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
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
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
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
The development of efficient photocatalysts for crucial organic transformation,such as aerobic oxidation,remains challenging.Although powdered porous materials offer abundant accessible active sites,their application ...The development of efficient photocatalysts for crucial organic transformation,such as aerobic oxidation,remains challenging.Although powdered porous materials offer abundant accessible active sites,their application in liquid-phase catalysis is often limited by insufficient light absorption and inevitable charge recombination,which are inherent drawbacks of conventional heterogeneous catalysts.Here,through rational design and nanoscale-engineering of porous aromatic frameworks(PAFs)comprising porphyrin and porous organic cage,a quasi-homogeneous porous photocatalyst with high catalytic activity and controllable dimension was developed.The interface-directed growth in oil-in-water emulsion shaped the morphology of photoactive PAFs from powders to nanoflakes,which facilitated the light absorbance and catalyst-substrate interaction.Compared with PAF powders,PAF nanoflakes exhibited superior photocatalytic activity for aerobic oxidation.For mustard gas simulant(2-chloroethyl ethyl sulfide,CEES),PAF nanoflakes exhibited ultrafast detoxification rates in room air with a half-life(t_(1/2))as fast as 26s,which even exceeded other catalysts in pure oxygen.It also completely catalyzed the aerobic oxidation of thioether within 15 min,which is almost the fastest rate among any reported organic photocatalysts.Furthermore,the efficient catalytic performance under mild conditions caused by improved light enrichment,surface charge transfer and carrier lifetime was elucidated.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
This study presents a novel polyoxometalate(POM)constructed crystalline inorganic framework,featuring a 2D layered architecture with irregular porosity and inherent proton sources.This unique configuration establishes...This study presents a novel polyoxometalate(POM)constructed crystalline inorganic framework,featuring a 2D layered architecture with irregular porosity and inherent proton sources.This unique configuration establishes an intrinsic hydrogen bonding network that facilitates proton hopping(Grotthuss mechanism),achieving a[100]directional proton conductivity of 1.75×10^(-3)S cm^(-1)under a low relative humidity(RH)of 35%at 298 K.Notably,under elevated conditions(338 K,95%RH),it attains a superprotonic conductivity of 1.61 S cm^(-1),representing one of the highest values recorded for framework materials to date.Analysis of the molecular structure,pore geometry characteristics and topological connectivity,and water vapor adsorption experiment(offering proton diffusion coefficient),indicates that the exceptional water-mediated proton dynamics stem from the interlayer S-shaped irregular pore channels,which probably induce a siphon-like effect to significantly enhance the transport of hydrated protons under the vehicle mechanism.This work not only proposes a POM strategy for constructing 2D inorganic frameworks but also reveals the irregular pore channel-enhanced proton dynamics,providing new insights into the optimization of proton conductors.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
Three-dimensional supramolecular organic frameworks with precisely tunable pore sizes are highly demanded for a wide range of applications,e.g.,encapsulating enzymes to enhance their stability,activity,and reusability...Three-dimensional supramolecular organic frameworks with precisely tunable pore sizes are highly demanded for a wide range of applications,e.g.,encapsulating enzymes to enhance their stability,activity,and reusability.However,precise control and tune the pore size of such frameworks still remains a significant challenge to date.In this study,we constructed supramolecular polymer frameworks using rigid tetrahedral star polyisocyanides with tunable length and sufficiently narrow distribution as building block.First,a series of tetrahedral four-arm star polyisocyanides with controlled chain lengths and narrow molecular weight distributions was prepared via the Pd(Ⅱ)-catalyzed living isocyanide polymerization.Then 2-ureido-4[1H]-pyrimidinone(Upy) unit was installed onto each chain-end of polyisocyanide arms via post-polymerization functionalization.Leveraging the supramolecular hydrogen bonding interactions between the terminal Upy units,well-ordered supramolecular polymer frameworks were readily obtained.Notably,the pore size was dependent on the chain length of the polyisocyanide arms.Precisely control the chain length of polyisocyanide arms,supramolecular polymer frameworks with pore sizes ranging from 5.06 nm to 9.72 nm were achieved.These frameworks,with tunable and large pore apertures,demonstrated exceptional capabilities in encapsulating enzymes of different sizes,such as lipase(TL),horseradish peroxidase(HRP),and glucose oxidase(GOx).The encapsulated enzymes exhibited significantly enhanced catalytic activity and durability.Moreover,the frameworks' tunable and large pore apertures facilitated the co-encapsulation of multiple enzymes,enabling efficient dual-enzyme cascade reactions.展开更多
When performing English-to-Tamil Neural Machine Translation(NMT),end users face several challenges due to Tamil's rich morphology,free word order,and limited annotated corpora.Although available transformer-based ...When performing English-to-Tamil Neural Machine Translation(NMT),end users face several challenges due to Tamil's rich morphology,free word order,and limited annotated corpora.Although available transformer-based models offer strong baselines,they compromise syntactic awareness and the detection and man-agement of offensive content in cluttered,noisy,and informal text.In this paper,we present POSDEP-Offense-Trans,a multi-task NMT framework that combines Part-of-Speech(POS)and Dependency Parsing(DEP)methods with a robust offensive language classification module.Our architecture enriches the Transformer encoder with syntax-aware embeddings and provides syntax-guided attention mechanisms.The architecture incorporates a structure-aware contrastive loss that reinforces syntactic consistency and deploys auxiliary classification heads for POS tagging,dependency parsing,and multi-class offensive detection.The classifier for offensive words operates at both sentence and token levels and obtains guidance from syntactic features and formal finite automata rules that model offensive language structures-hate speech,profanity,sarcasm,and threats.Using this architecture,we construct a syntactically enriched,socially annotated corpus.Experimental results show improvements in translation quality,with a BLEU score of 33.5,UAS/LAS parsing accuracies of 92.4%and 90%,and a 4.5%Fl-score gain in offensive content detection compared with baseline POS+DEP+Offense models.Also,the proposed model achieved 92.3%in offensive content neutralization,as confirmed by ablation studies.This comprehensive English-Tamil NMT model that unifies syntactic modelling and ethical filtering-laying the groundwork for applications in social media moderation,hate speech mitigation,and policy-compliant multilingual content generation.展开更多
The separation of propylene(C_(3)H_(6))and propane(C_(3)H_(8))presents a significant industrial challenge due to their similar molecular dimensions and physicochemical properties.Among various separation methods,molec...The separation of propylene(C_(3)H_(6))and propane(C_(3)H_(8))presents a significant industrial challenge due to their similar molecular dimensions and physicochemical properties.Among various separation methods,molecular sieving emerges as the most promising approach,but it will be significantly compromised at high temperatures due to the significant thermal motion.Here,we report a thermally robust zinc-based metal-organic framework(MOF)that can be synthesized on sub-kilogram scale and achieve exceptional C_(3)H_(6)/C_(3)H_(8) separation performances across a broad temperature range(298–353 K).Unlike conventional MOFs suffering from thermal lattice expansion to give poorer selectivity,this new MOF gives the adsorption capacity of C_(3)H_(6)essentially unchanged and that of C_(3)H_(8) negligible at elevated temperatures,outperforming most state-of-the-art adsorbents,in virtue of multiple hydrogen bonds at the aperture.Column breakthrough experiments confirmed the excellent separation capability,and showed no performance degradation over multi-round adsorption-desorption cycles at 353 K.This study addresses the critical challenge of the trade-off between temperature and selectivity in adsorptive separation,which offers new insights into the design of porous structures for highly effective separation at high temperatures.展开更多
Redox-active porous aromatic frameworks(PAFs)have emerged as hopeful candidates for sodium-ion batteries(SIBs)in view of their porous structures,chemical stability and tunable architectures.Herein,we successfully synt...Redox-active porous aromatic frameworks(PAFs)have emerged as hopeful candidates for sodium-ion batteries(SIBs)in view of their porous structures,chemical stability and tunable architectures.Herein,we successfully synthesized two redox-active PAFs(PAF-305 and PAF-306)with different nitrogen-containing motifs,and demonstrated their application as cathode materials for SIBs.Density functional theory(DFT)calculations reveal that nitrogen-rich PAF-305 exhibits a lower lowest unoccupied molecular orbital(LUMO)energy level(-3.35 eV)and a narrower energy gap(E_(g))(2.40 eV)compared with nitrogen-poor PAF-306.As expected,PAF-305 displays outstanding electrochemical performance,comprising a high reversible capacity of 145.2 mAh g^(-1)at 0.05 A g^(-1)and satisfactory cycling stability with 92% capacity retention over 1000 cycles at 0.2 A g^(-1).Remarkably,PAF-305 maintains robust electrochemical properties across a wide temperature range(-20℃ to 50℃).Through a combination of experimental characterizations and theoretical calculations,the sodium-ion storage mechanism of PAF-305 is elucidated.This study not only provides a promising strategy for exploring other redox-active organic units in the design of novel PAFs,but also expands the potential applications of PAFs in energy storage systems.展开更多
Progressive skin fibrosis ultimately results in irreversible contractures,causing both joint dysfunction and cosmetic deformity.The key pathological features of skin fibrosis include persistent inflammation and abnorm...Progressive skin fibrosis ultimately results in irreversible contractures,causing both joint dysfunction and cosmetic deformity.The key pathological features of skin fibrosis include persistent inflammation and abnormal accumulation of the extracellular matrix(ECM),with epithelialmesenchymal transition(EMT)playing a critical role in disease progression.However,current therapeutic strategies for cutaneous fibrosis are largely palliative and often require repeated interventions,with limited efficacy.Celastrol(Cel)exerts anti-inflammatory and anti-fibrotic effects in skin tissue,but its clinical application is limited by poor bioavailability and a narrow therapeutic window.Tetrahedral framework nucleic acid(tFNA),a novel nanocarrier system,exhibits multiple advantages,including enhanced cellular uptake,improved cell viability,and intrinsic anti-fibrotic and anti-inflammatory properties.Therefore,this study applied tFNA-Cel complex(TCC)as an advanced nanotherapeutic agent,designed to exert a synergistic anti-fibrotic effect.In this study,an in vitro model of skin fibrosis was established using human keratinocyte(HaCaT)cells treated with 5 ng mL^(-1) transforming growth factor beta(TGF-β)for 24 h.The results showed that TCC significantly inhibited EMT progression by reducingα-smooth muscle actin(α-SMA)levels and increasing E-cadherin level.Compared to tFNA or Cel alone,TCC exhibited superior anti-fibrotic effects in the fibrosis model,as evidenced by modulation of SMAD family member 2(SMAD2)signaling and collagen I expression.Furthermore,the TCC group showed lower levels of nuclear factorκB p65(NF-κB p65),BCL-2-associated X protein(Bax),and reactive oxygen species(ROS)compared to the Cel or tFNA groups.These findings highlight TCC as a promising treatment for skin fibrosis,with its synergistic anti-fibrotic effects providing new therapeutic avenues.展开更多
CO_(2)reduction technology can promote the resource utilization of carbon and help alleviate global warming and energy supply pressure.It is an effective way to achieve energy conversion and utilization.Covalent organ...CO_(2)reduction technology can promote the resource utilization of carbon and help alleviate global warming and energy supply pressure.It is an effective way to achieve energy conversion and utilization.Covalent organic frameworks(COFs)are porous crystalline materials formed by connecting organic monomers through covalent bonds.They have the characteristics of functional diversity and rich chemical properties.Their advantages,such as high porosity,a wide range of visible light absorption,and excellent charge separation efficiency,give them good potential in CO_(2)capture,separation,and conversion.Currently,Cu is a key metal in the catalytic CO_(2)reduction reaction(CO_(2)RR)for the preparation of high-value-added chemicals.The preparation of highly stable and large-pore Cu-based COFs using COFs as an ideal sacrificial template for loading Cu can be used to develop high-performance electrocatalysts and photocatalysts.In this review,we discuss the latest advancements in this field,including the development of various Cu-based COFs and their applications as catalysts for CO_(2)RR.Here,we mainly introduce the synthesis strategies,some important characterization information,and the applications of electrocatalytic and photocatalytic CO_(2)conversion using these previously reported Cu-based COFs.展开更多
The practical deployment of lithium metal batteries remains severely constrained,especially under elevated temperatures.Although metal-organic frameworks(MOFs)improve the thermal stability of liquid electrolytes by ca...The practical deployment of lithium metal batteries remains severely constrained,especially under elevated temperatures.Although metal-organic frameworks(MOFs)improve the thermal stability of liquid electrolytes by capturing them in well-ordered sub-nanopores,interparticle voids between MOF particles readily absorb liquid electrolyte,obscuring our understanding of the intrinsic role of nanopores in directing Li^(+)transport.To address this challenge,we introduce a one-dimensional(1D)MOF model architecture that eliminates interparticle effects and enables direct observation of Li^(+)solvation and de-solvation dynamics.Comparative studies of 1D HKUST-1 and ZIF-8 uncover distinct transport behaviors,supported by both experimental measurements and neural network potential-based molecular dynamics simulations.Building on these insights,we construct a hierarchical core-shell MOF architecture by integrating ZIF-8(core)and HKUST-1(shell)onto a hybrid fiber scaffold.This design harnesses the complementary strengths of both MOFs to achieve continuous ion pathways,directional Li^(+)conduction,and improved thermal and electrochemical resilience.展开更多
The recovery of gold from waste electronic and electric equipment(WEEE) has gained great attention with the increased number of WEEE,because it can largely alleviate the pressure on the environment and resources.Coval...The recovery of gold from waste electronic and electric equipment(WEEE) has gained great attention with the increased number of WEEE,because it can largely alleviate the pressure on the environment and resources.Covalent organic frameworks(COFs) are ideal adsorbents for gold recovery owing to their large surface area,good stability,easily functionalized ability,periodic structures,and definitive nanopores.Herein,a cyano-functionalized COF(COF-CN) with high crystallinity was large-scale prepared under mild conditions for the recovery of gold.The introduction of cyano groups enable COF-CN to exhibit excellent gold recovery performance,which possesses fast adsorption kinetics,high cycling stability,and adsorption capacity up to 663.67 mg/g.Excitingly,COF-CN showed extremely high selectivity for gold ions,even in the presence of various competing cations and anions.The COF-CN maintained excellent selectivity and removal efficiency in gold recovery experiments from WEEE.The facile synthesis of COF-CN and its outstanding selectivity in actual samples make it an attractive opportunity for practical gold recovery.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.
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 Natural Science Foundation of China(22075040,U21A20330,22131004)the National Key R&D Program of China(2022YFB3805900)+2 种基金the Jilin Provincial Scientific and Technological Development Program(20240602105RC)the Innovation Platform for Academicians of Hainan Provincethe Specific Research Fund of the Innovation Platform for Academicians of Hainan Province(YSPTZX202321)。
文摘The development of efficient photocatalysts for crucial organic transformation,such as aerobic oxidation,remains challenging.Although powdered porous materials offer abundant accessible active sites,their application in liquid-phase catalysis is often limited by insufficient light absorption and inevitable charge recombination,which are inherent drawbacks of conventional heterogeneous catalysts.Here,through rational design and nanoscale-engineering of porous aromatic frameworks(PAFs)comprising porphyrin and porous organic cage,a quasi-homogeneous porous photocatalyst with high catalytic activity and controllable dimension was developed.The interface-directed growth in oil-in-water emulsion shaped the morphology of photoactive PAFs from powders to nanoflakes,which facilitated the light absorbance and catalyst-substrate interaction.Compared with PAF powders,PAF nanoflakes exhibited superior photocatalytic activity for aerobic oxidation.For mustard gas simulant(2-chloroethyl ethyl sulfide,CEES),PAF nanoflakes exhibited ultrafast detoxification rates in room air with a half-life(t_(1/2))as fast as 26s,which even exceeded other catalysts in pure oxygen.It also completely catalyzed the aerobic oxidation of thioether within 15 min,which is almost the fastest rate among any reported organic photocatalysts.Furthermore,the efficient catalytic performance under mild conditions caused by improved light enrichment,surface charge transfer and carrier lifetime was elucidated.
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 Natural Science Foundation of China(22271075,22171071)。
文摘This study presents a novel polyoxometalate(POM)constructed crystalline inorganic framework,featuring a 2D layered architecture with irregular porosity and inherent proton sources.This unique configuration establishes an intrinsic hydrogen bonding network that facilitates proton hopping(Grotthuss mechanism),achieving a[100]directional proton conductivity of 1.75×10^(-3)S cm^(-1)under a low relative humidity(RH)of 35%at 298 K.Notably,under elevated conditions(338 K,95%RH),it attains a superprotonic conductivity of 1.61 S cm^(-1),representing one of the highest values recorded for framework materials to date.Analysis of the molecular structure,pore geometry characteristics and topological connectivity,and water vapor adsorption experiment(offering proton diffusion coefficient),indicates that the exceptional water-mediated proton dynamics stem from the interlayer S-shaped irregular pore channels,which probably induce a siphon-like effect to significantly enhance the transport of hydrated protons under the vehicle mechanism.This work not only proposes a POM strategy for constructing 2D inorganic frameworks but also reveals the irregular pore channel-enhanced proton dynamics,providing new insights into the optimization of proton conductors.
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.
基金The National Natural Science Foundation of China (NSFC,Nos.92256201,52273006,22071041,92356302,and 21971052)Natural Science Foundation of Jilin Province (No.20240101181JC) are gratefully appreciated for financial the supportssupported by the User Experiment Assist System of Shanghai Synchrotron Radiation Facility (SSRF)。
文摘Three-dimensional supramolecular organic frameworks with precisely tunable pore sizes are highly demanded for a wide range of applications,e.g.,encapsulating enzymes to enhance their stability,activity,and reusability.However,precise control and tune the pore size of such frameworks still remains a significant challenge to date.In this study,we constructed supramolecular polymer frameworks using rigid tetrahedral star polyisocyanides with tunable length and sufficiently narrow distribution as building block.First,a series of tetrahedral four-arm star polyisocyanides with controlled chain lengths and narrow molecular weight distributions was prepared via the Pd(Ⅱ)-catalyzed living isocyanide polymerization.Then 2-ureido-4[1H]-pyrimidinone(Upy) unit was installed onto each chain-end of polyisocyanide arms via post-polymerization functionalization.Leveraging the supramolecular hydrogen bonding interactions between the terminal Upy units,well-ordered supramolecular polymer frameworks were readily obtained.Notably,the pore size was dependent on the chain length of the polyisocyanide arms.Precisely control the chain length of polyisocyanide arms,supramolecular polymer frameworks with pore sizes ranging from 5.06 nm to 9.72 nm were achieved.These frameworks,with tunable and large pore apertures,demonstrated exceptional capabilities in encapsulating enzymes of different sizes,such as lipase(TL),horseradish peroxidase(HRP),and glucose oxidase(GOx).The encapsulated enzymes exhibited significantly enhanced catalytic activity and durability.Moreover,the frameworks' tunable and large pore apertures facilitated the co-encapsulation of multiple enzymes,enabling efficient dual-enzyme cascade reactions.
文摘When performing English-to-Tamil Neural Machine Translation(NMT),end users face several challenges due to Tamil's rich morphology,free word order,and limited annotated corpora.Although available transformer-based models offer strong baselines,they compromise syntactic awareness and the detection and man-agement of offensive content in cluttered,noisy,and informal text.In this paper,we present POSDEP-Offense-Trans,a multi-task NMT framework that combines Part-of-Speech(POS)and Dependency Parsing(DEP)methods with a robust offensive language classification module.Our architecture enriches the Transformer encoder with syntax-aware embeddings and provides syntax-guided attention mechanisms.The architecture incorporates a structure-aware contrastive loss that reinforces syntactic consistency and deploys auxiliary classification heads for POS tagging,dependency parsing,and multi-class offensive detection.The classifier for offensive words operates at both sentence and token levels and obtains guidance from syntactic features and formal finite automata rules that model offensive language structures-hate speech,profanity,sarcasm,and threats.Using this architecture,we construct a syntactically enriched,socially annotated corpus.Experimental results show improvements in translation quality,with a BLEU score of 33.5,UAS/LAS parsing accuracies of 92.4%and 90%,and a 4.5%Fl-score gain in offensive content detection compared with baseline POS+DEP+Offense models.Also,the proposed model achieved 92.3%in offensive content neutralization,as confirmed by ablation studies.This comprehensive English-Tamil NMT model that unifies syntactic modelling and ethical filtering-laying the groundwork for applications in social media moderation,hate speech mitigation,and policy-compliant multilingual content generation.
基金supported by the National Natural Science Foundation of China(22475240,22090061,22488101)the State Key Laboratory of Catalysis(2024SKL-A-010)。
文摘The separation of propylene(C_(3)H_(6))and propane(C_(3)H_(8))presents a significant industrial challenge due to their similar molecular dimensions and physicochemical properties.Among various separation methods,molecular sieving emerges as the most promising approach,but it will be significantly compromised at high temperatures due to the significant thermal motion.Here,we report a thermally robust zinc-based metal-organic framework(MOF)that can be synthesized on sub-kilogram scale and achieve exceptional C_(3)H_(6)/C_(3)H_(8) separation performances across a broad temperature range(298–353 K).Unlike conventional MOFs suffering from thermal lattice expansion to give poorer selectivity,this new MOF gives the adsorption capacity of C_(3)H_(6)essentially unchanged and that of C_(3)H_(8) negligible at elevated temperatures,outperforming most state-of-the-art adsorbents,in virtue of multiple hydrogen bonds at the aperture.Column breakthrough experiments confirmed the excellent separation capability,and showed no performance degradation over multi-round adsorption-desorption cycles at 353 K.This study addresses the critical challenge of the trade-off between temperature and selectivity in adsorptive separation,which offers new insights into the design of porous structures for highly effective separation at high temperatures.
基金supported by the Science&Technology Department of Jilin Province(20230508057RC)。
文摘Redox-active porous aromatic frameworks(PAFs)have emerged as hopeful candidates for sodium-ion batteries(SIBs)in view of their porous structures,chemical stability and tunable architectures.Herein,we successfully synthesized two redox-active PAFs(PAF-305 and PAF-306)with different nitrogen-containing motifs,and demonstrated their application as cathode materials for SIBs.Density functional theory(DFT)calculations reveal that nitrogen-rich PAF-305 exhibits a lower lowest unoccupied molecular orbital(LUMO)energy level(-3.35 eV)and a narrower energy gap(E_(g))(2.40 eV)compared with nitrogen-poor PAF-306.As expected,PAF-305 displays outstanding electrochemical performance,comprising a high reversible capacity of 145.2 mAh g^(-1)at 0.05 A g^(-1)and satisfactory cycling stability with 92% capacity retention over 1000 cycles at 0.2 A g^(-1).Remarkably,PAF-305 maintains robust electrochemical properties across a wide temperature range(-20℃ to 50℃).Through a combination of experimental characterizations and theoretical calculations,the sodium-ion storage mechanism of PAF-305 is elucidated.This study not only provides a promising strategy for exploring other redox-active organic units in the design of novel PAFs,but also expands the potential applications of PAFs in energy storage systems.
基金supported by the National Natural Science Foundation of China(82101077)Sichuan Science and Technology Program(2023NSFSC1516)+2 种基金West China School/Hospital of Stomatology Sichuan University(RCDWJS2023-5)Fundamental Research Funds for the Central UniversitiesResearch and Develop Program,West China School/Hospital of Stomatology Sichuan University。
文摘Progressive skin fibrosis ultimately results in irreversible contractures,causing both joint dysfunction and cosmetic deformity.The key pathological features of skin fibrosis include persistent inflammation and abnormal accumulation of the extracellular matrix(ECM),with epithelialmesenchymal transition(EMT)playing a critical role in disease progression.However,current therapeutic strategies for cutaneous fibrosis are largely palliative and often require repeated interventions,with limited efficacy.Celastrol(Cel)exerts anti-inflammatory and anti-fibrotic effects in skin tissue,but its clinical application is limited by poor bioavailability and a narrow therapeutic window.Tetrahedral framework nucleic acid(tFNA),a novel nanocarrier system,exhibits multiple advantages,including enhanced cellular uptake,improved cell viability,and intrinsic anti-fibrotic and anti-inflammatory properties.Therefore,this study applied tFNA-Cel complex(TCC)as an advanced nanotherapeutic agent,designed to exert a synergistic anti-fibrotic effect.In this study,an in vitro model of skin fibrosis was established using human keratinocyte(HaCaT)cells treated with 5 ng mL^(-1) transforming growth factor beta(TGF-β)for 24 h.The results showed that TCC significantly inhibited EMT progression by reducingα-smooth muscle actin(α-SMA)levels and increasing E-cadherin level.Compared to tFNA or Cel alone,TCC exhibited superior anti-fibrotic effects in the fibrosis model,as evidenced by modulation of SMAD family member 2(SMAD2)signaling and collagen I expression.Furthermore,the TCC group showed lower levels of nuclear factorκB p65(NF-κB p65),BCL-2-associated X protein(Bax),and reactive oxygen species(ROS)compared to the Cel or tFNA groups.These findings highlight TCC as a promising treatment for skin fibrosis,with its synergistic anti-fibrotic effects providing new therapeutic avenues.
文摘CO_(2)reduction technology can promote the resource utilization of carbon and help alleviate global warming and energy supply pressure.It is an effective way to achieve energy conversion and utilization.Covalent organic frameworks(COFs)are porous crystalline materials formed by connecting organic monomers through covalent bonds.They have the characteristics of functional diversity and rich chemical properties.Their advantages,such as high porosity,a wide range of visible light absorption,and excellent charge separation efficiency,give them good potential in CO_(2)capture,separation,and conversion.Currently,Cu is a key metal in the catalytic CO_(2)reduction reaction(CO_(2)RR)for the preparation of high-value-added chemicals.The preparation of highly stable and large-pore Cu-based COFs using COFs as an ideal sacrificial template for loading Cu can be used to develop high-performance electrocatalysts and photocatalysts.In this review,we discuss the latest advancements in this field,including the development of various Cu-based COFs and their applications as catalysts for CO_(2)RR.Here,we mainly introduce the synthesis strategies,some important characterization information,and the applications of electrocatalytic and photocatalytic CO_(2)conversion using these previously reported Cu-based COFs.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2023-00217581)supported by the Nano&Material Technology Development Program through the National Research Foundation of Korea(NRF)funded by Ministry of Science and ICT(RS-2024-00406724)supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2025-25430676)。
文摘The practical deployment of lithium metal batteries remains severely constrained,especially under elevated temperatures.Although metal-organic frameworks(MOFs)improve the thermal stability of liquid electrolytes by capturing them in well-ordered sub-nanopores,interparticle voids between MOF particles readily absorb liquid electrolyte,obscuring our understanding of the intrinsic role of nanopores in directing Li^(+)transport.To address this challenge,we introduce a one-dimensional(1D)MOF model architecture that eliminates interparticle effects and enables direct observation of Li^(+)solvation and de-solvation dynamics.Comparative studies of 1D HKUST-1 and ZIF-8 uncover distinct transport behaviors,supported by both experimental measurements and neural network potential-based molecular dynamics simulations.Building on these insights,we construct a hierarchical core-shell MOF architecture by integrating ZIF-8(core)and HKUST-1(shell)onto a hybrid fiber scaffold.This design harnesses the complementary strengths of both MOFs to achieve continuous ion pathways,directional Li^(+)conduction,and improved thermal and electrochemical resilience.
基金financially supported by the National Natural Science Foundation of China (No.51972302)。
文摘The recovery of gold from waste electronic and electric equipment(WEEE) has gained great attention with the increased number of WEEE,because it can largely alleviate the pressure on the environment and resources.Covalent organic frameworks(COFs) are ideal adsorbents for gold recovery owing to their large surface area,good stability,easily functionalized ability,periodic structures,and definitive nanopores.Herein,a cyano-functionalized COF(COF-CN) with high crystallinity was large-scale prepared under mild conditions for the recovery of gold.The introduction of cyano groups enable COF-CN to exhibit excellent gold recovery performance,which possesses fast adsorption kinetics,high cycling stability,and adsorption capacity up to 663.67 mg/g.Excitingly,COF-CN showed extremely high selectivity for gold ions,even in the presence of various competing cations and anions.The COF-CN maintained excellent selectivity and removal efficiency in gold recovery experiments from WEEE.The facile synthesis of COF-CN and its outstanding selectivity in actual samples make it an attractive opportunity for practical gold recovery.
