capacity of 358.2 mg·g^(-1)at its isoelectric point(pH=6.50),outperforming most reported MOF-based adsorbents.This exceptional performance is likely attributed to the well-matched pore size of Ni_(3)-MOF-N(1.6 nm×1.6 nm)and the molecular dimensions of TC(0.8 nm×1.2 nm),combined with the presence of partial Ni(Ⅱ)sites on the surface of Ni_(3)-MOF-N.These features collectively facilitate effective TC adsorption through a combination of pore filling,electrostatic attraction,hydrogen bonding,surface complexation,andπ-πinteractions.Recycling experiments demonstrated that Ni_(3)-MOF-N possesses excellent structural stability and consistent adsorption performance.CCDC:2481791,Ni_(3)-MOF.展开更多
A metal-organic framework{[Zn(L)_(0.5)(1,2,4,5-tpb)_(0.5)]·DMF·3H_(2)O}_(n)(1)was synthesized by solvothermal reaction,where H4L=5,5'-(ethane-1,2-diyl)diisophthalic acid,and 1,2,4,5-tpb=1,2,4,5-tetra(pyr...A metal-organic framework{[Zn(L)_(0.5)(1,2,4,5-tpb)_(0.5)]·DMF·3H_(2)O}_(n)(1)was synthesized by solvothermal reaction,where H4L=5,5'-(ethane-1,2-diyl)diisophthalic acid,and 1,2,4,5-tpb=1,2,4,5-tetra(pyridin-4-yl)benzene.The analysis of the single crystal structure indicates that L^(4-)and 1,2,4,5-tpb are connected with Zn(Ⅱ)to form a 2D layered structure,and the layers are linked by 1,2,4,5-tpb to form a 3D structure.1 can be used as a highly selective fluorescent probe for the detection of 2,4-dinitrophenylhydrazine(DNP)and tetracycline(TET),and the detection limits were 0.013 and 0.31μmol·L^(-1),respectively.1 was applied successfully to the determination of TET content in the Yanhe River water sample.CCDC:2466221.展开更多
In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detec...In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detection of Vibrio parahaemolyticus.The nanozyme integrated magnetic separation,peroxidase-like catalytic activity,and specific target recognition through an aptamer-based strategy.Upon binding to V.parahaemolyticus,the catalytic oxidation of tetra-aminophenylethylene(TPE-4A)by the nanozyme was selectively inhibited,resulting in distinct colorimetric and fluorescent signals that significantly enhanced the detection accuracy and reliability.The proposed method exhibited high sensitivity,with limits of detection(LOD)of 21 and 7 CFU/mL for the colorimetric and fluorescent assays,respectively.The performance of this method was validated using real seafood samples,including Penaeus vannamei,Mytilus coruscus,and Crassostrea gigas,which showed high recovery rates(101.11%-107.30%)and excellent reproducibility.The system also demonstrated strong specificity and accuracy under various conditions,confirming its robustness and practical applicability.Collectively,this innovative platform presents a promising solution for the rapid,versatile,and sensitive detection of V.parahaemolyticus in seafood,with considerable potential to advance food safety diagnosis and on-site monitoring.展开更多
Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully construct...Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully constructed by coordinatively assembling the semi-rigid multidentate ligand 5-(1-carboxyethoxy)isophthalic acid(H₃CIA)with the Nheterocyclic ligands 1,4-di(4H-1,2,4-triazol-4-yl)benzene(1,4-dtb)and 1,4-di(1H-imidazol-1-yl)benzene(1,4-dib),respectively,around Co^(2+)ions.Single-crystal X-ray diffraction analysis revealed that in both complexes HU23 and HU24,the CIA^(3-)anions adopt aκ^(7)-coordination mode,bridging six Co^(2+)ions via their five carboxylate oxygen atoms and one ether oxygen atom.This linkage forms tetranuclear[Co4(μ3-OH)2]^(6+)units.These Co-oxo cluster units were interconnected by CIA^(3-)anions to assemble into 2D kgd-type structures featuring a 3,6-connected topology.The 2D layers were further connected by 1,4-dtb and 1,4-dib,resulting in 3D pillar-layered frameworks for HU23 and HU24.Notably,despite the similar configurations of 1,4-dtb and 1,4-dib,differences in their coordination spatial orientations lead to topological divergence in the 3D frameworks of HU23 and HU24.Topological analysis indicates that the frameworks of HU23 and HU24 can be simplified into a 3,10-connected net(point symbol:(4^(10).6^(3).8^(2))(4^(3))_(2))and a 3,8-connected tfz-d net(point symbol:(4^(3))_(2)((4^(6).6^(18).8^(4)))),respectively.This structural differentiation confirms the precise regulatory role of ligands on the topology of metal-organic frameworks.Moreover,the ultraviolet-visible absorption spectra confirmed that HU23 and HU24 have strong absorption capabilities for ultraviolet and visible light.According to the Kubelka-Munk method,their bandwidths were 2.15 and 2.08 eV,respectively,which are consistent with those of typical semiconductor materials.Variable-temperature magnetic susceptibility measurements(2-300 K)revealed significant antiferromagnetic coupling in both complexes,with their effective magnetic moments decreasing markedly as the temperature lowered.CCDC:2457554,HU23;2457553,HU24.展开更多
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id...Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.展开更多
Knowledge distillation has become a standard technique for compressing large language models into efficient student models,but existing methods often struggle to balance prediction accuracy with explanation quality.Re...Knowledge distillation has become a standard technique for compressing large language models into efficient student models,but existing methods often struggle to balance prediction accuracy with explanation quality.Recent approaches such as Distilling Step-by-Step(DSbS)introduce explanation supervision,yet they apply it in a uniform manner that may not fully exploit the different learning dynamics of prediction and explanation.In this work,we propose a task-structured curriculum learning(TSCL)framework that structures training into three sequential phases:(i)prediction-only,to establish stable feature representations;(ii)joint prediction-explanation,to align task outputs with rationale generation;and(iii)explanation-only,to refine the quality of rationales.This design provides a simple but effective modification to DSbS,requiring no architectural changes and adding negligible training cost.We justify the phase scheduling with ablation studies and convergence analysis,showing that an initial prediction-heavy stage followed by a balanced joint phase improves both stability and explanation alignment.Extensive experiments on five datasets(e-SNLI,ANLI,CommonsenseQA,SVAMP,and MedNLI)demonstrate that TSCL consistently outperforms strong baselines,achieving gains of+1.7-2.6 points in accuracy and 0.8-1.2 in ROUGE-L,corresponding to relative error reductions of up to 21%.Beyond lexical metrics,human evaluation and ERASERstyle faithfulness diagnostics confirm that TSCL produces more faithful and informative explanations.Comparative training curves further reveal faster convergence and lower variance across seeds.Efficiency analysis shows less than 3%overhead in wall-clock training time and no additional inference cost,making the approach practical for realworld deployment.This study demonstrates that a simple task-structured curriculum can significantly improve the effectiveness of knowledge distillation.By separating and sequencing objectives,TSCL achieves a better balance between accuracy,stability,and explanation quality.The framework generalizes across domains,including medical NLI,and offers a principled recipe for future applications in multimodal reasoning and reinforcement learning.展开更多
Reconfigurable intelligent surface(RIS)have been cast as a promising alternative to alleviate blockage vulnerability and enhance coverage capability for terahertz(THz)communications.Owing to large-scale array elements...Reconfigurable intelligent surface(RIS)have been cast as a promising alternative to alleviate blockage vulnerability and enhance coverage capability for terahertz(THz)communications.Owing to large-scale array elements at transceivers and RIS,the codebook based beamforming can be utilized in a computationally efficient manner.However,the codeword selection for analog beamforming is an intractable combinatorial optimization(CO)problem.To this end,by taking the CO problem as a classification problem,a multi-task learning based analog beam selection(MTL-ABS)framework is developed to implement cooperative beam selection concurrently at transceivers and RIS.In addition,residual network and self-attention mechanism are used to combat the network degradation and mine intrinsic THz channel features.Finally,the network convergence is analyzed from a blockwise perspective,and numerical results demonstrate that the MTL-ABS framework greatly decreases the beam selection overhead and achieves near optimal sum-rate compared with heuristic search based counterparts.展开更多
Silicon possesses a high theoretical capacity,making it a potential contender for lithium-ion battery(LIB)anodes.Nonetheless,its practical usage is challenged by low electrical conductivity and significant volume expa...Silicon possesses a high theoretical capacity,making it a potential contender for lithium-ion battery(LIB)anodes.Nonetheless,its practical usage is challenged by low electrical conductivity and significant volume expansion during cycling.Here,we synthesized a novel silicon/carbon(Si/C)anode doped with ZnO via a template-derived method and high-temperature carbonization.The carbon structure,originated from metal-organic frameworks(MOFs)and ZnO doping,substantially enhanced the electrochemical properties of the composite material.It exhibited an initial capacity of 2100.3 mA h g^(-1)at a current density of 0.2 A g^(-1)and demonstrated excellent capacity retention over successive cycles.Moreover,the composite material displayed superior rate performance at higher current densities of 2 A g^(-1)and 3 A g^(-1).To address the low initial Coulombic efficiency(ICE)of siliconbased materials,we adopted a direct contact prelithiation approach and optimized the lithiation process by controlling the prelithiation time.After 30 min of prelithiation,the ICE reached 97.9%,thereby reducing the initial irreversible capacity loss(ICL)and realizing stable discharge-charge in subsequent cycles.This rational design provides valuable insights for achieving high-performance silicon anode.展开更多
High-sensitive quantitative determination of alpha-fetoprotein(AFP)is of crucial importance for early clinical diagnosis of cancers.Herein,an AuNPs-free electrochemical immunosensor(Ab1-Fc-COF)was prepared from a carb...High-sensitive quantitative determination of alpha-fetoprotein(AFP)is of crucial importance for early clinical diagnosis of cancers.Herein,an AuNPs-free electrochemical immunosensor(Ab1-Fc-COF)was prepared from a carboxylic group enriched COF by post-functionalization with detecting antibody(Ab1)and ferrocene(Fc),and used for electrochemical detection of AFP.Due to the small,homogeneous pore size of the COF,Ab1 with a big size was immobilized on the surface of the COF,while Fc with a small size was covalently modified both on the surface and in the pores of COF.The covalently immobilized Ab1 was quite stable and beneficial to specifically detect AFP biomarkers.Meanwhile,the enriched Fc molecules not only improved the conductivity of the COF,but also effectively transferred and amplified the electrochemical signal.This proposed immunosensor exhibited high sensitivity in detecting AFP with a detection limit of 0.39 pg/mL(S/N of 3:1)and a wide linear response range spanning from 1 pg/mL to 100 ng/mL when plotted against logarithmic concentrations.Furthermore,this immunosensor showed excellent selectivity,stability and reproducibility in the testing of real samples.This study presents an innovative prototype for construction of a precious metal-free,antibody-directly-immobilized,simple and stable electrochemical immunoprobe.展开更多
Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based...Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.展开更多
Accelerating the development of new quality productive forces(NQPF),with innovation at its core,has become essential for firm growth in the new era.Drawing on financial data from China's A-share listed companies s...Accelerating the development of new quality productive forces(NQPF),with innovation at its core,has become essential for firm growth in the new era.Drawing on financial data from China's A-share listed companies spanning the period 2010–2023,this study empirically investigates the impact of entrepreneurial spirit on firm-level NQPF.The results indicate that entrepreneurial spirit significantly promotes firm-level NQPF.Mechanism analysis indicates that entrepreneurial effort—underpinned by technological capital accumulation,effective incentive and constraint mechanisms,and a competitive market environment—plays a mediating role in this relationship.Further heterogeneity analysis reveals that,amid China's economic transition,the positive effects of entrepreneurial spirit are more pronounced in non-state-owned enterprises,high-tech firms,and newly established firms.Accordingly,systematic efforts should be pursued across the technological,organizational,and environmental(TOE)dimensions to optimize the cultivation of entrepreneurial spirit.In particular,greater emphasis should be placed on productive entrepreneurial spirit and the constructive role of entrepreneurial effort,so as to fully leverage their contribution to the advancement of firm-level NQPF.展开更多
Gas sensors are valuable tools for human applications,and extensive research has been conducted in this field.However,practical implementation has yet to be fully realized.In response,efforts have been made to explore...Gas sensors are valuable tools for human applications,and extensive research has been conducted in this field.However,practical implementation has yet to be fully realized.In response,efforts have been made to explore metal-organic frameworks(MOFs),a novel class of porous materials,as potential solutions.MOFs exhibit exceptional porosity and highly tunable chemical compositions and structures,giving rise to a wide range of unique physical and chemical properties.Significant progress has been achieved in developing MOF-based gas sensors,improving sensing performance for various gases.This review aims to provide a comprehensive understanding of MOF-based gas sensors,even for readers unfamiliar with MOFs and gas sensors.It covers the working principles of these sensors,fundamental concepts of MOFs,strategies for tuning MOF properties,fabrication techniques for MOF films,and recent studies on MOF and MOF-derivative gas sensors.Finally,current challenges,overlooked aspects,and future directions for fully exploiting the potential of MOFs in gas sensor development are discussed.展开更多
Lead-halide perovskite solar cells(PSCs)have rapidly achieved certified efficiencies>27%,rivaling silicon photovoltaics.However,their commercialization is hindered by intrinsic material challenges:poor operational ...Lead-halide perovskite solar cells(PSCs)have rapidly achieved certified efficiencies>27%,rivaling silicon photovoltaics.However,their commercialization is hindered by intrinsic material challenges:poor operational stability under moisture,heat,and light;toxic lead leakage from degraded films.Metal-organic frameworks(MOFs),with their unique framework structure,large specific surface area,high heavy metal capturing capacity,and tunable conductivity,offer promising solutions to these issues.Recent studies have integrated MOFs into PSCs architectures to enhance performance and durability.This comprehensive review begins with an in-depth discussion of the structure,optical properties,electrical characteristics,and stability of MOFs,as well as their theoretical compatibility with perovskites.Subsequently,it provides a detailed analysis of how MOFs enhance charge carrier transport,promote perovskite crystallinity,improve device stability,and suppress lead leakage in PSCs.In summary,this review examines the research progress and potential of integrating MOFs with perovskites to address the critical PSCs challenges of efficiency,instability,and toxicity.展开更多
Photocatalytic carbon dioxide reduction reaction(CO_(2)RR)is a carbon-neutral strategy to address global energy use and its impact on climate.Metal oxide and metal chalcogenide catalysts are the most investigated cata...Photocatalytic carbon dioxide reduction reaction(CO_(2)RR)is a carbon-neutral strategy to address global energy use and its impact on climate.Metal oxide and metal chalcogenide catalysts are the most investigated catalysts for photocatalytic CO_(2)RR.Unfortunately,low CO_(2)adsorption ability and limited active sites of metal oxide and metal chalcogenide catalysts for CO_(2)RR make them less competitive compared to their industrial counterparts.Inspired by applications of porphyrin-based metal-organic framework(MOF)catalysts for hydrogen evolution and photodynamic therapy,the investigations of these porphyrin-based MOFs,including pristine and composite porphyrin-based MOFs in photocatalytic CO_(2)RR,have attracted significant attention in the last five years due to their excellent CO_(2)adsorption capacities,high porosity,high stability,exceptional optoelectronic properties,and multi-functionality.However,due to the difference in photocatalytic CO_(2)RR,several critical issues need to be addressed to achieve the rational design of advanced porphyrin-based MOF photocatalysts to improve activity,selectivity,and stability for CO_(2)RR.Here,we review recent developments in the field of porphyrin-based MOF CO_(2)RR photocatalysts,along with critical issues,challenges,and perspectives concerning porphyrin-based MOF catalysts for photocatalytic CO_(2)RR.展开更多
Metal-organic frameworks(MOFs)with high porosity,specific surface area,and unique topologies are highly regarded for their applications in photocatalysis,medical treatment,and environmental pollutant degradation.Howev...Metal-organic frameworks(MOFs)with high porosity,specific surface area,and unique topologies are highly regarded for their applications in photocatalysis,medical treatment,and environmental pollutant degradation.However,due to the limitations of the tumor microenvironment(TME),traditional MOFs have limited efficacy in this environment.This paper designs multi-metal oxide-based heterostructure POMOFs nanoreactors with a nesting doll-like structure.This new structure not only exhibits therapeutic effects in TME but also utilizes ultrasound(US)to enhance the release of reactive oxygen species(ROS)for CDT&SDT co-therapy,becoming an effective sound sensitizer for destroying tumor cells.In summary,our study proposes an idea for constructing multi-metal oxide-based heterostructure MOFs nanoreactors material with a nesting doll-like structure to enhance ROS release and synergistically treat tumor diseases.展开更多
The recovery of precious metals(PMs)from secondary resources is critical for addressing global supply-chain vulnerabilities and sustainable resource utilization.This review systematically examines the transformative p...The recovery of precious metals(PMs)from secondary resources is critical for addressing global supply-chain vulnerabilities and sustainable resource utilization.This review systematically examines the transformative potential of metal-organic frameworks(MOFs)as next-generation adsorbents for PM recovery,focusing on their synthesis,functionalization,and multiscale adsorption mechanisms.We critically analyze conventional pyrometallurgical and hydrometallurgical methods and highlight their limitations in terms of selectivity,energy consumption,and secondary pollution.In contrast,MOFs offer tunable porosity,abundant active sites,and tunable surface chemistry,enabling efficient PM capture via synergistic physical and chemical adsorption.Advanced modification techniques,including direct synthesis and post-synthetic modification,are reviewed to propose strategies for enhancing the adsorption kinetics and selectivity for Au,Ag,Pt,and Pd.Key structure-property relationships are established through multiscale characterization and thermodynamic models,revealing the critical roles of hierarchical porosity,soft donor atoms,and framework stability.Industrial challenges,such as aqueous stability and scalability,are addressed via Zr-O bond strengthening,hydrophobic functionalization,and support immobilization.This study consolidates the experimental and theoretical advances in MOF-based PM recovery and provides a roadmap for translating laboratory innovations into practical applications within the circular-economy framework.展开更多
Polyimide-linkage covalent organic frameworks(PI-COFs),as a subclass of the COFs material family,featuring the unique combination of excellent thermal stability of polyimide,tunable pore sizes,as well as high crystall...Polyimide-linkage covalent organic frameworks(PI-COFs),as a subclass of the COFs material family,featuring the unique combination of excellent thermal stability of polyimide,tunable pore sizes,as well as high crystallinity and surface area of COFs,are expected to be a novel type of promising crystalline porous material with potential applications in adsorption and separation,catalysis,chemical sensing,and energy storage.Therefore,it is increasingly important to summarize polyimide-linkage in COFs and related applications and provide in-depth insight to accelerate future development.In this review,we offer a comprehensive overview of recent advancements in PI-COFs,emphasizing their synthesis methods,design principles and applications.Finally,our brief outlooks on the current challenges and future developments of PI-COFs are provided.Overall,this review aims to guide the recent and future development of PI-COFs.展开更多
Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fund...Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fundamental trade-off between haze and transparency,coupled with impractical thicknesses(≥1 mm).Inspired by squid’s skin-peeling mechanism,this work develops a peroxyformic acid(HCOOOH)-enabled precision peeling strategy to isolate intact 10-μm-thick bamboo green(BG)frameworks—100×thinner than wood-based counterparts while achieving an unprecedented optical performance(88%haze with 80%transparency).This performance surpasses delignified biomass(transparency<40%at 1 mm)and matches engineered cellulose composites,yet requires no energy-intensive nanofibrillation.The preserved native cellulose I crystalline structure(64.76%crystallinity)and wax-coated uniaxial fibril alignment(Hermans factor:0.23)contribute to high mechanical strength(903 MPa modulus)and broadband light scattering.As a light-management layer in polycrystalline silicon solar cells,the BG framework boosts photoelectric conversion efficiency by 0.41%absolute(18.74%→19.15%),outperforming synthetic anti-reflective coatings.The work establishes a scalable,waste-to-wealth route for optical-grade cellulose materials in next-generation optoelectronics.展开更多
基金supported by the research on key technologies for monitoring and identifying drug abuse of anesthetic drugs and psychotropic drugs,and intervention for addiction(No.2023YFC3304200)the program of a study on the diagnosis of addiction to synthetic cannabinoids and methods of assessing the risk of abuse(No.2022YFC3300905)+1 种基金the program of Ab initio design and generation of AI models for small molecule ligands based on target structures(No.2022PE0AC03)ZHIJIANG LAB.
文摘The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks.This research proposes molecular properties prediction with parallel-view and collaborative learning(MolP-PC),a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints(MFs),2D molecular graphs,and 3D geometric representations,incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions.Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks,with its multi-task learning(MTL)mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks.Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization.A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC’s effective generalization in predicting key pharmacokinetic parameters such as half-life(T0.5)and clearance(CL),indicating its practical utility in drug modeling.However,the model exhibits a tendency to underestimate volume of distribution(VD),indicating potential for improvement in analyzing compounds with high tissue distribution.This study presents an efficient and interpretable approach for ADMET property prediction,establishing a novel framework for molecular optimization and risk assessment in drug development.
基金supported in part by the grants from National Natural Science Foundation of China(No.62222213,U22B2059,62072423)the USTC Research Funds of the Double First-Class Initiative(No.YD2150002009).
文摘Entity linking(EL)plays a crucial role in natural language processing(NLP)NLP tasks by linking ambiguous entity mentions to relevant entities in a knowledge base.Due to the inconsistency in data distribution across diverse domains,it is difficult to accurately estimate the overall data distribution of the target domain,resulting in the zero-shot scenarios with a significant decrease in generalization performance.Currently,existing works primarily focus on sampling and incorporating fine-grained information to deal with above issue.Unfortunately,they may face either significant computational cost of negative samples for sampling strategy,or shortcomings in interaction between coarse and fine-grained information.To tackle these challenges,in this paper,we propose a Multi-Task Framework with Anchor Point Sampling(MAPS).Specifically,for the anchor point sampling(APS)part,with considering fine-grained information,we pre-bind mention-entity pairs based on prior conditions(e.g.,entity type)to introduce challenging negative samples and modifies the conditional distribution.In this way,the optimal trade-off between computational effectiveness and efficiency will be reached.Moreover,we propose a novel multi-task framework that shares coarse-grained information at a lower level,and utilizes multiple extractors to extract fine-grained information at a higher level.By combining the multi-task framework and various APS approaches,comprehensive fusion of coarse and fine-grained information will be finally achieved.Experimental results on the benchmark dataset ZESHEL demonstrate that MAPS significantly outperforms the competitive baselines.
文摘In this study,a nickel-based MOF{(NH_(2)(CH_(3))_(2))_(2)[Ni_(3)(O)(L)3(NH(CH_(3))_(2))_(3)]}_(n)(Ni_(3)-MOF),with pore sizes of approximately 1.6 nm×1.6 nm,was synthesized by reacting 4,4′-biphenyldicarboxylic acid(H_(2)L)with Ni(NO_(3))_(2)·6H_(2)O in an N,N-dimethylformamide(DMF)solution.The nanoscale adsorbent Ni_(3)-MOF-N with a particle diameter of approximately 200 nm was prepared using Ni_(3)-MOF.It exhibited a maximum equilibrium tetracycline(TC)adsorption capacity of 358.2 mg·g^(-1)at its isoelectric point(pH=6.50),outperforming most reported MOF-based adsorbents.This exceptional performance is likely attributed to the well-matched pore size of Ni_(3)-MOF-N(1.6 nm×1.6 nm)and the molecular dimensions of TC(0.8 nm×1.2 nm),combined with the presence of partial Ni(Ⅱ)sites on the surface of Ni_(3)-MOF-N.These features collectively facilitate effective TC adsorption through a combination of pore filling,electrostatic attraction,hydrogen bonding,surface complexation,andπ-πinteractions.Recycling experiments demonstrated that Ni_(3)-MOF-N possesses excellent structural stability and consistent adsorption performance.CCDC:2481791,Ni_(3)-MOF.
文摘A metal-organic framework{[Zn(L)_(0.5)(1,2,4,5-tpb)_(0.5)]·DMF·3H_(2)O}_(n)(1)was synthesized by solvothermal reaction,where H4L=5,5'-(ethane-1,2-diyl)diisophthalic acid,and 1,2,4,5-tpb=1,2,4,5-tetra(pyridin-4-yl)benzene.The analysis of the single crystal structure indicates that L^(4-)and 1,2,4,5-tpb are connected with Zn(Ⅱ)to form a 2D layered structure,and the layers are linked by 1,2,4,5-tpb to form a 3D structure.1 can be used as a highly selective fluorescent probe for the detection of 2,4-dinitrophenylhydrazine(DNP)and tetracycline(TET),and the detection limits were 0.013 and 0.31μmol·L^(-1),respectively.1 was applied successfully to the determination of TET content in the Yanhe River water sample.CCDC:2466221.
文摘In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detection of Vibrio parahaemolyticus.The nanozyme integrated magnetic separation,peroxidase-like catalytic activity,and specific target recognition through an aptamer-based strategy.Upon binding to V.parahaemolyticus,the catalytic oxidation of tetra-aminophenylethylene(TPE-4A)by the nanozyme was selectively inhibited,resulting in distinct colorimetric and fluorescent signals that significantly enhanced the detection accuracy and reliability.The proposed method exhibited high sensitivity,with limits of detection(LOD)of 21 and 7 CFU/mL for the colorimetric and fluorescent assays,respectively.The performance of this method was validated using real seafood samples,including Penaeus vannamei,Mytilus coruscus,and Crassostrea gigas,which showed high recovery rates(101.11%-107.30%)and excellent reproducibility.The system also demonstrated strong specificity and accuracy under various conditions,confirming its robustness and practical applicability.Collectively,this innovative platform presents a promising solution for the rapid,versatile,and sensitive detection of V.parahaemolyticus in seafood,with considerable potential to advance food safety diagnosis and on-site monitoring.
文摘Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully constructed by coordinatively assembling the semi-rigid multidentate ligand 5-(1-carboxyethoxy)isophthalic acid(H₃CIA)with the Nheterocyclic ligands 1,4-di(4H-1,2,4-triazol-4-yl)benzene(1,4-dtb)and 1,4-di(1H-imidazol-1-yl)benzene(1,4-dib),respectively,around Co^(2+)ions.Single-crystal X-ray diffraction analysis revealed that in both complexes HU23 and HU24,the CIA^(3-)anions adopt aκ^(7)-coordination mode,bridging six Co^(2+)ions via their five carboxylate oxygen atoms and one ether oxygen atom.This linkage forms tetranuclear[Co4(μ3-OH)2]^(6+)units.These Co-oxo cluster units were interconnected by CIA^(3-)anions to assemble into 2D kgd-type structures featuring a 3,6-connected topology.The 2D layers were further connected by 1,4-dtb and 1,4-dib,resulting in 3D pillar-layered frameworks for HU23 and HU24.Notably,despite the similar configurations of 1,4-dtb and 1,4-dib,differences in their coordination spatial orientations lead to topological divergence in the 3D frameworks of HU23 and HU24.Topological analysis indicates that the frameworks of HU23 and HU24 can be simplified into a 3,10-connected net(point symbol:(4^(10).6^(3).8^(2))(4^(3))_(2))and a 3,8-connected tfz-d net(point symbol:(4^(3))_(2)((4^(6).6^(18).8^(4)))),respectively.This structural differentiation confirms the precise regulatory role of ligands on the topology of metal-organic frameworks.Moreover,the ultraviolet-visible absorption spectra confirmed that HU23 and HU24 have strong absorption capabilities for ultraviolet and visible light.According to the Kubelka-Munk method,their bandwidths were 2.15 and 2.08 eV,respectively,which are consistent with those of typical semiconductor materials.Variable-temperature magnetic susceptibility measurements(2-300 K)revealed significant antiferromagnetic coupling in both complexes,with their effective magnetic moments decreasing markedly as the temperature lowered.CCDC:2457554,HU23;2457553,HU24.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130719 and 42177173)the Doctoral Direct Train Project of Chongqing Natural Science Foundation(Grant No.CSTB2023NSCQ-BSX0029).
文摘Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.
文摘Knowledge distillation has become a standard technique for compressing large language models into efficient student models,but existing methods often struggle to balance prediction accuracy with explanation quality.Recent approaches such as Distilling Step-by-Step(DSbS)introduce explanation supervision,yet they apply it in a uniform manner that may not fully exploit the different learning dynamics of prediction and explanation.In this work,we propose a task-structured curriculum learning(TSCL)framework that structures training into three sequential phases:(i)prediction-only,to establish stable feature representations;(ii)joint prediction-explanation,to align task outputs with rationale generation;and(iii)explanation-only,to refine the quality of rationales.This design provides a simple but effective modification to DSbS,requiring no architectural changes and adding negligible training cost.We justify the phase scheduling with ablation studies and convergence analysis,showing that an initial prediction-heavy stage followed by a balanced joint phase improves both stability and explanation alignment.Extensive experiments on five datasets(e-SNLI,ANLI,CommonsenseQA,SVAMP,and MedNLI)demonstrate that TSCL consistently outperforms strong baselines,achieving gains of+1.7-2.6 points in accuracy and 0.8-1.2 in ROUGE-L,corresponding to relative error reductions of up to 21%.Beyond lexical metrics,human evaluation and ERASERstyle faithfulness diagnostics confirm that TSCL produces more faithful and informative explanations.Comparative training curves further reveal faster convergence and lower variance across seeds.Efficiency analysis shows less than 3%overhead in wall-clock training time and no additional inference cost,making the approach practical for realworld deployment.This study demonstrates that a simple task-structured curriculum can significantly improve the effectiveness of knowledge distillation.By separating and sequencing objectives,TSCL achieves a better balance between accuracy,stability,and explanation quality.The framework generalizes across domains,including medical NLI,and offers a principled recipe for future applications in multimodal reasoning and reinforcement learning.
文摘Reconfigurable intelligent surface(RIS)have been cast as a promising alternative to alleviate blockage vulnerability and enhance coverage capability for terahertz(THz)communications.Owing to large-scale array elements at transceivers and RIS,the codebook based beamforming can be utilized in a computationally efficient manner.However,the codeword selection for analog beamforming is an intractable combinatorial optimization(CO)problem.To this end,by taking the CO problem as a classification problem,a multi-task learning based analog beam selection(MTL-ABS)framework is developed to implement cooperative beam selection concurrently at transceivers and RIS.In addition,residual network and self-attention mechanism are used to combat the network degradation and mine intrinsic THz channel features.Finally,the network convergence is analyzed from a blockwise perspective,and numerical results demonstrate that the MTL-ABS framework greatly decreases the beam selection overhead and achieves near optimal sum-rate compared with heuristic search based counterparts.
基金supported by the National Key R&D Program of China(No.2022YFA1504100)the Anhui Provincial Major Science and Technology Project(No.202203a05020017)+4 种基金the National Natural Science Foundation of China(Nos.52222210,51925207,U1910210,52161145101,51972067,51902062,and 52002083)the“Transformational Technologies for Clean Energy and Demonstration”Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA21000000)the National Synchrotron Radiation Laboratory(No.KY2060000173)the Joint Fund of the Yulin University and the Dalian National Laboratory for Clean Energy(No.YLU-DNL Fund 2021002)the Fundamental Research Funds for the Central Universities(No.WK2060140026)。
文摘Silicon possesses a high theoretical capacity,making it a potential contender for lithium-ion battery(LIB)anodes.Nonetheless,its practical usage is challenged by low electrical conductivity and significant volume expansion during cycling.Here,we synthesized a novel silicon/carbon(Si/C)anode doped with ZnO via a template-derived method and high-temperature carbonization.The carbon structure,originated from metal-organic frameworks(MOFs)and ZnO doping,substantially enhanced the electrochemical properties of the composite material.It exhibited an initial capacity of 2100.3 mA h g^(-1)at a current density of 0.2 A g^(-1)and demonstrated excellent capacity retention over successive cycles.Moreover,the composite material displayed superior rate performance at higher current densities of 2 A g^(-1)and 3 A g^(-1).To address the low initial Coulombic efficiency(ICE)of siliconbased materials,we adopted a direct contact prelithiation approach and optimized the lithiation process by controlling the prelithiation time.After 30 min of prelithiation,the ICE reached 97.9%,thereby reducing the initial irreversible capacity loss(ICL)and realizing stable discharge-charge in subsequent cycles.This rational design provides valuable insights for achieving high-performance silicon anode.
基金the Natural Science Foundation of ZhejiangProvince(No.LZ24B020005)the National Natural Science Foundation of China(No.22071040)for financial support.
文摘High-sensitive quantitative determination of alpha-fetoprotein(AFP)is of crucial importance for early clinical diagnosis of cancers.Herein,an AuNPs-free electrochemical immunosensor(Ab1-Fc-COF)was prepared from a carboxylic group enriched COF by post-functionalization with detecting antibody(Ab1)and ferrocene(Fc),and used for electrochemical detection of AFP.Due to the small,homogeneous pore size of the COF,Ab1 with a big size was immobilized on the surface of the COF,while Fc with a small size was covalently modified both on the surface and in the pores of COF.The covalently immobilized Ab1 was quite stable and beneficial to specifically detect AFP biomarkers.Meanwhile,the enriched Fc molecules not only improved the conductivity of the COF,but also effectively transferred and amplified the electrochemical signal.This proposed immunosensor exhibited high sensitivity in detecting AFP with a detection limit of 0.39 pg/mL(S/N of 3:1)and a wide linear response range spanning from 1 pg/mL to 100 ng/mL when plotted against logarithmic concentrations.Furthermore,this immunosensor showed excellent selectivity,stability and reproducibility in the testing of real samples.This study presents an innovative prototype for construction of a precious metal-free,antibody-directly-immobilized,simple and stable electrochemical immunoprobe.
基金the Hebei Province Science and Technology Plan Project(19221909D)rincess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R308),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems.
基金Liaoning Provincial Social Science Fund Key Disciplines Development Project,Research on the New Supply Function of Entrepreneurs Based on Innovation Ecosystems Driven by Data(Grant No.L22ZD061)。
文摘Accelerating the development of new quality productive forces(NQPF),with innovation at its core,has become essential for firm growth in the new era.Drawing on financial data from China's A-share listed companies spanning the period 2010–2023,this study empirically investigates the impact of entrepreneurial spirit on firm-level NQPF.The results indicate that entrepreneurial spirit significantly promotes firm-level NQPF.Mechanism analysis indicates that entrepreneurial effort—underpinned by technological capital accumulation,effective incentive and constraint mechanisms,and a competitive market environment—plays a mediating role in this relationship.Further heterogeneity analysis reveals that,amid China's economic transition,the positive effects of entrepreneurial spirit are more pronounced in non-state-owned enterprises,high-tech firms,and newly established firms.Accordingly,systematic efforts should be pursued across the technological,organizational,and environmental(TOE)dimensions to optimize the cultivation of entrepreneurial spirit.In particular,greater emphasis should be placed on productive entrepreneurial spirit and the constructive role of entrepreneurial effort,so as to fully leverage their contribution to the advancement of firm-level NQPF.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(RS-2024-00333650)supported by basic science research program through the National Research Foundation of Korea funded by the Ministry of Education(NRF-2019R1A6A1A11055660)+1 种基金supported by the Technology Innovation Program(“20013621”,Center for Super Critical Material Industrial Technology)funded By the Ministry of Trade,Industry&Energy(MOTIE,Korea)supported by Strategic Networking&Development Program funded by the Ministry of Science and ICT through the National Research Foundation of Korea(RS-2023-00268523)。
文摘Gas sensors are valuable tools for human applications,and extensive research has been conducted in this field.However,practical implementation has yet to be fully realized.In response,efforts have been made to explore metal-organic frameworks(MOFs),a novel class of porous materials,as potential solutions.MOFs exhibit exceptional porosity and highly tunable chemical compositions and structures,giving rise to a wide range of unique physical and chemical properties.Significant progress has been achieved in developing MOF-based gas sensors,improving sensing performance for various gases.This review aims to provide a comprehensive understanding of MOF-based gas sensors,even for readers unfamiliar with MOFs and gas sensors.It covers the working principles of these sensors,fundamental concepts of MOFs,strategies for tuning MOF properties,fabrication techniques for MOF films,and recent studies on MOF and MOF-derivative gas sensors.Finally,current challenges,overlooked aspects,and future directions for fully exploiting the potential of MOFs in gas sensor development are discussed.
基金financially supported by the National Natural Science foundation of China(grants nos.52272176)。
文摘Lead-halide perovskite solar cells(PSCs)have rapidly achieved certified efficiencies>27%,rivaling silicon photovoltaics.However,their commercialization is hindered by intrinsic material challenges:poor operational stability under moisture,heat,and light;toxic lead leakage from degraded films.Metal-organic frameworks(MOFs),with their unique framework structure,large specific surface area,high heavy metal capturing capacity,and tunable conductivity,offer promising solutions to these issues.Recent studies have integrated MOFs into PSCs architectures to enhance performance and durability.This comprehensive review begins with an in-depth discussion of the structure,optical properties,electrical characteristics,and stability of MOFs,as well as their theoretical compatibility with perovskites.Subsequently,it provides a detailed analysis of how MOFs enhance charge carrier transport,promote perovskite crystallinity,improve device stability,and suppress lead leakage in PSCs.In summary,this review examines the research progress and potential of integrating MOFs with perovskites to address the critical PSCs challenges of efficiency,instability,and toxicity.
基金financially supported by the National Natural Science Foundation of China(No.22305009)the Science and Technology Development Fund,Macao SAR(File no.FDCT-0125/2022/A and FDCT-0006/2023/RIB1)Hong Kong Research Grant Council(RGC)General Research Fund(GRF)City U 11305419,11306920,CityU 11308721,CityU 11316522,and SIRG7020022。
文摘Photocatalytic carbon dioxide reduction reaction(CO_(2)RR)is a carbon-neutral strategy to address global energy use and its impact on climate.Metal oxide and metal chalcogenide catalysts are the most investigated catalysts for photocatalytic CO_(2)RR.Unfortunately,low CO_(2)adsorption ability and limited active sites of metal oxide and metal chalcogenide catalysts for CO_(2)RR make them less competitive compared to their industrial counterparts.Inspired by applications of porphyrin-based metal-organic framework(MOF)catalysts for hydrogen evolution and photodynamic therapy,the investigations of these porphyrin-based MOFs,including pristine and composite porphyrin-based MOFs in photocatalytic CO_(2)RR,have attracted significant attention in the last five years due to their excellent CO_(2)adsorption capacities,high porosity,high stability,exceptional optoelectronic properties,and multi-functionality.However,due to the difference in photocatalytic CO_(2)RR,several critical issues need to be addressed to achieve the rational design of advanced porphyrin-based MOF photocatalysts to improve activity,selectivity,and stability for CO_(2)RR.Here,we review recent developments in the field of porphyrin-based MOF CO_(2)RR photocatalysts,along with critical issues,challenges,and perspectives concerning porphyrin-based MOF catalysts for photocatalytic CO_(2)RR.
基金funded by the National Natural Science Foundation of China(Nos.52372264,32271609and 52473109)+2 种基金The Natural Science Foundation of Heilongjiang Province of China(No.LH2023B002)The Fundamental Research Funds for the Central Universities(No.2572023CT12)Undergraduate Training Programs for Innovations by NEFU(No.202310225565)。
文摘Metal-organic frameworks(MOFs)with high porosity,specific surface area,and unique topologies are highly regarded for their applications in photocatalysis,medical treatment,and environmental pollutant degradation.However,due to the limitations of the tumor microenvironment(TME),traditional MOFs have limited efficacy in this environment.This paper designs multi-metal oxide-based heterostructure POMOFs nanoreactors with a nesting doll-like structure.This new structure not only exhibits therapeutic effects in TME but also utilizes ultrasound(US)to enhance the release of reactive oxygen species(ROS)for CDT&SDT co-therapy,becoming an effective sound sensitizer for destroying tumor cells.In summary,our study proposes an idea for constructing multi-metal oxide-based heterostructure MOFs nanoreactors material with a nesting doll-like structure to enhance ROS release and synergistically treat tumor diseases.
基金supported by the National Natural Science Foundation of China(No.52304329)the Yunnan Fundamental Research Projects(No.202201BE070001-003),Guo Lin would like to acknowledge Xing Dian talent support program of Yunnan Province.
文摘The recovery of precious metals(PMs)from secondary resources is critical for addressing global supply-chain vulnerabilities and sustainable resource utilization.This review systematically examines the transformative potential of metal-organic frameworks(MOFs)as next-generation adsorbents for PM recovery,focusing on their synthesis,functionalization,and multiscale adsorption mechanisms.We critically analyze conventional pyrometallurgical and hydrometallurgical methods and highlight their limitations in terms of selectivity,energy consumption,and secondary pollution.In contrast,MOFs offer tunable porosity,abundant active sites,and tunable surface chemistry,enabling efficient PM capture via synergistic physical and chemical adsorption.Advanced modification techniques,including direct synthesis and post-synthetic modification,are reviewed to propose strategies for enhancing the adsorption kinetics and selectivity for Au,Ag,Pt,and Pd.Key structure-property relationships are established through multiscale characterization and thermodynamic models,revealing the critical roles of hierarchical porosity,soft donor atoms,and framework stability.Industrial challenges,such as aqueous stability and scalability,are addressed via Zr-O bond strengthening,hydrophobic functionalization,and support immobilization.This study consolidates the experimental and theoretical advances in MOF-based PM recovery and provides a roadmap for translating laboratory innovations into practical applications within the circular-economy framework.
基金supported by the National Key R&D Program of China(No.2023YFA1507204)National Natural Science Foundation ofChina(Nos.22475074,22171139,22225109,22302055)+4 种基金Natural Science Foundation of Guangdong Province(No.2023B1515020076)Key Scientific Research Project Plan of Colleges and Universities of Henan Province(No.24B150004)The Double Thousand Talents Plan of Jiangxi Province(No.jxsq2023102003)Project supported by the Guangdong Provincial Key Laboratory of Carbon Dioxide Resource Utilization(No.2024B121201001)Project supportedby the Major Research plan of the National Natural Science Foundation of China(No.92461310).
文摘Polyimide-linkage covalent organic frameworks(PI-COFs),as a subclass of the COFs material family,featuring the unique combination of excellent thermal stability of polyimide,tunable pore sizes,as well as high crystallinity and surface area of COFs,are expected to be a novel type of promising crystalline porous material with potential applications in adsorption and separation,catalysis,chemical sensing,and energy storage.Therefore,it is increasingly important to summarize polyimide-linkage in COFs and related applications and provide in-depth insight to accelerate future development.In this review,we offer a comprehensive overview of recent advancements in PI-COFs,emphasizing their synthesis methods,design principles and applications.Finally,our brief outlooks on the current challenges and future developments of PI-COFs are provided.Overall,this review aims to guide the recent and future development of PI-COFs.
基金supported by National Natural Science Foundation of China(32494793).
文摘Cellulose frameworks have emerged as promising materials for light management due to their exceptional light-scattering capabilities and sustainable nature.Conventional biomass-derived cellulose frameworks face a fundamental trade-off between haze and transparency,coupled with impractical thicknesses(≥1 mm).Inspired by squid’s skin-peeling mechanism,this work develops a peroxyformic acid(HCOOOH)-enabled precision peeling strategy to isolate intact 10-μm-thick bamboo green(BG)frameworks—100×thinner than wood-based counterparts while achieving an unprecedented optical performance(88%haze with 80%transparency).This performance surpasses delignified biomass(transparency<40%at 1 mm)and matches engineered cellulose composites,yet requires no energy-intensive nanofibrillation.The preserved native cellulose I crystalline structure(64.76%crystallinity)and wax-coated uniaxial fibril alignment(Hermans factor:0.23)contribute to high mechanical strength(903 MPa modulus)and broadband light scattering.As a light-management layer in polycrystalline silicon solar cells,the BG framework boosts photoelectric conversion efficiency by 0.41%absolute(18.74%→19.15%),outperforming synthetic anti-reflective coatings.The work establishes a scalable,waste-to-wealth route for optical-grade cellulose materials in next-generation optoelectronics.
