Stevioside, extracted from the leaves of Stevia rebaudiana Bertoni, is a natural, high intensity, lowcaloric sweetener with wide therapeutic activities. Conventional stevioside extraction methodologies involve the use...Stevioside, extracted from the leaves of Stevia rebaudiana Bertoni, is a natural, high intensity, lowcaloric sweetener with wide therapeutic activities. Conventional stevioside extraction methodologies involve the use of non-green solvents, supercritical fluids, microwaves, etc., however, all these processes are expensive, time-consuming and eco-unfriendly. Therefore an alternative process is desired for the isolation of stevioside. In this study, a novel enzyme-mediated extraction (EME) method has been developed. The dry stevia leaves were pre-treated with hydrolytic enzymes aided by transition metal salts (FeCl3). This was followed by pressurized hot water extraction (PHWE) to release stevioside. The crude extract was purified and clarified through multi-stage membrane filtration. The results confirm that metal salt-assisted cellulase pre-treatment enhanced the yield of stevioside to 72%, with 98% purity, which was higher in comparison to existing methods. Thus, the methodology developed establishes a simple, “green”, enzyme-mediated process for the efficient isolation of stevioside under economical and eco-friendly conditions.展开更多
Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and...Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.展开更多
The recycling of neptunium(Np)from nuclear wastes is crucial for the sustainable development of nuclear energy,yet it is still a challenging task owing to the complexity of Np chemistry.Precise control of oxidation st...The recycling of neptunium(Np)from nuclear wastes is crucial for the sustainable development of nuclear energy,yet it is still a challenging task owing to the complexity of Np chemistry.Precise control of oxidation state is highly desirable for the effective recovery of Np.In this study,we report an innovative strategy for Np recovery through in-situ coordination and reduction of Np(Ⅴ)in a biphasic extraction system.By leveraging the synergistic effects of coordination by a P=O donating ligand(trialkyl phosphine oxide,TRPO)and reduction by hydroquinone(HQ)in the organic phase,efficient Np(Ⅴ)-to-Np(Ⅳ)conversion and high distribution ratio(D)of Np were achieved in a single extraction contact.The reduction mechanism of Np was elucidated through spectroscopic and theoretical analyses.This work enriches the redox chemistry of Np and provides a novel pathway for Np recovery in advanced nuclear fuel cycles.展开更多
The lack of macro-continuity and mechanical strength of covalent organic frameworks(COFs)has significantly limited their practical applications.Here,we propose an“alcohol-triggered defect cleavage”strategy to precis...The lack of macro-continuity and mechanical strength of covalent organic frameworks(COFs)has significantly limited their practical applications.Here,we propose an“alcohol-triggered defect cleavage”strategy to precisely regulate the growth and stacking of COF grains through a moderate reversed Schiff base reaction,realizing the direct synthesis of COF nanofibers(CNFs)with high aspect ratio(L/D=103.05)and long length(>20μm).An individual CNF exhibits a biomimetic scale-like architecture,achieving superior flexibility and fatigue resistance under dynamic bending via a multiscale stress dissipation mechanism.Taking advantages of these structural features,we engineer CNF aerogels(CNF-As)with programmable porous structures(e.g.,honeycomb,lamellar,isotropic)via directional ice-template methodology.CNF-As demonstrate 100%COF content,high specific surface area(396.15 m^(2)g^(-1))and superelasticity(~0%elastic deformation after 500 compression cycles at 50%strain),outperforming most COF-based counterparts.Compared with the conventional COF aerogels,the unique structural features of CNF-A enable it to perform outstandingly in uranium extraction,with an 11.72-fold increment in adsorption capacity(920.12 mg g^(-1))and adsorption rate(89.9%),and a 2.48-fold improvement in selectivity(U/V=2.31).This study provides a direct strategy for the development of next-generation COF materials with outstanding functionality and structural robustness.展开更多
As an important class of phenanthroline derivatives containing soft N and hard O donor atoms,the laborious syntheses of unsymmetrical 1,10-phenanthroline-derived diamide ligands(DAPhen) have hindered its extensive stu...As an important class of phenanthroline derivatives containing soft N and hard O donor atoms,the laborious syntheses of unsymmetrical 1,10-phenanthroline-derived diamide ligands(DAPhen) have hindered its extensive study.In this work,we first report a convenient synthetic method for the construction of DAPhen using Friedländer reaction by two facile steps(vs.previous 12 steps).A variety of DAPhen ligands are readily available,especially unsymmetrical ones,which give us a platform to systematically study the substituent effect on f-block elements extraction performance.The performance of unsymmetrical extractants is experimentally confirmed to falls between that of their corresponding symmetrical extractants by extracting UO_(2)^(2+) as the representative f-block element.This work provides a direct and versatile method to synthesize symmetrical and unsymmetrical DAPhen,which paves way for the investigations on their coordination properties with metal ions and other applications.展开更多
This article presents a new synergistic extraction system composed of Cyanex 272(C272,bis(2,4,4-trimethylpentyl)phosphinic acid)and iso-octanol for Sc_(3+) separation.The proposed synergistic system possessed an Sc^(3...This article presents a new synergistic extraction system composed of Cyanex 272(C272,bis(2,4,4-trimethylpentyl)phosphinic acid)and iso-octanol for Sc_(3+) separation.The proposed synergistic system possessed an Sc^(3+) extraction efficiency of 93.5%and a back-extraction efficiency of 82.7%,with selectivity coefficients of β_(Sc/Fe)=459 and β_(Sc/Al)=4241,which are considerably higher as compared to the current extraction systems.The extraction mechanism was studied and interpreted.The enhanced extraction efficiency is attributed to the increased hydrophobicity of the ternary complex,whereas the back-extraction efficiency can be ascribed to the attenuated stability of the complex.C272 and C272–iso-octanol systems also possess considerable surface activity,which is beneficial for the phase separation in solvent extraction.Based on the solvent extraction results,a preliminary study was conducted on polymer inclusion membranes(PIMs)using the binary system for Sc^(3+) separation to avoid the formation of the third phase,achieving an optimal initial flux of PIM of 6.71×10^(−4)mol·m^(−2)·h^(−1).Our results provide valuable information on highly efficient Sc^(3+) separation,and the study on PIM extraction has shown a green alternative to solvent extraction.展开更多
The extraction of uranium from seawater via membrane adsorption is a promising strategy for ensuring a long-term supply of uranium and the sustainability of nuclear energy.However,this approach has been hindered by th...The extraction of uranium from seawater via membrane adsorption is a promising strategy for ensuring a long-term supply of uranium and the sustainability of nuclear energy.However,this approach has been hindered by the longstanding challenge of identifying sustainable membrane materials.In response,we propose a prototypal hybridization strategy to design a novel series of aminated conjugated microporous polymer(CMPN)@collagen fiber membrane(COLM).These sustainable and low-cost membrane materials allow a rapid and high-affinity kinetic to capture 90%of the uranium in just 30 min from 50 ppm with a high selectivity of Kd>105 mL·g^(−1).They also afford a robustly reusable adsorption capacity as high as 345 mg·g^(−1)that could harvest 1.61 mg·g^(−1)of uranium in a short 7-day real marine engineering in Fujian Province,even though suffered from very low uranium concentration of 3.29μg·L^(−1)and tough influence of salts such as 10.77 g·L^(−1)of Na^(+),1.75μg·L^(−1)of VO_(3)^(−)etc.in the rough seas.The structural evidence from both experimental and theoretical studies confirmed the formation of favorable chelating motifs from the amino group on CMPN-COLM,and the intensification by the synergistic effect from the size-sieving action of CMPN and the capillary inflow effect of COLM.展开更多
Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targ...Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targets,complex backgrounds,and small objects in remote sensing.Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot.Therefore,we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture,specifically optimized for the characteristics of large target scale variations,diverse orientations,and numerous small objects in remote sensing images.Our innovations lie in two main aspects:First,a dynamic snake convolution(DSC)is introduced into the backbone network to enhance the model’s feature extraction capability for oriented targets.Second,an innovative focusing-diffusion module is designed in the feature fusion neck to effectively integrate multi-scale feature information.Finally,we introduce Layer-Adaptive Sparsity for magnitude-based Pruning(LASP)method to perform lightweight network pruning to better complete tasks in resource-constrained scenarios.Experimental results on the lightweight platform Orin demonstrate that the proposed method significantly outperforms the original YOLOv8s model in oriented remote sensing object detection tasks,and achieves comparable or superior performance to state-of-the-art methods on three authoritative remote sensing datasets(DOTA v1.0,DOTA v1.5,and HRSC2016).展开更多
Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representati...Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development.展开更多
To ease the scarcity of lithium(Li)resource and cut down on environmental pollution,an efficient,selective,inexpensive and sustainable Li recycling process from waste batteries is needed,which is yet to be achieved.He...To ease the scarcity of lithium(Li)resource and cut down on environmental pollution,an efficient,selective,inexpensive and sustainable Li recycling process from waste batteries is needed,which is yet to be achieved.Here,we report a low-potential photoelectrochemical(PEC)system that selectively and efficiently extracts Li metals from multi-cation electrolytes under 1 sun illumination.Based on the difference of redox potential,we can get rid of the disturbance of other cations(i.e.,Fe,Co and Ni ions)by a bias-free PEC device to realize the extraction of high-purity Li metals on a coplanar Si-based photocathode-TiO_(2) photoanode tandem device at 2 V of applied bias(far less than the redox potentials of Li^(+)/Li).In such system,the extraction rate of Li metals(purity>99.5%)exceeds 1.35 g h^(-1)m^(-2)with 90%of Faradaic efficiency.Long-term experiments,different electrode/electrolyte tests,and various price assessments further demonstrate the stability,compatibility and economy of PEC extraction system,enabling a solar-driven pathway for the recycling of critical metal resources.展开更多
As a key low-carbon energy source,nuclear power plays a vital role in the global transition toward sustainable energy.Photocatalytic uranium extraction from seawater(UES)offers a promising solution to ensure long-term...As a key low-carbon energy source,nuclear power plays a vital role in the global transition toward sustainable energy.Photocatalytic uranium extraction from seawater(UES)offers a promising solution to ensure long-term uranium supply but is challenged by ultra-low uranium concentrations and ion interference.To overcome these issues,we design three diketopyrrolopyrrole-based covalent organic frameworks(COFs)via a synergisticπ-extended lock and carboxyl-functionalized anchor molecular engineering strategy.Among them,TPy-DPP-COF features a covalently lockedπ-conjugated structure that enhances planarity,optimizes energy alignment,and minimizes exciton binding energy,thereby promoting charge transfer and suppressing recombination.Concurrently,carboxyl groups enable uranyl-specific coordination and create local electric fields to facilitate charge separation.These features contribute to the outstanding performance of TPy-DPP-COF,which achieves a high uranium adsorption capacity of 16.33 mg g−1 in natural seawater under irradiation,with only 29.3%capacity loss after 10 cycles,surpassing industrial benchmarks.Density functional theory(DFT)calculations and experimental studies reveal a synergistic photocatalysis-adsorption pathway,with DPP units acting as active sites for uranium reduction.This work highlights a molecular design strategy for developing efficient COF-based photocatalysts for practical marine uranium recovery.展开更多
AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longit...AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longitudinal observational study,detailed medical histories of APAC patients and comprehensive ophthalmic examinations at final followup were collected.Logistic regression analysis was performed to identify predictors of blindness.Univariate and multivariate linear regression analyses were conducted to determine risk factors associated with visual outcomes.RESULTS:This study included 39 affected eyes of 31 subjects(26 females)with an average age of 74.1±8.0y.At 6.7±4.2y after APAC attack,2(5.7%)eyes had bestcorrected visual acuity(VA)worse than 3/60.Advanced glaucomatous visual field loss was observed in 15(39.5%)affected eyes and 5(25.0%)fellow eyes.Nine affected eyes(23.7%)had GON,and 11(28.9%)were blind.Six(15.4%)affected eyes and 2(9.1%)fellow eyes had suspicious progression.A significantly higher blindness rate in factory workers compared to office workers.Logistic regression identified that worse VA at attack(OR 10.568,95%CI 1.288-86.695;P=0.028)and worse early postoperative VA(OR 13.214,95%CI 1.157-150.881;P=0.038)were risk factors for blindness.Multivariate regression showed that longer duration of elevated intraocular pressure(P=0.004)and worse early postoperative VA(P=0.009)were associated with worse visual outcomes.CONCLUSION:Despite LE surgery,some APAC patients experience continued visual function deterioration.Lifelong monitoring is necessary.Target pressure and progression rates should be re-evaluated during follow-up.展开更多
Nuclear energy is critical not only to sustainable economic development but also to achieving peaking carbon dioxide emissions and carbon neutrality[1].China is expected to host the world’s largest number of nuclear ...Nuclear energy is critical not only to sustainable economic development but also to achieving peaking carbon dioxide emissions and carbon neutrality[1].China is expected to host the world’s largest number of nuclear power plants in a few years.Uranium-235 serves as the primary fissile material for the fabrication of nuclear fuel.However,there are only about 6.14 million tons of uranium resources on land,which can sustain~70 years of operation for global nuclear power plants.Thereby,the recovery of uranium from spent fuel,radioactive waste solutions,seawater,and salt lakes is crucial for the healthy development of nuclear power utilization[2-4].展开更多
In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
Solvent extraction is the main method used to separate and purify rare earth elements.In the process of rare earths extraction,emulsification often generated due to the instability of the aqueous and organic phases or...Solvent extraction is the main method used to separate and purify rare earth elements.In the process of rare earths extraction,emulsification often generated due to the instability of the aqueous and organic phases or improper operating conditions.Once emulsification occurs,it would not only lead to low rare earths recovery efficiency,small product quantities,high production costs and the losing of extractant and rare earth resources,but also result in serious environmental pollution.Therefore,it is very important to study the micro-mechanisms of emulsification and establish new methods to prevent emulsification at the source.In this paper,possible factors resulting in emulsification,such as the compositions and properties of the organic and aqueous phases,the operating conditions of the rare earths extraction are reviewed.The micro-mechanisms of emulsification are summarized basing on the microscopic structures in the bulk phase,aggregations of the extractants at the organic-aqueous interface,spectral characterizations and computational simulations.On this basis,new formation mechanisms are proposed for emulsification.Preliminary explorations are employed to verify the correctness of these new viewpoints.Finally,future directions for studies of the emulsification micro-mechanism are proposed.This study provides a theoretical basis for further understanding the micro-mechanisms of interfacial instability resulting in emulsification in the process of rare earths extraction.展开更多
Given the scarcity of Satellite Frequency and Orbit(SFO)resources,it holds paramount importance to establish a comprehensive knowledge graph of SFO field(SFO-KG)and employ knowledge reasoning technology to automatical...Given the scarcity of Satellite Frequency and Orbit(SFO)resources,it holds paramount importance to establish a comprehensive knowledge graph of SFO field(SFO-KG)and employ knowledge reasoning technology to automatically mine available SFO resources.An essential aspect of constructing SFO-KG is the extraction of Chinese entity relations.Unfortunately,there is currently no publicly available Chinese SFO entity Relation Extraction(RE)dataset.Moreover,publicly available SFO text data contain numerous NA(representing for“No Answer”)relation category sentences that resemble other relation sentences and pose challenges in accurate classification,resulting in low recall and precision for the NA relation category in entity RE.Consequently,this issue adversely affects both the accuracy of constructing the knowledge graph and the efficiency of RE processes.To address these challenges,this paper proposes a method for extracting Chinese SFO text entity relations based on dynamic integrated learning.This method includes the construction of a manually annotated Chinese SFO entity RE dataset and a classifier combining features of SFO resource data.The proposed approach combines integrated learning and pre-training models,specifically utilizing Bidirectional Encoder Representation from Transformers(BERT).In addition,it incorporates one-class classification,attention mechanisms,and dynamic feedback mechanisms to improve the performance of the RE model.Experimental results show that the proposed method outperforms the traditional methods in terms of F1 value when extracting entity relations from both balanced and long-tailed datasets.展开更多
The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for th...The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for the market service for green energy consumers.This study proposed a two-stage feature extraction approach for green energy consumers leveraging clustering and termfrequency-inverse document frequency(TF-IDF)algorithms within a knowledge graph framework to provide an information basis that supports the green development of the retail electricity market.First,the multi-source heterogeneous data of green energy consumers under an actual market environment is systematically introduced and the information is categorized into discrete,interval,and relational features.A clustering algorithm was employed to extract features of the trading behavior of green energy consumers in the first stage using the parameter data of green retail electricity contracts.Then,TF-IDF algorithm was applied in the second stage to extract features for green energy consumers in different clusters.Finally,the effectiveness of the proposed approach was validated based on the actual operational data in a southern province of China.It is shown that the most significant discrepancy between the retail trading behaviors of green energy consumers is the power share of green retail packages,whose averaged values are 25.64%,50%,39.66%,and 24.89%in four different clusters,respectively.Additionally,power supply bureaus and electricity retail companies affects the behavior of the green energy consumers most significantly.展开更多
Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the con...Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the context of fewshot and zero-shot scenarios remains highly challenging due to the scarcity of training data.Large language models(LLMs),on the other hand,can generalize well to unseen tasks with few-shot demonstrations or even zero-shot instructions and have demonstrated impressive ability for a wide range of natural language understanding or generation tasks.Nevertheless,it is unclear,whether such effectiveness can be replicated in the task of IE,where the target tasks involve specialized schema and quite abstractive entity or relation concepts.In this paper,we first examine the validity of LLMs in executing IE tasks with an established prompting strategy and further propose multiple types of augmented prompting methods,including the structured fundamental prompt(SFP),the structured interactive reasoning prompt(SIRP),and the voting-enabled structured interactive reasoning prompt(VESIRP).The experimental results demonstrate that while directly promotes inferior performance,the proposed augmented prompt methods significantly improve the extraction accuracy,achieving comparable or even better performance(e.g.,zero-shot FewNERD,FewNERD-INTRA)than state-of-theart methods that require large-scale training samples.This study represents a systematic exploration of employing instruction-following LLM for the task of IE.It not only establishes a performance benchmark for this novel paradigm but,more importantly,validates a practical technical pathway through the proposed prompt enhancement method,offering a viable solution for efficient IE in low-resource settings.展开更多
This research optimized the structure of lithium extraction solar ponds to enhance the crystallization rate and yield of Li_(2)CO_(3).Using the response surface methodology in Design-Expert 10.0.3,the authors conducte...This research optimized the structure of lithium extraction solar ponds to enhance the crystallization rate and yield of Li_(2)CO_(3).Using the response surface methodology in Design-Expert 10.0.3,the authors conducted experiments to investigate the influence of four factors related to solar pond structure on the crystallization of Li_(2)CO_(3) and their pairwise interactions.Computational Fluid Dynamics(CFD)simulations of the flow field within the solar pond were performed using COMSOL Multiphysics software to compare temperature distributions before and after optimization.The results indicate that the optimal structure for lithium extraction from the Zabuye Salt Lake solar ponds includes UCZ(Upper Convective Zone)thickness of 53.63 cm,an LCZ(Lower Convective Zone)direct heating temperature of 57.39℃,a CO32−concentration of 32.21 g/L,and an added soda ash concentration of 6.52 g/L.Following this optimized pathway,the Li_(2)CO_(3) precipitation increased by 7.34% compared to the initial solar pond process,with a 33.33% improvement in lithium carbonate crystallization rate.This study demonstrates the feasibility of optimizing lithium extraction solar pond structures,offering a new approach for constructing such ponds in salt lakes.It provides valuable guidance for the efficient extraction of lithium resources from carbonate-type salt lake brines.展开更多
Background:Ampelopsis grossedentata,vine tea,which is the tea alternative beverages in China.In vine tea processing,a large amount of broken tea is produced,which has low commercial value.Methods:This study investigat...Background:Ampelopsis grossedentata,vine tea,which is the tea alternative beverages in China.In vine tea processing,a large amount of broken tea is produced,which has low commercial value.Methods:This study investigates the influence of different extraction methods(room temperature water extraction,boiling water extraction,ultrasonic-assisted room temperature water extraction,and ultrasonic-assisted boiling water extraction,referred to as room temperature water extraction(RE),boiling water extraction(BE),ultrasonic assistance at room temperature water extraction(URE),and ultrasonic assistance in boiling water extraction(UBE))on the yield,dihydromyricetin(DMY)content,free amino acid composition,volatile aroma components,and antioxidant properties of vine tea extracts.Results:A notable influence of extraction temperature on the yield of vine tea extracts(P<0.05),with BE yielding the highest at 43.13±0.26%,higher than that of RE(34.29±0.81%).Ultrasound-assisted extraction significantly increased the DMY content of the extracts(P<0.05),whereas DMY content in the RE extracts was 59.94±1.70%,that of URE reached 66.14±2.78%.Analysis revealed 17 amino acids,with L-serine and aspartic acid being the most abundant in the extracts,nevertheless ultrasound-assisted extraction reduced total free amino acid content.Gas chromatography-mass spectrometry analysis demonstrated an increase in the diversity and quantity of compounds in the vine tea water extracts obtained through ultrasonic-assisted extraction.Specifically,69 and 68 volatile compounds were found in URE and UBE extracts,which were higher than the number found in RE and BE extracts.In vitro,antioxidant activity assessments revealed varying antioxidant capacities among different extraction methods,with RE exhibiting the highest DPPH scavenging rate,URE leading in ABTS•+free radical scavenging,and BE demonstrating superior ferric ion reducing antioxidant activity.Conclusion:The findings suggest that extraction methods significantly influence the chemical composition and antioxidant properties of vine tea extracts.Ultrasonic-assisted extraction proved instrumental in elevating the DMY content in vine tea extracts,thereby enriching its flavor profile while maintaining its antioxidant properties.展开更多
文摘Stevioside, extracted from the leaves of Stevia rebaudiana Bertoni, is a natural, high intensity, lowcaloric sweetener with wide therapeutic activities. Conventional stevioside extraction methodologies involve the use of non-green solvents, supercritical fluids, microwaves, etc., however, all these processes are expensive, time-consuming and eco-unfriendly. Therefore an alternative process is desired for the isolation of stevioside. In this study, a novel enzyme-mediated extraction (EME) method has been developed. The dry stevia leaves were pre-treated with hydrolytic enzymes aided by transition metal salts (FeCl3). This was followed by pressurized hot water extraction (PHWE) to release stevioside. The crude extract was purified and clarified through multi-stage membrane filtration. The results confirm that metal salt-assisted cellulase pre-treatment enhanced the yield of stevioside to 72%, with 98% purity, which was higher in comparison to existing methods. Thus, the methodology developed establishes a simple, “green”, enzyme-mediated process for the efficient isolation of stevioside under economical and eco-friendly conditions.
文摘Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.
基金the financial support from the National Natural Science Foundation of China(22325603)the financial support from the National Natural Science Foundation of China(22376116)+3 种基金the financial support from the National Natural Science Foundation of China(22076130)the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(CAST)(2023QNRC001)the Fundamental Research Funds for the Central Universities(20826041D4117)the Natural Science Foundation of Sichuan(2025ZNSFSC0109)。
文摘The recycling of neptunium(Np)from nuclear wastes is crucial for the sustainable development of nuclear energy,yet it is still a challenging task owing to the complexity of Np chemistry.Precise control of oxidation state is highly desirable for the effective recovery of Np.In this study,we report an innovative strategy for Np recovery through in-situ coordination and reduction of Np(Ⅴ)in a biphasic extraction system.By leveraging the synergistic effects of coordination by a P=O donating ligand(trialkyl phosphine oxide,TRPO)and reduction by hydroquinone(HQ)in the organic phase,efficient Np(Ⅴ)-to-Np(Ⅳ)conversion and high distribution ratio(D)of Np were achieved in a single extraction contact.The reduction mechanism of Np was elucidated through spectroscopic and theoretical analyses.This work enriches the redox chemistry of Np and provides a novel pathway for Np recovery in advanced nuclear fuel cycles.
基金supported by the National Natural Science Foundation of China(No.52403035)the Shanghai Sailing Program(23YF1400300)+1 种基金the Fundamental Research Funds for the Central Universities(2232023D-05)the Weiqiao Teaching and Research Innovation Program.
文摘The lack of macro-continuity and mechanical strength of covalent organic frameworks(COFs)has significantly limited their practical applications.Here,we propose an“alcohol-triggered defect cleavage”strategy to precisely regulate the growth and stacking of COF grains through a moderate reversed Schiff base reaction,realizing the direct synthesis of COF nanofibers(CNFs)with high aspect ratio(L/D=103.05)and long length(>20μm).An individual CNF exhibits a biomimetic scale-like architecture,achieving superior flexibility and fatigue resistance under dynamic bending via a multiscale stress dissipation mechanism.Taking advantages of these structural features,we engineer CNF aerogels(CNF-As)with programmable porous structures(e.g.,honeycomb,lamellar,isotropic)via directional ice-template methodology.CNF-As demonstrate 100%COF content,high specific surface area(396.15 m^(2)g^(-1))and superelasticity(~0%elastic deformation after 500 compression cycles at 50%strain),outperforming most COF-based counterparts.Compared with the conventional COF aerogels,the unique structural features of CNF-A enable it to perform outstandingly in uranium extraction,with an 11.72-fold increment in adsorption capacity(920.12 mg g^(-1))and adsorption rate(89.9%),and a 2.48-fold improvement in selectivity(U/V=2.31).This study provides a direct strategy for the development of next-generation COF materials with outstanding functionality and structural robustness.
基金financial support from the National Natural Science Foundation of China (Nos.22476178,U2067213)Natural Science Foundation of Zhejiang Province (No.LRG25B060002)。
文摘As an important class of phenanthroline derivatives containing soft N and hard O donor atoms,the laborious syntheses of unsymmetrical 1,10-phenanthroline-derived diamide ligands(DAPhen) have hindered its extensive study.In this work,we first report a convenient synthetic method for the construction of DAPhen using Friedländer reaction by two facile steps(vs.previous 12 steps).A variety of DAPhen ligands are readily available,especially unsymmetrical ones,which give us a platform to systematically study the substituent effect on f-block elements extraction performance.The performance of unsymmetrical extractants is experimentally confirmed to falls between that of their corresponding symmetrical extractants by extracting UO_(2)^(2+) as the representative f-block element.This work provides a direct and versatile method to synthesize symmetrical and unsymmetrical DAPhen,which paves way for the investigations on their coordination properties with metal ions and other applications.
基金support from the National Natural Science Foundation of China Regional Innovation and Development Joint Fund(U24A20557)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDC0230403)+3 种基金the National Natural Science Foundation of China(22378393,22208356)“Hundred Talents Program”of the Chinese Academy of Sciencesthe Chinese Academy of Sciences stably supports the youth team plan in the field of basic research(YSBR 038)Key Research&Development projects in Qinghai Province(2023-HZ-805).
文摘This article presents a new synergistic extraction system composed of Cyanex 272(C272,bis(2,4,4-trimethylpentyl)phosphinic acid)and iso-octanol for Sc_(3+) separation.The proposed synergistic system possessed an Sc^(3+) extraction efficiency of 93.5%and a back-extraction efficiency of 82.7%,with selectivity coefficients of β_(Sc/Fe)=459 and β_(Sc/Al)=4241,which are considerably higher as compared to the current extraction systems.The extraction mechanism was studied and interpreted.The enhanced extraction efficiency is attributed to the increased hydrophobicity of the ternary complex,whereas the back-extraction efficiency can be ascribed to the attenuated stability of the complex.C272 and C272–iso-octanol systems also possess considerable surface activity,which is beneficial for the phase separation in solvent extraction.Based on the solvent extraction results,a preliminary study was conducted on polymer inclusion membranes(PIMs)using the binary system for Sc^(3+) separation to avoid the formation of the third phase,achieving an optimal initial flux of PIM of 6.71×10^(−4)mol·m^(−2)·h^(−1).Our results provide valuable information on highly efficient Sc^(3+) separation,and the study on PIM extraction has shown a green alternative to solvent extraction.
基金supported by National Natural Science Foundation of China(Grant No.22378066,22108040)Collaboration&Innovation Platform Project of National Independent Innovation Demonstration Zone(Fuzhou,Xiamen&Quanzhou)(Project No:3502ZCQXT2023004).
文摘The extraction of uranium from seawater via membrane adsorption is a promising strategy for ensuring a long-term supply of uranium and the sustainability of nuclear energy.However,this approach has been hindered by the longstanding challenge of identifying sustainable membrane materials.In response,we propose a prototypal hybridization strategy to design a novel series of aminated conjugated microporous polymer(CMPN)@collagen fiber membrane(COLM).These sustainable and low-cost membrane materials allow a rapid and high-affinity kinetic to capture 90%of the uranium in just 30 min from 50 ppm with a high selectivity of Kd>105 mL·g^(−1).They also afford a robustly reusable adsorption capacity as high as 345 mg·g^(−1)that could harvest 1.61 mg·g^(−1)of uranium in a short 7-day real marine engineering in Fujian Province,even though suffered from very low uranium concentration of 3.29μg·L^(−1)and tough influence of salts such as 10.77 g·L^(−1)of Na^(+),1.75μg·L^(−1)of VO_(3)^(−)etc.in the rough seas.The structural evidence from both experimental and theoretical studies confirmed the formation of favorable chelating motifs from the amino group on CMPN-COLM,and the intensification by the synergistic effect from the size-sieving action of CMPN and the capillary inflow effect of COLM.
基金funded by the Hainan Province Science and Technology Special Fund under Grant ZDYF2024GXJS292.
文摘Deep learning has made significant progress in the field of oriented object detection for remote sensing images.However,existing methods still face challenges when dealing with difficult tasks such as multi-scale targets,complex backgrounds,and small objects in remote sensing.Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot.Therefore,we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture,specifically optimized for the characteristics of large target scale variations,diverse orientations,and numerous small objects in remote sensing images.Our innovations lie in two main aspects:First,a dynamic snake convolution(DSC)is introduced into the backbone network to enhance the model’s feature extraction capability for oriented targets.Second,an innovative focusing-diffusion module is designed in the feature fusion neck to effectively integrate multi-scale feature information.Finally,we introduce Layer-Adaptive Sparsity for magnitude-based Pruning(LASP)method to perform lightweight network pruning to better complete tasks in resource-constrained scenarios.Experimental results on the lightweight platform Orin demonstrate that the proposed method significantly outperforms the original YOLOv8s model in oriented remote sensing object detection tasks,and achieves comparable or superior performance to state-of-the-art methods on three authoritative remote sensing datasets(DOTA v1.0,DOTA v1.5,and HRSC2016).
文摘Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development.
基金the National Natural Science Foundation of China(22479047,22409058)the Outstanding Youth Scientist Foundation of Hunan Province(2022JJ10023)the Provincial Natural Science Foundation of Guangdong(2023A1515011745)for financial support of this research。
文摘To ease the scarcity of lithium(Li)resource and cut down on environmental pollution,an efficient,selective,inexpensive and sustainable Li recycling process from waste batteries is needed,which is yet to be achieved.Here,we report a low-potential photoelectrochemical(PEC)system that selectively and efficiently extracts Li metals from multi-cation electrolytes under 1 sun illumination.Based on the difference of redox potential,we can get rid of the disturbance of other cations(i.e.,Fe,Co and Ni ions)by a bias-free PEC device to realize the extraction of high-purity Li metals on a coplanar Si-based photocathode-TiO_(2) photoanode tandem device at 2 V of applied bias(far less than the redox potentials of Li^(+)/Li).In such system,the extraction rate of Li metals(purity>99.5%)exceeds 1.35 g h^(-1)m^(-2)with 90%of Faradaic efficiency.Long-term experiments,different electrode/electrolyte tests,and various price assessments further demonstrate the stability,compatibility and economy of PEC extraction system,enabling a solar-driven pathway for the recycling of critical metal resources.
基金the Young Elite Scientists Sponsorship Program by JXAST(2024QT11)the National Natural Science Foundation of China(22465001,22309003)the Jiangxi Provincial Natural Science Foundation(20232BAB203042,20242BAB22002).
文摘As a key low-carbon energy source,nuclear power plays a vital role in the global transition toward sustainable energy.Photocatalytic uranium extraction from seawater(UES)offers a promising solution to ensure long-term uranium supply but is challenged by ultra-low uranium concentrations and ion interference.To overcome these issues,we design three diketopyrrolopyrrole-based covalent organic frameworks(COFs)via a synergisticπ-extended lock and carboxyl-functionalized anchor molecular engineering strategy.Among them,TPy-DPP-COF features a covalently lockedπ-conjugated structure that enhances planarity,optimizes energy alignment,and minimizes exciton binding energy,thereby promoting charge transfer and suppressing recombination.Concurrently,carboxyl groups enable uranyl-specific coordination and create local electric fields to facilitate charge separation.These features contribute to the outstanding performance of TPy-DPP-COF,which achieves a high uranium adsorption capacity of 16.33 mg g−1 in natural seawater under irradiation,with only 29.3%capacity loss after 10 cycles,surpassing industrial benchmarks.Density functional theory(DFT)calculations and experimental studies reveal a synergistic photocatalysis-adsorption pathway,with DPP units acting as active sites for uranium reduction.This work highlights a molecular design strategy for developing efficient COF-based photocatalysts for practical marine uranium recovery.
文摘AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longitudinal observational study,detailed medical histories of APAC patients and comprehensive ophthalmic examinations at final followup were collected.Logistic regression analysis was performed to identify predictors of blindness.Univariate and multivariate linear regression analyses were conducted to determine risk factors associated with visual outcomes.RESULTS:This study included 39 affected eyes of 31 subjects(26 females)with an average age of 74.1±8.0y.At 6.7±4.2y after APAC attack,2(5.7%)eyes had bestcorrected visual acuity(VA)worse than 3/60.Advanced glaucomatous visual field loss was observed in 15(39.5%)affected eyes and 5(25.0%)fellow eyes.Nine affected eyes(23.7%)had GON,and 11(28.9%)were blind.Six(15.4%)affected eyes and 2(9.1%)fellow eyes had suspicious progression.A significantly higher blindness rate in factory workers compared to office workers.Logistic regression identified that worse VA at attack(OR 10.568,95%CI 1.288-86.695;P=0.028)and worse early postoperative VA(OR 13.214,95%CI 1.157-150.881;P=0.038)were risk factors for blindness.Multivariate regression showed that longer duration of elevated intraocular pressure(P=0.004)and worse early postoperative VA(P=0.009)were associated with worse visual outcomes.CONCLUSION:Despite LE surgery,some APAC patients experience continued visual function deterioration.Lifelong monitoring is necessary.Target pressure and progression rates should be re-evaluated during follow-up.
基金supported by the National Natural Science Foundation of China(22341602,U24B20195)。
文摘Nuclear energy is critical not only to sustainable economic development but also to achieving peaking carbon dioxide emissions and carbon neutrality[1].China is expected to host the world’s largest number of nuclear power plants in a few years.Uranium-235 serves as the primary fissile material for the fabrication of nuclear fuel.However,there are only about 6.14 million tons of uranium resources on land,which can sustain~70 years of operation for global nuclear power plants.Thereby,the recovery of uranium from spent fuel,radioactive waste solutions,seawater,and salt lakes is crucial for the healthy development of nuclear power utilization[2-4].
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
基金Project supported by the National Natural Science Foundation of China(52074031)the Key Research and Development Program of Shandong Province(ZR2021MB051,ZR2020ME256)the Open Project of Key Laboratory of Green Chemical Engineering Process of Ministry of Education(GCP202117)。
文摘Solvent extraction is the main method used to separate and purify rare earth elements.In the process of rare earths extraction,emulsification often generated due to the instability of the aqueous and organic phases or improper operating conditions.Once emulsification occurs,it would not only lead to low rare earths recovery efficiency,small product quantities,high production costs and the losing of extractant and rare earth resources,but also result in serious environmental pollution.Therefore,it is very important to study the micro-mechanisms of emulsification and establish new methods to prevent emulsification at the source.In this paper,possible factors resulting in emulsification,such as the compositions and properties of the organic and aqueous phases,the operating conditions of the rare earths extraction are reviewed.The micro-mechanisms of emulsification are summarized basing on the microscopic structures in the bulk phase,aggregations of the extractants at the organic-aqueous interface,spectral characterizations and computational simulations.On this basis,new formation mechanisms are proposed for emulsification.Preliminary explorations are employed to verify the correctness of these new viewpoints.Finally,future directions for studies of the emulsification micro-mechanism are proposed.This study provides a theoretical basis for further understanding the micro-mechanisms of interfacial instability resulting in emulsification in the process of rare earths extraction.
文摘Given the scarcity of Satellite Frequency and Orbit(SFO)resources,it holds paramount importance to establish a comprehensive knowledge graph of SFO field(SFO-KG)and employ knowledge reasoning technology to automatically mine available SFO resources.An essential aspect of constructing SFO-KG is the extraction of Chinese entity relations.Unfortunately,there is currently no publicly available Chinese SFO entity Relation Extraction(RE)dataset.Moreover,publicly available SFO text data contain numerous NA(representing for“No Answer”)relation category sentences that resemble other relation sentences and pose challenges in accurate classification,resulting in low recall and precision for the NA relation category in entity RE.Consequently,this issue adversely affects both the accuracy of constructing the knowledge graph and the efficiency of RE processes.To address these challenges,this paper proposes a method for extracting Chinese SFO text entity relations based on dynamic integrated learning.This method includes the construction of a manually annotated Chinese SFO entity RE dataset and a classifier combining features of SFO resource data.The proposed approach combines integrated learning and pre-training models,specifically utilizing Bidirectional Encoder Representation from Transformers(BERT).In addition,it incorporates one-class classification,attention mechanisms,and dynamic feedback mechanisms to improve the performance of the RE model.Experimental results show that the proposed method outperforms the traditional methods in terms of F1 value when extracting entity relations from both balanced and long-tailed datasets.
基金support by the Science and Technology Project of Guangdong Power Exchange Center Co.,Ltd.(No.GDKJXM20222599)National Natural Science Foundation of China(No.52207104)Natural Science Foundation of Guangdong Province(No.2024A1515010426).
文摘The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for the market service for green energy consumers.This study proposed a two-stage feature extraction approach for green energy consumers leveraging clustering and termfrequency-inverse document frequency(TF-IDF)algorithms within a knowledge graph framework to provide an information basis that supports the green development of the retail electricity market.First,the multi-source heterogeneous data of green energy consumers under an actual market environment is systematically introduced and the information is categorized into discrete,interval,and relational features.A clustering algorithm was employed to extract features of the trading behavior of green energy consumers in the first stage using the parameter data of green retail electricity contracts.Then,TF-IDF algorithm was applied in the second stage to extract features for green energy consumers in different clusters.Finally,the effectiveness of the proposed approach was validated based on the actual operational data in a southern province of China.It is shown that the most significant discrepancy between the retail trading behaviors of green energy consumers is the power share of green retail packages,whose averaged values are 25.64%,50%,39.66%,and 24.89%in four different clusters,respectively.Additionally,power supply bureaus and electricity retail companies affects the behavior of the green energy consumers most significantly.
基金supported by the National Natural Science Foundation of China(62222212).
文摘Information extraction(IE)aims to automatically identify and extract information about specific interests from raw texts.Despite the abundance of solutions based on fine-tuning pretrained language models,IE in the context of fewshot and zero-shot scenarios remains highly challenging due to the scarcity of training data.Large language models(LLMs),on the other hand,can generalize well to unseen tasks with few-shot demonstrations or even zero-shot instructions and have demonstrated impressive ability for a wide range of natural language understanding or generation tasks.Nevertheless,it is unclear,whether such effectiveness can be replicated in the task of IE,where the target tasks involve specialized schema and quite abstractive entity or relation concepts.In this paper,we first examine the validity of LLMs in executing IE tasks with an established prompting strategy and further propose multiple types of augmented prompting methods,including the structured fundamental prompt(SFP),the structured interactive reasoning prompt(SIRP),and the voting-enabled structured interactive reasoning prompt(VESIRP).The experimental results demonstrate that while directly promotes inferior performance,the proposed augmented prompt methods significantly improve the extraction accuracy,achieving comparable or even better performance(e.g.,zero-shot FewNERD,FewNERD-INTRA)than state-of-theart methods that require large-scale training samples.This study represents a systematic exploration of employing instruction-following LLM for the task of IE.It not only establishes a performance benchmark for this novel paradigm but,more importantly,validates a practical technical pathway through the proposed prompt enhancement method,offering a viable solution for efficient IE in low-resource settings.
基金This study was supported by the National Natural Science Foundation of China(U20A20148)the Major Science and Technology Projects of the Xizang(Tibet)Autonomous Region(XZ202201ZD0004G and XZ202201ZD0004G01).
文摘This research optimized the structure of lithium extraction solar ponds to enhance the crystallization rate and yield of Li_(2)CO_(3).Using the response surface methodology in Design-Expert 10.0.3,the authors conducted experiments to investigate the influence of four factors related to solar pond structure on the crystallization of Li_(2)CO_(3) and their pairwise interactions.Computational Fluid Dynamics(CFD)simulations of the flow field within the solar pond were performed using COMSOL Multiphysics software to compare temperature distributions before and after optimization.The results indicate that the optimal structure for lithium extraction from the Zabuye Salt Lake solar ponds includes UCZ(Upper Convective Zone)thickness of 53.63 cm,an LCZ(Lower Convective Zone)direct heating temperature of 57.39℃,a CO32−concentration of 32.21 g/L,and an added soda ash concentration of 6.52 g/L.Following this optimized pathway,the Li_(2)CO_(3) precipitation increased by 7.34% compared to the initial solar pond process,with a 33.33% improvement in lithium carbonate crystallization rate.This study demonstrates the feasibility of optimizing lithium extraction solar pond structures,offering a new approach for constructing such ponds in salt lakes.It provides valuable guidance for the efficient extraction of lithium resources from carbonate-type salt lake brines.
基金supported by the Key Research and Development Program of Hunan Province of China(No.2022NK2036)Xiangxi Prefecture Science and Technology Plan Project"School-Local Integration"Special Project(No.2022001)the scientific research project of Hunan Provincial Department of Education(No.22B0520).
文摘Background:Ampelopsis grossedentata,vine tea,which is the tea alternative beverages in China.In vine tea processing,a large amount of broken tea is produced,which has low commercial value.Methods:This study investigates the influence of different extraction methods(room temperature water extraction,boiling water extraction,ultrasonic-assisted room temperature water extraction,and ultrasonic-assisted boiling water extraction,referred to as room temperature water extraction(RE),boiling water extraction(BE),ultrasonic assistance at room temperature water extraction(URE),and ultrasonic assistance in boiling water extraction(UBE))on the yield,dihydromyricetin(DMY)content,free amino acid composition,volatile aroma components,and antioxidant properties of vine tea extracts.Results:A notable influence of extraction temperature on the yield of vine tea extracts(P<0.05),with BE yielding the highest at 43.13±0.26%,higher than that of RE(34.29±0.81%).Ultrasound-assisted extraction significantly increased the DMY content of the extracts(P<0.05),whereas DMY content in the RE extracts was 59.94±1.70%,that of URE reached 66.14±2.78%.Analysis revealed 17 amino acids,with L-serine and aspartic acid being the most abundant in the extracts,nevertheless ultrasound-assisted extraction reduced total free amino acid content.Gas chromatography-mass spectrometry analysis demonstrated an increase in the diversity and quantity of compounds in the vine tea water extracts obtained through ultrasonic-assisted extraction.Specifically,69 and 68 volatile compounds were found in URE and UBE extracts,which were higher than the number found in RE and BE extracts.In vitro,antioxidant activity assessments revealed varying antioxidant capacities among different extraction methods,with RE exhibiting the highest DPPH scavenging rate,URE leading in ABTS•+free radical scavenging,and BE demonstrating superior ferric ion reducing antioxidant activity.Conclusion:The findings suggest that extraction methods significantly influence the chemical composition and antioxidant properties of vine tea extracts.Ultrasonic-assisted extraction proved instrumental in elevating the DMY content in vine tea extracts,thereby enriching its flavor profile while maintaining its antioxidant properties.
