Focusing on the digital evaluation of classroom teaching quality in private colleges and universities,an indicator model of“teaching subject-teaching object-teaching effect”for the landscape architecture major of Ch...Focusing on the digital evaluation of classroom teaching quality in private colleges and universities,an indicator model of“teaching subject-teaching object-teaching effect”for the landscape architecture major of Chongqing College of Humanities,Science&Technology was constructed.By using methods such as Delphi,AHP,Likert and questionnaire survey,the teaching quality of 8 courses of landscape architecture major was evaluated.The results show that the average score of the indicators is 2.8776,indicating that the overall improvement space for the teaching quality of the sample professional courses is relatively large,and the key shortcomings are students’learning interest and initiative,the application and transformation of professional knowledge,as well as the cultivation of innovation and practical ability.The research verified the scientific nature and discrimination of the model,and put forward suggestions for the precise improvement of classroom atmosphere,assignment design and ability cultivation driven by data,thereby providing a replicable model for the digital evaluation and teaching quality improvement of engineering majors in private colleges and universities.展开更多
Water-cooled system have significantly enhanced the power generation efficiency of offshore wind turbines.However,these innovative systems are susceptible to substantial biological fouling,maintenance challenges,and h...Water-cooled system have significantly enhanced the power generation efficiency of offshore wind turbines.However,these innovative systems are susceptible to substantial biological fouling,maintenance challenges,and high upkeep costs.Therefore,the development of a specialized front-end filter tailored for direct current water-cooled system is importance.This involves the integration of dimensionally stable anode(DSA)and nickel alloy cathode,valued for their corrosion resistance in seawater,into a novel front-end filter system for Water-cooled applications.This system has the dual capability of generating hydrogen and chlorine for self-cleaning purposes.Implementing a flushing pulse electrolysis mode,it effectively mitigates electrode failure induced by cathodic calcium and magnesium deposition,thereby significantly prolonging electrode lifespan.Laboratory tests comprising system assembly and performance evaluations were conducted,with the system programmed to operate for 5 minutes every 24 hours under continuous flushing by natural seawater to simulate real-world conditions.After more than 11 months of continuous flushing,observations reveal that the DSA mesh and nickel alloy mesh maintain intact structural integrity and normal functioning.Subsequent 1꞉1 physical prototype Sea trial further validated the soundness of the system design and electrolytic control parameters.展开更多
The damage caused by thermal stress during rewarming vitrified biosamples is one of the major obstacles for clinical purposes.Magnetic warming is a highly effective approach to overcome this hurdle and can achieve rap...The damage caused by thermal stress during rewarming vitrified biosamples is one of the major obstacles for clinical purposes.Magnetic warming is a highly effective approach to overcome this hurdle and can achieve rapid and spatially homogeneous heating.The current research investigates the effects of magnetic warming on the histological and biomechanical properties of the vitrified umbilical arteries(UAs)through experiments and simulation.The results of experiments show that,for the case of magnetic warming comparing with the conventional water bath,magnetic warming presents better preservation of extracellular matrix(ECM),collagen fibers,elastic fibers,and muscle fibers of the umbilical artery.There is no significant difference between magnetothermal and fresh UAs(p>0.05)in the elastic modulus and the ultimate stress.The theoretical results reveal that the maximum temperature difference Tmax inside the biosample is 1.117±0.649℃,and the maximum thermal stressmax is 0.026±0.016 MPa.However,for the case of conventional water bath,Tmax is 32.342±0.967℃andmax is 1.453±0.047 MPa.Moreover,we have arrived at the same conclusion by simulation as theoretical calculation have.Therefore,magnetic warming can effectively reduce the thermal stress damage of biological samples during the warming period due to more uniform and rapid warming.These results confirm that magnetothermal can significantly improve the mechanical properties of large size cryopreserved tissues or organs such as UAs.展开更多
Firstly,the behavior of marine science and technology talents,such as scientific research,innovation,agglomeration and flow behavior,was analyzed,and then the problems in the training of marine science and technology ...Firstly,the behavior of marine science and technology talents,such as scientific research,innovation,agglomeration and flow behavior,was analyzed,and then the problems in the training of marine science and technology talents were discussed.Finally,the training ways of marine science and technology talents were proposed.展开更多
Since the beginning of the 21st century,advances in big data and artificial intelligence have driven a paradigm shift in the geosciences,moving the field from qualitative descriptions toward quantitative analysis,from...Since the beginning of the 21st century,advances in big data and artificial intelligence have driven a paradigm shift in the geosciences,moving the field from qualitative descriptions toward quantitative analysis,from observing phenomena to uncovering underlying mechanisms,from regional-scale investigations to global perspectives,and from experience-based inference toward data-and model-enabled intelligent prediction.AlphaEarth Foundations(AEF)is a next-generation geospatial intelligence platform that addresses these changes by introducing a unified 64-dimensional shared embedding space,enabling-for the first time-standardized representation and seamless integration of 12 distinct types of Earth observation data,including optical,radar,and lidar.This framework significantly improves data assimilation efficiency and resolves the persistent problem of“data silos”in geoscience research.AEF is helping redefine research methodologies and fostering breakthroughs,particularly in quantitative Earth system science.This paper systematically examines how AEF’s innovative architecture-featuring multi-source data fusion,high-dimensional feature representation learning,and a scalable computational framework-facilitates intelligent,precise,and realtime data-driven geoscientific research.Using case studies from resource and environmental applications,we demonstrate AEF’s broad potential and identify emerging innovation needs.Our findings show that AEF not only enhances the efficiency of solving traditional geoscientific problems but also stimulates novel research directions and methodological approaches.展开更多
NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large lan...NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials discovery.The platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular informatics.By automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural applications.Additionally,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding expertise.Through automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials research.The latest version(available at https://figshare.com/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction tasks.By utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials datasets.While NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training data.By providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.展开更多
With the development of educational digitalization,how to effectively apply digital animation technology to traditional classroom teaching has become an urgent problem to be solved.This study explores the application ...With the development of educational digitalization,how to effectively apply digital animation technology to traditional classroom teaching has become an urgent problem to be solved.This study explores the application of Manim in the course of Mathematical Methods for Physics.Taking the visualization of Fourier series,complex numbers,and other content as examples,it improves students’understanding of complex and abstract mathematical physics concepts through dynamic and visual teaching methods.The teaching effect shows that Manim helps to enhance students’learning experience,improve teaching efficiency and effectiveness,and has a positive impact on students’active learning ability.The research in this paper can provide references and inspiration for the educational digitalization of higher education.展开更多
As one of the commonly used technologies in modern civil engineering,the construction technology is becoming more and more widely used with the continuous growth of building height.In the construction process of highr...As one of the commonly used technologies in modern civil engineering,the construction technology is becoming more and more widely used with the continuous growth of building height.In the construction process of highrise buildings,the deep foundation pit support provides the necessary stability for the foundation structure of the building project,and more effectively guarantees the quality of the project.Through the reasonable supporting structure,the deep foundation pit technology can effectively prevent the risk of soil collapse,foundation pit deformation and other risks,and improve the safety factor of the whole construction project.Especially in the high-rise buildings,the deep foundation pit support technology can consolidate the foundation for the long-term stability of the project,and significantly prolong the service life of the building.The continuous development of deep foundation pit construction technology is the inevitable demand of high-rise building construction,and also provides a powerful help for the development of civil engineering industry.Based on this,this paper focuses on the application of deep foundation pit construction technology in civil engineering construction.展开更多
To investigate the targets and mechanism of Hedysarum Multijugum Maxim(HMM)in treatment of bladder cancer(BC).Based on Traditional Chinese Medicine Systems Pharmacology(TCMSP)and gene databases,active substances and p...To investigate the targets and mechanism of Hedysarum Multijugum Maxim(HMM)in treatment of bladder cancer(BC).Based on Traditional Chinese Medicine Systems Pharmacology(TCMSP)and gene databases,active substances and potential targets of HMM were screened,and the HMM-active substances-targets-BC(HATB)regulatory network and PPI network were constructed.Hub targets were screened by Cytoscape.The main active substances and Hub targets were molecularly docked with AutoDock and visualized by PyMOL.12 Hub targets were screened.Molecular docking showed that active substances mainly acted on MAPK14,MAPK1 and CCND1.The bindings of calycosin to MAPK14,formononetin to MAPK14,and calycosin to CCND1 were stable.展开更多
The capture of atmospheric carbon dioxide by adsorbents is an important strategy to deal with the greenhouse effect.Compared with traditional CO_(2) adsorption materials like activated carbon,silica gel,and zeolite mo...The capture of atmospheric carbon dioxide by adsorbents is an important strategy to deal with the greenhouse effect.Compared with traditional CO_(2) adsorption materials like activated carbon,silica gel,and zeolite molecular sieves,covalent organic frameworks(COFs)have excellent thermal and chemical stabilities and can be produced in many different forms.Using their different possible construction units,ordered structures for specific applications can be produced,giving them broad prospects in fields such as gas storage.This review analyzes the different types of COFs that have been synthesized and their different methods of CO_(2) capture.It then discusses different ways to increase CO_(2) adsorption by changing the internal structure of COFs and modifying their surfaces.The limitations of COF-derived carbon materials in CO_(2) capture are reviewed and,finally,the key role of machine learning and computational simulation in improving CO_(2) adsorption is mentioned,and the current status and future possible uses of COFs are summarized.展开更多
Under the backdrop of“Carbon Peak and Carbon Neutrality”(dual carbon)goal in China,the methane-carbon dioxide reforming reaction has attracted considerable attention due to its environmental benefits of converting t...Under the backdrop of“Carbon Peak and Carbon Neutrality”(dual carbon)goal in China,the methane-carbon dioxide reforming reaction has attracted considerable attention due to its environmental benefits of converting two greenhouse gases(methane and carbon dioxide)into syngas and its promising industrial applications.Nickel(Ni)-based catalysts,with high catalytic activity,low cost,and abundant resources,are considered ideal candidates for industrial applications.In this article,three reaction kinetic models were briefly introduced,namely the Power-Law(PL)model,the Eley-Rideal(ER)model,and the Langmuir-Hinshelwood-Hougen-Watson(LHHW)model.Based on the LHHW model,the reaction kinetics and mechanisms of different catalytic systems were systematically discussed,including the properties of supports,the doping of noble metals and transition metals,the role of promoters,and the influence of the geometric and electronic structures of Ni on the reaction mechanism.Furthermore,the kinetics of carbon deposition and elimination on various catalysts were analyzed.Based on the reaction rate expressions for carbon elimination,the reasons for the high activity of transition metal iron(Fe)-doped catalysts and core-shell structured catalysts in carbon elimination were explained.Based on the detailed collation and comparative analysis of the reaction mechanisms and kinetic characteristics across diverse Ni-based catalytic systems,a theoretical guidance for the designing of high-performance catalysts was provided in this work.展开更多
Low-velocity impact tests are carried out to explore the energy absorption characteristics of bio-inspired lattices,mimicking the architecture of the marine sponge organism Euplectella aspergillum.These sea sponge-ins...Low-velocity impact tests are carried out to explore the energy absorption characteristics of bio-inspired lattices,mimicking the architecture of the marine sponge organism Euplectella aspergillum.These sea sponge-inspired lattice structures feature a square-grid 2D lattice with double diagonal bracings and are additively manufactured via digital light processing(DLP).The collapse strength and energy absorption capacity of sea sponge lattice structures are evaluated under various impact conditions and are compared to those of their constituent square-grid and double diagonal lattices.This study demonstrates that sea sponge lattices can achieve an 11-fold increase in energy absorption compared to the square-grid lattice,due to the stabilizing effect of the double diagonal bracings prompting the structure to collapse layer-bylayer under impact.By adjusting the thickness ratio in the sea sponge lattice,up to 76.7%increment in energy absorption is attained.It is also shown that sea-sponge lattices outperform well-established energy-absorbing materials of equal weight,such as hexagonal honeycombs,confirming their significant potential for impact mitigation.Additionally,this research highlights the enhancements in energy absorption achieved by adding a small amount(0.015 phr)of Multi-Walled Carbon Nanotubes(MWCNTs)to the photocurable resin,thus unlocking new possibilities for the design of innovative lightweight structures with multifunctional attributes.展开更多
With the development of sharded blockchains,high cross-shard rates and load imbalance have emerged as major challenges.Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates...With the development of sharded blockchains,high cross-shard rates and load imbalance have emerged as major challenges.Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates.Account partitioning based on historical transaction graphs is effective in reducing cross-shard rates but suffers from load imbalance and limited adaptability to dynamic workloads.Meanwhile,because of the coupling between consensus and execution,a target shard must receive both the partitioned transactions and the partitioned accounts before initiating consensus and execution.However,we observe that transaction partitioning and subsequent consensus do not require actual account data but only need to determine the relative partition order between shards.Therefore,we propose a novel sharded blockchain,called HATLedger,based on Hybrid Account and Transaction partitioning.First,HATLedger proposes building a future transaction graph to detect upcoming hotspot accounts and making more precise account partitioning to reduce transaction cross-shard rates.In the event of an impending overload,the source shard employs simulated partition transactions to specify the partition order across multiple target shards,thereby rapidly partitioning the pending transactions.The target shards can reach consensus on received transactions without waiting for account data.The source shard subsequently sends the account data to the corresponding target shards in the order specified by the previously simulated partition transactions.Based on real transaction history from Ethereum,we conducted extensive sharding scalability experiments.By maintaining low cross-shard rates and a relatively balanced load distribution,HATLedger achieves throughput improvements of 2.2x,1.9x,and 1.8x over SharPer,Shard Scheduler,and TxAllo,respectively,significantly enhancing efficiency and scalability.展开更多
The root-to-shoot(R/S)ratio is a critical indicator of the balance between root biomass and shoot biomass,representing the ecological strategies and adaptive responses of plants to environmental conditions.However,the...The root-to-shoot(R/S)ratio is a critical indicator of the balance between root biomass and shoot biomass,representing the ecological strategies and adaptive responses of plants to environmental conditions.However,the patterns of change in community R/S ratios during forest succession and their response to moisture levels across broad geographic gradients remains unclear.Based on forest biomass data from a national field inventory of 5,825 plots conducted across China between 2011 and 2015,this study looked into allocating biomass shoots and roots at the early,middle,and late stages of growth in plantations and succession in natural forests,and evaluated how moisture availability influences this allocation.The results revealed a significant decline in R/S ratios from early to late stages for both plantations and natural forests.Shoot and root biomass in plantations grew isometrically during the early and middle succession stages but shifted to allometric growth in the late stage,with the slope of the log-transformed shoot-root biomass relationship differing significantly across growth stages.Natural forests,in contrast,maintained isometric growth across successional stages,showing no significant variation in the slope of the log-transformed shoot-root biomass relationship.Environmental factors,particularly moisture levels,strongly influenced R/S ratios.Moisture levels significantly affected size-corrected R/S ratios,particularly in the middle stage of plantations and the early and middle stages of natural forests,supporting the hypothesis of optimal allocation.These findings suggest that in water-limited regions,forest management should prioritize drought-tolerant,deep-rooted native species,encourage mixed-species planting in the early stage,and reduce logging intensity in mature plantations.Conserving natural forests to maintain successional dynamics is essential for long-term ecological resilience.These findings emphasize the importance of balancing productivity with ecological sustainability by adapting practices to specific environments and forest types under climate change.展开更多
A method for correlating thermal light over a wide spectral range is proposed.A multi-wavelength pseudothermal source,prepared by projecting laser beams of multiple wavelengths(650 nm,635 nm,532 nm,and 473 nm)onto a m...A method for correlating thermal light over a wide spectral range is proposed.A multi-wavelength pseudothermal source,prepared by projecting laser beams of multiple wavelengths(650 nm,635 nm,532 nm,and 473 nm)onto a moving thin ground glass plate,is employed in a double-slit interference experiment.The ground glass plate induces random phase differences between light beams of different wavelengths passing through it.This initial random phase difference significantly influences the high-order intensity correlation functions of multi-wavelength thermal beams.Experimentally,second-order correlated interference patterns,including subwavelength interference,of pseudothermal beams with different wavelengths are observed in the intensity correlation measurements.This method facilitates applications of correlated thermal photons in quantum information processing and quantum imaging.展开更多
This study investigates the relationship between atmospheric stratification (i.e., static stability given by N^(2)) and the vertical energy transfer of stationary planetary waves, and further illustrates the underlyin...This study investigates the relationship between atmospheric stratification (i.e., static stability given by N^(2)) and the vertical energy transfer of stationary planetary waves, and further illustrates the underlying physical mechanism. Specifically, for the simplified case of constant stratospheric N^(2), the refractive index square of planetary waves has a theoretical tendency to increase first and then decrease with an increased N^(2), whereas the group velocity weakens. Mechanistically, this behavior can be understood as an intensified suppression of vertical isentropic surface displacement caused by meridional heat transport of planetary waves under strong N^(2) conditions. Observational analysis corroborates this finding, demonstrating a reduction in the vertical-propagation velocity of waves with increased N^(2). A linear, quasi- geostrophic, mid-latitude beta-plane model with a constant background westerly wind and a prescribed N^(2) applicable to the stratosphere is used to obtain analytic solutions. In this model, the planetary waves are initiated by steady energy influx from the lower boundary. The analysis indicates that under strong N^(2) conditions, the amplitude of planetary waves can be sufficiently increased by the effective energy convergence due to the slowing vertical energy transfer, resulting in a streamfunction response in this model that contains more energy. For N^(2) with a quasi-linear vertical variation, the results bear a resemblance to the constant case, except that the wave amplitude and oscillating frequency show some vertical variations.展开更多
Accelerated aging tests are widely used to rapidly evaluate the durability of materials,of which thermal-oxidative aging is the most common approach.To quantitatively predict the effects of multiple coupled factors,th...Accelerated aging tests are widely used to rapidly evaluate the durability of materials,of which thermal-oxidative aging is the most common approach.To quantitatively predict the effects of multiple coupled factors,this study takes polyamide66 reinforced with glass fiber(PA66-GF)as a model system and proposed a high-precision paradigm for coupled thermal-oxidative aging.By integrating Arrhenius-type reaction kinetics with oxygen diffusion,a predictive formula that holistically captures the nonlinear synergistic effects of multiple factors was developed,thereby overcoming the limitations of traditional single-variable models.A systematic evaluation of the stepwise improved formulas through nonlinear fitting showed that the coefficient of determination(R^(2))increased from 0.223 to 0.803,elucidating the fundamental reason why conventional approaches fail in quantitative prediction.These formulae were further embedded as physical constraints into a physics-informed neural network(PINN),which further enhanced the predictive performance,with the proposed formula achieving a peak R^(2)of 0.946.The results highlight that robust data fitting alone is insufficient;the decisive factor for the success of PINN lies in whether the embedded formula faithfully reflects the underlying physical mechanisms.When applied to polyamide 6 reinforced with glass fiber(PA6-GF),the Formula-constrained PINN maintained a high level of accuracy(R^(2)=0.916),demonstrating its strong cross-system generalizability.In summary,this work establishes a robust hybrid physics-machine learning framework that combines high accuracy with transferability for predicting the thermal-oxidative aging behavior of composite material systems.展开更多
Graph Neural Networks(GNNs)have proven highly effective for graph classification across diverse fields such as social networks,bioinformatics,and finance,due to their capability to learn complex graph structures.Howev...Graph Neural Networks(GNNs)have proven highly effective for graph classification across diverse fields such as social networks,bioinformatics,and finance,due to their capability to learn complex graph structures.However,despite their success,GNNs remain vulnerable to adversarial attacks that can significantly degrade their classification accuracy.Existing adversarial attack strategies primarily rely on label information to guide the attacks,which limits their applicability in scenarios where such information is scarce or unavailable.This paper introduces an innovative unsupervised attack method for graph classification,which operates without relying on label information,thereby enhancing its applicability in a broad range of scenarios.Specifically,our method first leverages a graph contrastive learning loss to learn high-quality graph embeddings by comparing different stochastic augmented views of the graphs.To effectively perturb the graphs,we then introduce an implicit estimator that measures the impact of various modifications on graph structures.The proposed strategy identifies and flips edges with the top-K highest scores,determined by the estimator,to maximize the degradation of the model’s performance.In addition,to defend against such attack,we propose a lightweight regularization-based defense mechanism that is specifically tailored to mitigate the structural perturbations introduced by our attack strategy.It enhances model robustness by enforcing embedding consistency and edge-level smoothness during training.We conduct experiments on six public TU graph classification datasets:NCI1,NCI109,Mutagenicity,ENZYMES,COLLAB,and DBLP_v1,to evaluate the effectiveness of our attack and defense strategies.Under an attack budget of 3,the maximum reduction in model accuracy reaches 6.67%on the Graph Convolutional Network(GCN)and 11.67%on the Graph Attention Network(GAT)across different datasets,indicating that our unsupervised method induces degradation comparable to state-of-the-art supervised attacks.Meanwhile,our defense achieves the highest accuracy recovery of 3.89%(GCN)and 5.00%(GAT),demonstrating improved robustness against structural perturbations.展开更多
Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and ...Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and reanalysis data(MERRA-2)from March 2007 to February 2015(eight years).The horizontal distribution reveals lower cirrus fraction values in the northern SCS and higher values in the southern region,with minima observed in March and April and maxima sequentially occurring in August(northern SCS,NSCS),September(middle SCS,MSCS),and December(southern SCS,SSCS).Vertically,the cirrus fraction peaks in summer and reaches its lowest levels in spring.Opaque cirrus dominates during summer in the NSCS and MSCS,comprising 53.6%and 55.9%,respectively,while the SSCS exhibits a higher frequency of opaque cirrus relative to other cloud types.Subvisible cirrus clouds have the lowest frequency year-round,whereas thin cirrus is most prominent in winter in the NSCS(46.3%)and in spring in the MSCS(45.3%).A case study from September 2021 further explores the influence of ice crystal habits on brightness temperature(BT)over the SCS.Simulations utilizing five ice crystal shapes from the ARTS DDA(Atmospheric Radiative Transfer Simulator Discrete Dipole Approximation)database and the RTTOV 12.4 radiative transfer model reveal that the 8-column-aggregate shape best represents BT in the NSCS and SSCS,while the large-block-aggregate shape performs better in the SSCS.展开更多
As the annual production of industrial hemp in China increases and its global market share grows,its multipurpose development has become an important trend for future development.The cellulose mass fraction of industr...As the annual production of industrial hemp in China increases and its global market share grows,its multipurpose development has become an important trend for future development.The cellulose mass fraction of industrial hemp was found to be as high as 59.36%by chemical composition determination,providing a possibility for the production of nanocellulose.To broaden the application field of industrial hemp,the 2,2,6,6-tetramethylpiperidine-1-oxyl radical(TEMPO)-oxidized nanocellulose(TCNF),sulfuric acid hydrolyzed nanocellulose(SCNC),and lignincontaining hydrolyzed nanocellulose(LCNC)were prepared by multi-step chemical purification pretreatment combined with TEMPO oxidation and sulfuric acid hydrolysis,respectively.They were characterized by Fourier transform infrared(FTIR)spectroscopy,X-ray diffraction(XRD),and thermogravimetric analysis(TGA).The effects of the sodium hypochlorite volume,sodium hydroxide mass fraction in the pretreatment process,and acid hydrolysis reaction time on the Zeta potential and particle size of the prepared nanocellulose were investigated.The absolute value of the Zeta potential of SCNC could reach 29.59 mV,and the particle size was small.The suspension could still maintain good dispersion stability after standing for 24.0 h under the same dispersion conditions.The basic functional group composition and crystal morphology of TCNF,SCNC,and LCNC did not change compared with the raw hemp,and the highest crystallinity increased from 24.6%to 68.1%.Due to the introduction of ester and carboxyl groups,the initial degradation temperature and the temperature at the maximum mass loss rate of the nanocellulose were lower than those of the raw hemp,but the nanocellulose still maintained the thermal stability for practical applications.展开更多
基金Sponsored by the Research Project of Higher Education(Undergraduate)Teaching Reform of Chongqing Municipal Education Commission in 2025(253272)Key Project of Educational Reform of Chongqing College of Humanities,Science&Technology(23CRKXJJG08).
文摘Focusing on the digital evaluation of classroom teaching quality in private colleges and universities,an indicator model of“teaching subject-teaching object-teaching effect”for the landscape architecture major of Chongqing College of Humanities,Science&Technology was constructed.By using methods such as Delphi,AHP,Likert and questionnaire survey,the teaching quality of 8 courses of landscape architecture major was evaluated.The results show that the average score of the indicators is 2.8776,indicating that the overall improvement space for the teaching quality of the sample professional courses is relatively large,and the key shortcomings are students’learning interest and initiative,the application and transformation of professional knowledge,as well as the cultivation of innovation and practical ability.The research verified the scientific nature and discrimination of the model,and put forward suggestions for the precise improvement of classroom atmosphere,assignment design and ability cultivation driven by data,thereby providing a replicable model for the digital evaluation and teaching quality improvement of engineering majors in private colleges and universities.
基金Supported by the Project of Design of Anti-corrosion and Anti-fouling Solutions for Offshore Wind Power Water-Cooled Systems(No.E428161)the National Natural Science Foundation of China(No.42176047)。
文摘Water-cooled system have significantly enhanced the power generation efficiency of offshore wind turbines.However,these innovative systems are susceptible to substantial biological fouling,maintenance challenges,and high upkeep costs.Therefore,the development of a specialized front-end filter tailored for direct current water-cooled system is importance.This involves the integration of dimensionally stable anode(DSA)and nickel alloy cathode,valued for their corrosion resistance in seawater,into a novel front-end filter system for Water-cooled applications.This system has the dual capability of generating hydrogen and chlorine for self-cleaning purposes.Implementing a flushing pulse electrolysis mode,it effectively mitigates electrode failure induced by cathodic calcium and magnesium deposition,thereby significantly prolonging electrode lifespan.Laboratory tests comprising system assembly and performance evaluations were conducted,with the system programmed to operate for 5 minutes every 24 hours under continuous flushing by natural seawater to simulate real-world conditions.After more than 11 months of continuous flushing,observations reveal that the DSA mesh and nickel alloy mesh maintain intact structural integrity and normal functioning.Subsequent 1꞉1 physical prototype Sea trial further validated the soundness of the system design and electrolytic control parameters.
基金the National Natural Science Foundation of China(Nos.51576132,52076140)the National Science and Technology Major Project on Important Infectious Diseases Prevention and Control(2018ZX10734404).
文摘The damage caused by thermal stress during rewarming vitrified biosamples is one of the major obstacles for clinical purposes.Magnetic warming is a highly effective approach to overcome this hurdle and can achieve rapid and spatially homogeneous heating.The current research investigates the effects of magnetic warming on the histological and biomechanical properties of the vitrified umbilical arteries(UAs)through experiments and simulation.The results of experiments show that,for the case of magnetic warming comparing with the conventional water bath,magnetic warming presents better preservation of extracellular matrix(ECM),collagen fibers,elastic fibers,and muscle fibers of the umbilical artery.There is no significant difference between magnetothermal and fresh UAs(p>0.05)in the elastic modulus and the ultimate stress.The theoretical results reveal that the maximum temperature difference Tmax inside the biosample is 1.117±0.649℃,and the maximum thermal stressmax is 0.026±0.016 MPa.However,for the case of conventional water bath,Tmax is 32.342±0.967℃andmax is 1.453±0.047 MPa.Moreover,we have arrived at the same conclusion by simulation as theoretical calculation have.Therefore,magnetic warming can effectively reduce the thermal stress damage of biological samples during the warming period due to more uniform and rapid warming.These results confirm that magnetothermal can significantly improve the mechanical properties of large size cryopreserved tissues or organs such as UAs.
基金Supported by Foundation for Humanities and Social Sciences Research Planning of Ministry of Education of China in 2019(19YJA630058)
文摘Firstly,the behavior of marine science and technology talents,such as scientific research,innovation,agglomeration and flow behavior,was analyzed,and then the problems in the training of marine science and technology talents were discussed.Finally,the training ways of marine science and technology talents were proposed.
基金National Natural Science Foundation of China Key Project(No.42050103)Higher Education Disciplinary Innovation Program(No.B25052)+2 种基金the Guangdong Pearl River Talent Program Innovative and Entrepreneurial Team Project(No.2021ZT09H399)the Ministry of Education’s Frontiers Science Center for Deep-Time Digital Earth(DDE)(No.2652023001)Geological Survey Project of China Geological Survey(DD20240206201)。
文摘Since the beginning of the 21st century,advances in big data and artificial intelligence have driven a paradigm shift in the geosciences,moving the field from qualitative descriptions toward quantitative analysis,from observing phenomena to uncovering underlying mechanisms,from regional-scale investigations to global perspectives,and from experience-based inference toward data-and model-enabled intelligent prediction.AlphaEarth Foundations(AEF)is a next-generation geospatial intelligence platform that addresses these changes by introducing a unified 64-dimensional shared embedding space,enabling-for the first time-standardized representation and seamless integration of 12 distinct types of Earth observation data,including optical,radar,and lidar.This framework significantly improves data assimilation efficiency and resolves the persistent problem of“data silos”in geoscience research.AEF is helping redefine research methodologies and fostering breakthroughs,particularly in quantitative Earth system science.This paper systematically examines how AEF’s innovative architecture-featuring multi-source data fusion,high-dimensional feature representation learning,and a scalable computational framework-facilitates intelligent,precise,and realtime data-driven geoscientific research.Using case studies from resource and environmental applications,we demonstrate AEF’s broad potential and identify emerging innovation needs.Our findings show that AEF not only enhances the efficiency of solving traditional geoscientific problems but also stimulates novel research directions and methodological approaches.
基金supported by the Jiangsu Provincial Science and Technology Project Basic Research Program(Natural Science Foundation of Jiangsu Province)(No.BK20211283).
文摘NJmat is a user-friendly,data-driven machine learning interface designed for materials design and analysis.The platform integrates advanced computational techniques,including natural language processing(NLP),large language models(LLM),machine learning potentials(MLP),and graph neural networks(GNN),to facili-tate materials discovery.The platform has been applied in diverse materials research areas,including perovskite surface design,catalyst discovery,battery materials screening,structural alloy design,and molecular informatics.By automating feature selection,predictive modeling,and result interpretation,NJmat accelerates the development of high-performance materials across energy storage,conversion,and structural applications.Additionally,NJmat serves as an educational tool,allowing students and researchers to apply machine learning techniques in materials science with minimal coding expertise.Through automated feature extraction,genetic algorithms,and interpretable machine learning models,NJmat simplifies the workflow for materials informatics,bridging the gap between AI and experimental materials research.The latest version(available at https://figshare.com/articles/software/NJmatML/24607893(accessed on 01 January 2025))enhances its functionality by incorporating NJmatNLP,a module leveraging language models like MatBERT and those based on Word2Vec to support materials prediction tasks.By utilizing clustering and cosine similarity analysis with UMAP visualization,NJmat enables intuitive exploration of materials datasets.While NJmat primarily focuses on structure-property relationships and the discovery of novel chemistries,it can also assist in optimizing processing conditions when relevant parameters are included in the training data.By providing an accessible,integrated environment for machine learning-driven materials discovery,NJmat aligns with the objectives of the Materials Genome Initiative and promotes broader adoption of AI techniques in materials science.
基金supported by the Teaching Reform Research Project of Shaanxi University of Science&Technology(23Y083)the Project of National University Association for Mathematical Methods in Physics(JZW-23-SL-02)+3 种基金the Graduate Course Construction Project of Shaanxi University of Science&Technology(KC2024Y03)the 2024 National Higher Education University Physics Reform Research Project(2024PR064)the Teaching Reform Research Project of the International Office of Shaanxi University of Science&Technology(YB202410)Graduate Education and Teaching Reform Research Project of Shaanxi University of Science&Technology(JG2025Y18).
文摘With the development of educational digitalization,how to effectively apply digital animation technology to traditional classroom teaching has become an urgent problem to be solved.This study explores the application of Manim in the course of Mathematical Methods for Physics.Taking the visualization of Fourier series,complex numbers,and other content as examples,it improves students’understanding of complex and abstract mathematical physics concepts through dynamic and visual teaching methods.The teaching effect shows that Manim helps to enhance students’learning experience,improve teaching efficiency and effectiveness,and has a positive impact on students’active learning ability.The research in this paper can provide references and inspiration for the educational digitalization of higher education.
文摘As one of the commonly used technologies in modern civil engineering,the construction technology is becoming more and more widely used with the continuous growth of building height.In the construction process of highrise buildings,the deep foundation pit support provides the necessary stability for the foundation structure of the building project,and more effectively guarantees the quality of the project.Through the reasonable supporting structure,the deep foundation pit technology can effectively prevent the risk of soil collapse,foundation pit deformation and other risks,and improve the safety factor of the whole construction project.Especially in the high-rise buildings,the deep foundation pit support technology can consolidate the foundation for the long-term stability of the project,and significantly prolong the service life of the building.The continuous development of deep foundation pit construction technology is the inevitable demand of high-rise building construction,and also provides a powerful help for the development of civil engineering industry.Based on this,this paper focuses on the application of deep foundation pit construction technology in civil engineering construction.
基金2025 Open Experimental Special Fund of Beijing Institute of Technology, “Applications and Practices of R Language in Bioinformatics”。
文摘To investigate the targets and mechanism of Hedysarum Multijugum Maxim(HMM)in treatment of bladder cancer(BC).Based on Traditional Chinese Medicine Systems Pharmacology(TCMSP)and gene databases,active substances and potential targets of HMM were screened,and the HMM-active substances-targets-BC(HATB)regulatory network and PPI network were constructed.Hub targets were screened by Cytoscape.The main active substances and Hub targets were molecularly docked with AutoDock and visualized by PyMOL.12 Hub targets were screened.Molecular docking showed that active substances mainly acted on MAPK14,MAPK1 and CCND1.The bindings of calycosin to MAPK14,formononetin to MAPK14,and calycosin to CCND1 were stable.
文摘The capture of atmospheric carbon dioxide by adsorbents is an important strategy to deal with the greenhouse effect.Compared with traditional CO_(2) adsorption materials like activated carbon,silica gel,and zeolite molecular sieves,covalent organic frameworks(COFs)have excellent thermal and chemical stabilities and can be produced in many different forms.Using their different possible construction units,ordered structures for specific applications can be produced,giving them broad prospects in fields such as gas storage.This review analyzes the different types of COFs that have been synthesized and their different methods of CO_(2) capture.It then discusses different ways to increase CO_(2) adsorption by changing the internal structure of COFs and modifying their surfaces.The limitations of COF-derived carbon materials in CO_(2) capture are reviewed and,finally,the key role of machine learning and computational simulation in improving CO_(2) adsorption is mentioned,and the current status and future possible uses of COFs are summarized.
基金Supported by Innovation Capability Support Program of Shaanxi(2024RS-CXTD-53,2024ZC-KJXX-096)the Key R&D Program of Shaanxi Province(2022QCY-LL-69)Xi’an Science and Technology Project(24GXFW0089)。
文摘Under the backdrop of“Carbon Peak and Carbon Neutrality”(dual carbon)goal in China,the methane-carbon dioxide reforming reaction has attracted considerable attention due to its environmental benefits of converting two greenhouse gases(methane and carbon dioxide)into syngas and its promising industrial applications.Nickel(Ni)-based catalysts,with high catalytic activity,low cost,and abundant resources,are considered ideal candidates for industrial applications.In this article,three reaction kinetic models were briefly introduced,namely the Power-Law(PL)model,the Eley-Rideal(ER)model,and the Langmuir-Hinshelwood-Hougen-Watson(LHHW)model.Based on the LHHW model,the reaction kinetics and mechanisms of different catalytic systems were systematically discussed,including the properties of supports,the doping of noble metals and transition metals,the role of promoters,and the influence of the geometric and electronic structures of Ni on the reaction mechanism.Furthermore,the kinetics of carbon deposition and elimination on various catalysts were analyzed.Based on the reaction rate expressions for carbon elimination,the reasons for the high activity of transition metal iron(Fe)-doped catalysts and core-shell structured catalysts in carbon elimination were explained.Based on the detailed collation and comparative analysis of the reaction mechanisms and kinetic characteristics across diverse Ni-based catalytic systems,a theoretical guidance for the designing of high-performance catalysts was provided in this work.
基金supported by the Khalifa University of Science and Technology internal grants(Nos.2021-CIRA-109,2020-CIRA-007,and 2020-CIRA-024).
文摘Low-velocity impact tests are carried out to explore the energy absorption characteristics of bio-inspired lattices,mimicking the architecture of the marine sponge organism Euplectella aspergillum.These sea sponge-inspired lattice structures feature a square-grid 2D lattice with double diagonal bracings and are additively manufactured via digital light processing(DLP).The collapse strength and energy absorption capacity of sea sponge lattice structures are evaluated under various impact conditions and are compared to those of their constituent square-grid and double diagonal lattices.This study demonstrates that sea sponge lattices can achieve an 11-fold increase in energy absorption compared to the square-grid lattice,due to the stabilizing effect of the double diagonal bracings prompting the structure to collapse layer-bylayer under impact.By adjusting the thickness ratio in the sea sponge lattice,up to 76.7%increment in energy absorption is attained.It is also shown that sea-sponge lattices outperform well-established energy-absorbing materials of equal weight,such as hexagonal honeycombs,confirming their significant potential for impact mitigation.Additionally,this research highlights the enhancements in energy absorption achieved by adding a small amount(0.015 phr)of Multi-Walled Carbon Nanotubes(MWCNTs)to the photocurable resin,thus unlocking new possibilities for the design of innovative lightweight structures with multifunctional attributes.
基金funded by the National Key Research and Development Program of China(Grant No.2024YFE0209000)the NSFC(Grant No.U23B2019)。
文摘With the development of sharded blockchains,high cross-shard rates and load imbalance have emerged as major challenges.Account partitioning based on hashing and real-time load faces the issue of high cross-shard rates.Account partitioning based on historical transaction graphs is effective in reducing cross-shard rates but suffers from load imbalance and limited adaptability to dynamic workloads.Meanwhile,because of the coupling between consensus and execution,a target shard must receive both the partitioned transactions and the partitioned accounts before initiating consensus and execution.However,we observe that transaction partitioning and subsequent consensus do not require actual account data but only need to determine the relative partition order between shards.Therefore,we propose a novel sharded blockchain,called HATLedger,based on Hybrid Account and Transaction partitioning.First,HATLedger proposes building a future transaction graph to detect upcoming hotspot accounts and making more precise account partitioning to reduce transaction cross-shard rates.In the event of an impending overload,the source shard employs simulated partition transactions to specify the partition order across multiple target shards,thereby rapidly partitioning the pending transactions.The target shards can reach consensus on received transactions without waiting for account data.The source shard subsequently sends the account data to the corresponding target shards in the order specified by the previously simulated partition transactions.Based on real transaction history from Ethereum,we conducted extensive sharding scalability experiments.By maintaining low cross-shard rates and a relatively balanced load distribution,HATLedger achieves throughput improvements of 2.2x,1.9x,and 1.8x over SharPer,Shard Scheduler,and TxAllo,respectively,significantly enhancing efficiency and scalability.
基金supported by the China National Science Foundation(No.42130506,42071031)the Special Technology Innovation Fund of Carbon Peak and Carbon Neutrality in Jiangsu Province(BK20231515)+1 种基金the Spanish Government grant PID2022-140808NB-I00 funded by MICIU/AEI/https://doi.org/10.13039/501100011033the Catalan Government grants SGR 2021-1333 and AGAUR2023 CLIMA 00118.
文摘The root-to-shoot(R/S)ratio is a critical indicator of the balance between root biomass and shoot biomass,representing the ecological strategies and adaptive responses of plants to environmental conditions.However,the patterns of change in community R/S ratios during forest succession and their response to moisture levels across broad geographic gradients remains unclear.Based on forest biomass data from a national field inventory of 5,825 plots conducted across China between 2011 and 2015,this study looked into allocating biomass shoots and roots at the early,middle,and late stages of growth in plantations and succession in natural forests,and evaluated how moisture availability influences this allocation.The results revealed a significant decline in R/S ratios from early to late stages for both plantations and natural forests.Shoot and root biomass in plantations grew isometrically during the early and middle succession stages but shifted to allometric growth in the late stage,with the slope of the log-transformed shoot-root biomass relationship differing significantly across growth stages.Natural forests,in contrast,maintained isometric growth across successional stages,showing no significant variation in the slope of the log-transformed shoot-root biomass relationship.Environmental factors,particularly moisture levels,strongly influenced R/S ratios.Moisture levels significantly affected size-corrected R/S ratios,particularly in the middle stage of plantations and the early and middle stages of natural forests,supporting the hypothesis of optimal allocation.These findings suggest that in water-limited regions,forest management should prioritize drought-tolerant,deep-rooted native species,encourage mixed-species planting in the early stage,and reduce logging intensity in mature plantations.Conserving natural forests to maintain successional dynamics is essential for long-term ecological resilience.These findings emphasize the importance of balancing productivity with ecological sustainability by adapting practices to specific environments and forest types under climate change.
基金supported by the National Natural Science Foundation of China(Grant Nos.62105278 and 11674273)the Natural Science Foundation of Shandong Province(Grant No.ZR2023MA015)。
文摘A method for correlating thermal light over a wide spectral range is proposed.A multi-wavelength pseudothermal source,prepared by projecting laser beams of multiple wavelengths(650 nm,635 nm,532 nm,and 473 nm)onto a moving thin ground glass plate,is employed in a double-slit interference experiment.The ground glass plate induces random phase differences between light beams of different wavelengths passing through it.This initial random phase difference significantly influences the high-order intensity correlation functions of multi-wavelength thermal beams.Experimentally,second-order correlated interference patterns,including subwavelength interference,of pseudothermal beams with different wavelengths are observed in the intensity correlation measurements.This method facilitates applications of correlated thermal photons in quantum information processing and quantum imaging.
基金supported by the National Natural Science Foundation of China(Grant No.42261134532,42405059,and U2342212)。
文摘This study investigates the relationship between atmospheric stratification (i.e., static stability given by N^(2)) and the vertical energy transfer of stationary planetary waves, and further illustrates the underlying physical mechanism. Specifically, for the simplified case of constant stratospheric N^(2), the refractive index square of planetary waves has a theoretical tendency to increase first and then decrease with an increased N^(2), whereas the group velocity weakens. Mechanistically, this behavior can be understood as an intensified suppression of vertical isentropic surface displacement caused by meridional heat transport of planetary waves under strong N^(2) conditions. Observational analysis corroborates this finding, demonstrating a reduction in the vertical-propagation velocity of waves with increased N^(2). A linear, quasi- geostrophic, mid-latitude beta-plane model with a constant background westerly wind and a prescribed N^(2) applicable to the stratosphere is used to obtain analytic solutions. In this model, the planetary waves are initiated by steady energy influx from the lower boundary. The analysis indicates that under strong N^(2) conditions, the amplitude of planetary waves can be sufficiently increased by the effective energy convergence due to the slowing vertical energy transfer, resulting in a streamfunction response in this model that contains more energy. For N^(2) with a quasi-linear vertical variation, the results bear a resemblance to the constant case, except that the wave amplitude and oscillating frequency show some vertical variations.
基金financially supported by the National Natural Science Foundation of China(No.22473032)。
文摘Accelerated aging tests are widely used to rapidly evaluate the durability of materials,of which thermal-oxidative aging is the most common approach.To quantitatively predict the effects of multiple coupled factors,this study takes polyamide66 reinforced with glass fiber(PA66-GF)as a model system and proposed a high-precision paradigm for coupled thermal-oxidative aging.By integrating Arrhenius-type reaction kinetics with oxygen diffusion,a predictive formula that holistically captures the nonlinear synergistic effects of multiple factors was developed,thereby overcoming the limitations of traditional single-variable models.A systematic evaluation of the stepwise improved formulas through nonlinear fitting showed that the coefficient of determination(R^(2))increased from 0.223 to 0.803,elucidating the fundamental reason why conventional approaches fail in quantitative prediction.These formulae were further embedded as physical constraints into a physics-informed neural network(PINN),which further enhanced the predictive performance,with the proposed formula achieving a peak R^(2)of 0.946.The results highlight that robust data fitting alone is insufficient;the decisive factor for the success of PINN lies in whether the embedded formula faithfully reflects the underlying physical mechanisms.When applied to polyamide 6 reinforced with glass fiber(PA6-GF),the Formula-constrained PINN maintained a high level of accuracy(R^(2)=0.916),demonstrating its strong cross-system generalizability.In summary,this work establishes a robust hybrid physics-machine learning framework that combines high accuracy with transferability for predicting the thermal-oxidative aging behavior of composite material systems.
基金funded by the National Key Research and Development Program of China(Grant No.2024YFE0209000)the NSFC(Grant No.U23B2019).
文摘Graph Neural Networks(GNNs)have proven highly effective for graph classification across diverse fields such as social networks,bioinformatics,and finance,due to their capability to learn complex graph structures.However,despite their success,GNNs remain vulnerable to adversarial attacks that can significantly degrade their classification accuracy.Existing adversarial attack strategies primarily rely on label information to guide the attacks,which limits their applicability in scenarios where such information is scarce or unavailable.This paper introduces an innovative unsupervised attack method for graph classification,which operates without relying on label information,thereby enhancing its applicability in a broad range of scenarios.Specifically,our method first leverages a graph contrastive learning loss to learn high-quality graph embeddings by comparing different stochastic augmented views of the graphs.To effectively perturb the graphs,we then introduce an implicit estimator that measures the impact of various modifications on graph structures.The proposed strategy identifies and flips edges with the top-K highest scores,determined by the estimator,to maximize the degradation of the model’s performance.In addition,to defend against such attack,we propose a lightweight regularization-based defense mechanism that is specifically tailored to mitigate the structural perturbations introduced by our attack strategy.It enhances model robustness by enforcing embedding consistency and edge-level smoothness during training.We conduct experiments on six public TU graph classification datasets:NCI1,NCI109,Mutagenicity,ENZYMES,COLLAB,and DBLP_v1,to evaluate the effectiveness of our attack and defense strategies.Under an attack budget of 3,the maximum reduction in model accuracy reaches 6.67%on the Graph Convolutional Network(GCN)and 11.67%on the Graph Attention Network(GAT)across different datasets,indicating that our unsupervised method induces degradation comparable to state-of-the-art supervised attacks.Meanwhile,our defense achieves the highest accuracy recovery of 3.89%(GCN)and 5.00%(GAT),demonstrating improved robustness against structural perturbations.
基金supported by the National Natural Science Foundation of China(Grant Nos.42027804,41775026,and 41075012)。
文摘Cirrus clouds play a crucial role in the energy balance of the Earth-atmosphere system.We investigated the spatiotemporal variations of cirrus over the South China Sea(SCS)using satellite data(MOD08,MYD08,CALIPSO)and reanalysis data(MERRA-2)from March 2007 to February 2015(eight years).The horizontal distribution reveals lower cirrus fraction values in the northern SCS and higher values in the southern region,with minima observed in March and April and maxima sequentially occurring in August(northern SCS,NSCS),September(middle SCS,MSCS),and December(southern SCS,SSCS).Vertically,the cirrus fraction peaks in summer and reaches its lowest levels in spring.Opaque cirrus dominates during summer in the NSCS and MSCS,comprising 53.6%and 55.9%,respectively,while the SSCS exhibits a higher frequency of opaque cirrus relative to other cloud types.Subvisible cirrus clouds have the lowest frequency year-round,whereas thin cirrus is most prominent in winter in the NSCS(46.3%)and in spring in the MSCS(45.3%).A case study from September 2021 further explores the influence of ice crystal habits on brightness temperature(BT)over the SCS.Simulations utilizing five ice crystal shapes from the ARTS DDA(Atmospheric Radiative Transfer Simulator Discrete Dipole Approximation)database and the RTTOV 12.4 radiative transfer model reveal that the 8-column-aggregate shape best represents BT in the NSCS and SSCS,while the large-block-aggregate shape performs better in the SSCS.
基金Fundamental Research Funds for the Central Universities of China(No.2232024G-01)Textile Vision Basic Research Program,China(No.J202305)。
文摘As the annual production of industrial hemp in China increases and its global market share grows,its multipurpose development has become an important trend for future development.The cellulose mass fraction of industrial hemp was found to be as high as 59.36%by chemical composition determination,providing a possibility for the production of nanocellulose.To broaden the application field of industrial hemp,the 2,2,6,6-tetramethylpiperidine-1-oxyl radical(TEMPO)-oxidized nanocellulose(TCNF),sulfuric acid hydrolyzed nanocellulose(SCNC),and lignincontaining hydrolyzed nanocellulose(LCNC)were prepared by multi-step chemical purification pretreatment combined with TEMPO oxidation and sulfuric acid hydrolysis,respectively.They were characterized by Fourier transform infrared(FTIR)spectroscopy,X-ray diffraction(XRD),and thermogravimetric analysis(TGA).The effects of the sodium hypochlorite volume,sodium hydroxide mass fraction in the pretreatment process,and acid hydrolysis reaction time on the Zeta potential and particle size of the prepared nanocellulose were investigated.The absolute value of the Zeta potential of SCNC could reach 29.59 mV,and the particle size was small.The suspension could still maintain good dispersion stability after standing for 24.0 h under the same dispersion conditions.The basic functional group composition and crystal morphology of TCNF,SCNC,and LCNC did not change compared with the raw hemp,and the highest crystallinity increased from 24.6%to 68.1%.Due to the introduction of ester and carboxyl groups,the initial degradation temperature and the temperature at the maximum mass loss rate of the nanocellulose were lower than those of the raw hemp,but the nanocellulose still maintained the thermal stability for practical applications.
