With the continuous advancement of social technology and the increasing awareness of health management,biomass-based triboelectric nanogenerator(TENG)displayed significant potential as flexible wearable electronics fo...With the continuous advancement of social technology and the increasing awareness of health management,biomass-based triboelectric nanogenerator(TENG)displayed significant potential as flexible wearable electronics for continuous foot gait monitoring.Nevertheless,existing biomass-based TENG often faces challenges of insufficient mechanical robustness and durability in practical applications,where they are prone to surface abrasion and structural fracture under continuous compression and friction,severely limiting their long-term performances.In order to address these challenges,this work proposed a multiscale crosslinking strategy,which strengthened the noncovalent interactions within the polymer by constructing multiple reinforcement networks,successfully fabricating a dual-network C-lignin-based triboelectric material(CLTM)with excellent durability and crack resistance.Among them,the optimal CLTM(PSGCL-0.2)exhibited high mechanical strength(strain 445%,tensile strength 41.56 MPa,Young's modulus 41.25 MPa,toughness 159.67 MJ/m^(3))and excellent cyclic stability(300 cycles)with versatile functionalities,including antibacterial,antioxidant,and UV-shielding properties,water stabilization(255.51 g/m^(2)/d),efficient photothermal conversion,and full recyclability.Furthermore,biomass-based TENG device assembled from PSGCL-0.2 achieved stable triboelectric output properties(102.5 V,2.9μA,and 61.3 nC),and sustainable for 2000 cycles,fast response time(68 ms),and excellent power density(325.9 mW/m^(2)),effectively converting mechanical energy into electrical energy.Especially,PSGCL 0.2 was also integrated into the wireless self-powered smart insole,successfully enabling real-time visual monitoring of plantar pressure distribution and dynamic gait.Meanwhile,combined with the machine learning algorithm,the self-powered smart insole achieved precise recognition and classification of eight different motion states with an accuracy of 98%.This study provides the feasible strategy for developing extremely stable and durable biomass-based TENG,aimed at advancing sustainable intelligent healthcare systems.展开更多
The illicit trafficking of special nuclear materials(SNMs)poses a grave threat to global security and necessitates the development of effective nuclear material identification methods.This study investigated a method ...The illicit trafficking of special nuclear materials(SNMs)poses a grave threat to global security and necessitates the development of effective nuclear material identification methods.This study investigated a method to isotopically identify the SNMs,including^(233,235,238)U,^(239-242)Pu,and^(232)Th,based on the detection of delayedγ-rays from photofission fragments.The delayedγ-ray spectra resulting from the photofission of SNMs irradiated by a 14 MeVγbeam with a total of 10~9 were simulated using Geant4.Three high-yield fission fragments,namely^(138)Cs,^(89)Rb,and^(94)Y,were selected as candidate fragments for SNM identification.The yield ratios of these three fragments were calculated,and the results from the different SNMs were compared.The yield ratio of^(138)Cs/^(89)Rb was used to identify most SNMs,including^(233,235,238)U,^(242)Pu,and^(232)Th,with a confidence level above 95%.To identify^(239-241)Pu with the same confidence,a higher total number of 10^(11)γbeams is required.However,although the^(94)Y/^(89)Rb ratio is suitable for elementally identifying SNMs,isotopic identification is difficult.In addition,the count rate of the delayedγabove 3 MeV can be used to rapidly detect the presence of nuclear materials.展开更多
[Objectives]To further revise and enhance the quality standards of the Yao medicinal material Gendarussa vulgaris,and to provide empirical data to ensure the safety of G.vulgaris medicinal resources and the advancemen...[Objectives]To further revise and enhance the quality standards of the Yao medicinal material Gendarussa vulgaris,and to provide empirical data to ensure the safety of G.vulgaris medicinal resources and the advancement of medicinal material standards.[Methods]The identification of G.vulgaris was conducted through morphological,microscopic,and physicochemical analyses.Parameters such as moisture content,total ash,acid-insoluble ash,extract content,heavy metal concentrations,and organochlorine pesticide residues were determined in accordance with the 2025 edition of the Chinese Pharmacopoeia.[Results]The distinguishing characteristics of G.vulgaris were clearly evident,allowing for the establishment of an identification method for this species.The thin-layer chromatogram exhibited clear and well-resolved separation.Additionally,the permissible ranges for moisture content,total ash,and water-soluble extract in the medicinal materials were defined,while new limit standards for acid-insoluble ash and heavy metal content were concurrently established.[Conclusions]The test method established in this study is both accurate and reliable,thereby enhancing the quality standards of the medicinal material G.vulgaris.展开更多
In order to improve the traditional teaching model of traditional Chinese medicine identification course for graduate students of pharmacy,this paper described the research on constructing and practicing the blended t...In order to improve the traditional teaching model of traditional Chinese medicine identification course for graduate students of pharmacy,this paper described the research on constructing and practicing the blended teaching model of"online+offline"based on the teaching concept of TfU by taking the course of"Authentic Medicinal Materials and Quality of Traditional Chinese Medicine"as an example.In the preparatory phase,through resource integration and course content decomposition,it identifies"generative topics"to engage students in pre-class online discussions.During the instructional phase,"comprehension-oriented objectives"are established based on learning analytics,followed by the implementation of"understanding-focused activities"for guided inquiry in offline classrooms.The post-class phase employs online extended materials to conduct"sustained assessment"through evaluations and summaries,thereby continuously optimizing subsequent teaching practices.This pedagogical framework not only effectively cultivates investigative research thinking among graduate students but also enhances standardized management and scientific development of the teaching team.The practical research outcomes and experiences derived from this model can provide valuable references for analogous course reforms.展开更多
To address the challenge of balancing thermal management and thermal runaway mitigation,it is crucial to explore effective methods for enhancing the safety of lithium-ion battery systems.Herein,an innovative hydrated ...To address the challenge of balancing thermal management and thermal runaway mitigation,it is crucial to explore effective methods for enhancing the safety of lithium-ion battery systems.Herein,an innovative hydrated salt composite phase change material(HSCPCM)with dual phase transition temperature zones has been proposed.This HSCPCM,denoted as SDMA10,combines hydrophilic modified expanded graphite,an acrylic emulsion coating,and eutectic hydrated salts to achieve leakage prevention,enhanced thermal stability,cycling stability,and superior phase change behavior.Battery modules incorporating SDMA10 demonstrate significant thermal control capabilities.Specifically,the cylindrical battery modules with SDMA10 can maintain maximum operating temperatures below 55°C at 4 C discharge rate,while prismatic battery modules can keep maximum operating temperatures below 65°C at 2 C discharge rate.In extreme battery overheating conditions simulated using heating plates,SDMA10 effectively suppresses thermal propagation.Even when the central heating plate reaches 300°C,the maximum temperature at the module edge heating plates remains below 85°C.Further,compared to organic composite phase change materials(CPCMs),the battery module with SDMA10 can further reduce the peak thermal runaway temperature by 93°C and delay the thermal runaway trigger time by 689 s,thereby significantly decreasing heat diffusion.Therefore,the designed HSCPCM integrates excellent latent heat storage and thermochemical storage capabilities,providing high thermal energy storage density within the thermal management and thermal runaway threshold temperature range.This research will offer a promising pathway for improving the thermal safety performance of battery packs in electric vehicles and other energy storage systems.展开更多
Ultrasonic-Assisted Grinding(UAG)is a novel manufacturing technology that shows promising promise for use in processing Ceramic Matrix Composites(CMCs).Nevertheless,analyzing the material removal process of CMCs with ...Ultrasonic-Assisted Grinding(UAG)is a novel manufacturing technology that shows promising promise for use in processing Ceramic Matrix Composites(CMCs).Nevertheless,analyzing the material removal process of CMCs with multidirectional structure during UAG is challenging,impeding the progress and improvement of the UAG process.This work examined the impact of ultrasonic vibration on the dynamic mechanical characteristics during processing.Additionally,we experimentally elucidated the material removal mechanism of CMCs during the scratching process under the influence of vertical vibration.The results indicate that the introduction of ultrasonic vibration causes a strain rate effect,resulting in a modification of the material removal mechanism,subsequently impacting the processing quality.Ultrasonic vibration increases the dynamic strength and brittleness of the fibers in CMCs,leading to more cracks at fracture,which changes from the original bending fracture to shear fracture.In addition,ultrasonic vibration can effectively inhibit the impact of scratching depth and anisotropy on the removal mechanism of CMCs,resulting in a more uniform surface of CMCs after processing.展开更多
In this paper,we present a broadband,high-extinction-ratio,nonvolatile 2×2 Mach-Zehnder interfer⁃ometer(MZI)optical switch based on the phase change material Sb_(2)Se_(3).The insertion loss(IL)is 0.84 dB and the ...In this paper,we present a broadband,high-extinction-ratio,nonvolatile 2×2 Mach-Zehnder interfer⁃ometer(MZI)optical switch based on the phase change material Sb_(2)Se_(3).The insertion loss(IL)is 0.84 dB and the extinction ratio(ER)reaches 28.8 dB at the wavelength of 1550 nm.The 3 dB bandwidth is greater than 150 nm.Within the 3 dB bandwidth,the ER is greater than 20.3 dB and 16.3 dB at bar and cross states,respectively.The power consumption for crystallization and amorphization of Sb_(2)Se_(3) is 105.86 nJ and 49 nJ,respectively.The switch holds significant promise for optical interconnects and optical computing applications.展开更多
Standardization is necessary for the early industrialization of the new materials and technology.It is achieved by having agreed practices for the measurement of properties and other characteristics.The promising use ...Standardization is necessary for the early industrialization of the new materials and technology.It is achieved by having agreed practices for the measurement of properties and other characteristics.The promising use of graphene-based materials in fields like electronics,energy,and composites has resulted in standards for their nomenclature,the measurement of key characteristics,and their specification,etc.Among these,standards for measuring the key characteristics are crucial.The critical parameters are the number of layers,the type and concentration of defects and functional groups,elemental composition,sheet resistance,and carrier mobility.Standards for characterizing these have been analyzed by the International Organization for Standardization Technical Committee in ISO/TC229 and the International Electrotechnical Commission Technical Committee in IEC/TC113.These give details of applicable or preferred samples,the fundamental principles of the techniques,specific precautions,and points for attention in the relevant standards.The pivotal role of the ISO/TC229 and IEC/TC113 standards is considered and challenges and future trends are outlined.展开更多
In response to the global energy crisis and environmental challenges,photocatalytic hydrogen(H_(2))production has emerged as a sustainable alternative toward clean energy conversion.Among diverse photocatalysts invest...In response to the global energy crisis and environmental challenges,photocatalytic hydrogen(H_(2))production has emerged as a sustainable alternative toward clean energy conversion.Among diverse photocatalysts investigated,TiO_(2)-based nanomaterials have attracted significant attention due to their unique physicochemical properties,such as high chemical stability,strong redox capacity and tunable electronic structures,along with high cost-effectiveness.Extensive research on TiO_(2)-based photocatalysts proves their enormous potential in the field of H2 production.This timely and critical review explores the recent advances in TiO_(2)-based photocatalysts,discussing their distinctive advantages and synthesis methods in photocatalytic H2 production.Modification strategies,such as elemental doping(e.g.,precious metals,non-precious metals and non-metals),morphology engineering and composite formation,are summarised to improve photocatalytic efficiency.Advanced in/ex situ characterization techniques employed to probe photocatalytic mechanisms are also highlighted.Finally,major challenges,such as limited visible-light activity and charge recombination,are outlined,with perspectives on emerging TiO_(2)-based nanomaterials and design strategies to overcome current bottlenecks.And the research focus in the future is prospected,such as atomic interface engineering,machine learning auxiliary material design and large-scale preparation technology.This work aims to provide insights into the rational design of TiO_(2)-based photocatalysts for next-generation H2 production systems.展开更多
In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown de...In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform classincremental training.This study proposes a class-incremental open-set SEI approach.The open-set SEI model calculates radiofrequency fingerprints(RFFs)prototypes for known signals and employs a self-attention mechanism to enhance their discriminability.Detection thresholds are set through Gaussian fitting for each class.For class-incremental learning,the algorithm freezes the parameters of the previously trained model to initialize the new model.It designs specific losses:the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss,which force the new model to retain old knowledge while learning new knowledge.The training loss enables learning of new class RFFs.Experimental results demonstrate that the open-set SEI model achieves state-of-theart performance and strong noise robustness.Moreover,the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge,acquire new device RFFs knowledge,and detect unknown devices simultaneously.展开更多
CO_(2) capture and utilization(CCU)technologies have been recognized as crucial strategies for mitigating global warming,reducing carbon emission,and promoting resource circularity.As such,the design and development o...CO_(2) capture and utilization(CCU)technologies have been recognized as crucial strategies for mitigating global warming,reducing carbon emission,and promoting resource circularity.As such,the design and development of related materials have attracted considerable research attention.Carbon-based materials,characterized by tunable pore structures,abundant active sites,high specific surface area,and excellent chemical stability,demonstrate significant potential for applications in CO_(2) capture and utilization.This review systematically analyzes the adsorption behaviors and performance variations of typical carbon materials,including activated carbon,porous carbon,graphene,and carbon nanotubes during CO_(2) capture processes.Concerning CO_(2) utilization,emphasis is placed on recent advances in the catalytic applications of carbon-based materials in key reactions such as methanation,reverse water-gas shift,dry reforming of methane,and alcohol synthesis.Moreover,the benefits and drawbacks of carbon materials in terms of CO_(2) adsorption capacity,catalytic activity,and stability are thoroughly evaluated,and their potential applications in integrated CO_(2) capture and utilization technologies are discussed.Finally,key strategies for enhancing the performance of carbonaceous materials through structural modulation and surface modification are elucidated.This review aims to provide theoretical guidance for the future development and large-scale implementation of carbon-based materials in CCU technologies.展开更多
Moisture electricity generation(MEG)has emerged as a sustainable and versatile energy-harvesting technology capable of converting ubiquitous environmental moisture into electrical energy,which holds great promise for ...Moisture electricity generation(MEG)has emerged as a sustainable and versatile energy-harvesting technology capable of converting ubiquitous environmental moisture into electrical energy,which holds great promise for renewable energy and constructing self-powered electronics.In this review,we begin by outlining the fundamental mechanisms—ion diffusion,electric double layer formation,and streaming potential—that govern charge transport for MEG in moist environments.A comprehensive survey of material innovations follows,highlighting breakthroughs in carbon-based materials,conductive polymers,hydrogels,and bio-inspired systems that enhance MEG performance,scalability,and biocompatibility.We then explore a range of device architectures,from planar and layered systems to flexible,miniaturized,and textile-integrated designs,engineered for both energy conversion and sensor integration.Key challenges are analyzed,along with strategies for overcoming them.We conclude with a forward-looking perspective on future directions,including hybrid energy systems,AI-assisted material design,and real-world deployment.This review presents a timely and comprehensive overview of MEG technologies and their trajectory toward practical and sustainable energy solutions.展开更多
Bi-based transition metal oxide(Bi_(5)Nb_(3)O_(15))has become a highly hopeful anode material for lithium-ion batteries(LIBs)due to its large theoretical capacity and affordable availability.Unfortunately,poor conduct...Bi-based transition metal oxide(Bi_(5)Nb_(3)O_(15))has become a highly hopeful anode material for lithium-ion batteries(LIBs)due to its large theoretical capacity and affordable availability.Unfortunately,poor conductivity,as well as volume expansion and pulverization during repeated reactions will result in bad specific capacity and inferior cycling stability.Hence,Bi_(5)Nb_(3)O_(15)@C anode materials for LIBs were successfully synthesized using sucrose as a carbon source through a two-step high-temperature solid-phase method.Physical characterizations and electrochemical tests suggest that the highly conductive carbon shell derived from sucrose provides fast channels for Li^(+)transport and greatly reduces the charge transfer resistance.Moreover,ex situ scanning electron microscopy(SEM)indicates that the presence of carbon effectively suppresses the aggregation and pulverization of Bi_(5)Nb_(3)O_(15) particles in the reaction process,effectively ensuring the integrity of Bi_(5)Nb_(3)O_(15) particles.Benefiting from the above merits,the C-modified Bi_(5)Nb_(3)O_(15),especially Bi_(5)Nb_(3)O_(15)@8%C(BNO-C3),holds charge capacity of 414.6 and 281.4 mAh·g^(−1) at 0.1 and 0.5 A·g^(−1),respectively.Additionally,the high specific capacity of 379.5 mAh·g^(−1) is much greater than that of the bare Bi_(5)Nb_(3)O_(15)(only 158.7 mAh·g^(−1))after 200 cycles.Importantly,cyclic voltammetry(CV)combined with ex situ X-ray diffraction(XRD)confirms the conversion reaction between Bi_(5)Nb_(3)O_(15) and Bi during cycling.This work provides a method for suppressing volume expansion and pulverization during cycling of Bi-based transition metal oxides and constructing high-performance LIBs anode materials.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
Investments in eco-friendly,recyclable material solutions and innovation in bio-based nonwovens are increasingly shaping the next generation of automotive interiors.The development of nonwoven materials and associated...Investments in eco-friendly,recyclable material solutions and innovation in bio-based nonwovens are increasingly shaping the next generation of automotive interiors.The development of nonwoven materials and associated technologies is likely to lead to even wider adoption in the automotive industry,driven by rising global vehicle production,particularly in the growing electric vehicle(EV)segment,and an intensified focus on sustainable solutions.展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers(Z values),facilitating the identification of various Z-class materials,particularly r...Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers(Z values),facilitating the identification of various Z-class materials,particularly radioactive high-Z nuclear elements.Most traditional identification methods are based on complex statistical iterative reconstruction or simple trajectory approximation.Supervised machine learning methods offer some improvement but rely heavily on prior knowledge of the target materials,significantly limiting their practical applicability in detecting concealed materials.To the best of our knowledge,this is the first study to introduce transfer learning into muon tomography.We propose two lightweight neural network models for fine-tuning and adversarial transfer learning,utilizing muon scattering data of bare materials to predict the Z-class of materials coated by typical shieldings(e.g.,aluminum or polyethylene),simulating practical scenarios such as cargo inspection and arms control.By introducing a novel inverse cumulative distribution-based sampling method,more accurate scattering angle distributions could be obtained from the data,leading to an improvement of nearly 4% in prediction accuracy compared with the traditional random sampling-based training.When applied to coated materials with limited labeled or even unlabeled muon tomography data,the proposed method achieved an overall prediction accuracy exceeding 96%,with high-Z materials reaching nearly 99%.The simulation results indicate that transfer learning improves the prediction accuracy by approximately 10% compared to direct prediction without transfer.This study demonstrates the effectiveness of transfer learning in overcoming the physical challenges associated with limited labeled/unlabeled data and highlights the promising potential of transfer learning in the field of muon tomography.展开更多
Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously...Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously monitor transient changes in both sensor conductivity and temperature. The distinct response profiles of H_(2) and NH_(3) gases were attributed to differences in their redox rates and enthalpy changes during chemical reactions, which provided an opportunity for gas identification using machine learning(ML) algorithms. The test results indicate that preprocessing the extracted calorimetric and chemi-resistive parameters using the principal component analysis(PCA), followed by the application of ML classifiers for identification,enables a 100% accuracy for both target analytes. This work presents a facile gas identification method that enhances chiplevel sensor applications while minimizing the need for complex sensor arrays.展开更多
In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digesti...In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digestive system worldwide. Currently, CRC clinical diagnosis and treatment face challenges such as high costs and persistently high recurrence rates. Traditional quantification of tumor-infiltrating lymphocytes (TILs) relies on manual analysis and judgment, resulting in low diagnostic efficiency and susceptibility to subjective factors, leading to missed or misdiagnosed cases. To enhance the efficiency and quality of CRC clinical diagnosis and treatment, this study explores domestic and international research on the automatic identification of CRC cells using machine learning strategies. It analyzes the morphological heterogeneity and prognostic value in the application of this strategy, aiming to deepen the understanding of intelligent tool applications in precise diagnosis, treatment, and prognostic evaluation of colorectal cancer, comprehend the current research status and development trends, and provide references for addressing and addressing the gaps in related research.展开更多
The reactive materials filled structure(RMFS)is a structural penetrator that replaces high explosive(HE)with reactive materials,presenting a novel self-distributed initiation,multiple deflagrations behavior during pen...The reactive materials filled structure(RMFS)is a structural penetrator that replaces high explosive(HE)with reactive materials,presenting a novel self-distributed initiation,multiple deflagrations behavior during penetrating multi-layered plates,and generating a multipeak overpressure behind the plates.Here analytical models of RMFS self-distributed energy release and equivalent deflagration are developed.The multipeak overpressure formation model based on the single deflagration overpressure expression was promoted.The impact tests of RMFS on multi-layered plates at 584 m/s,616 m/s,and819 m/s were performed to validate the analytical model.Further,the influence of a single overpressure peak and time intervals versus impact velocity is discussed.The analysis results indicate that the deflagration happened within 20.68 mm behind the plate,the initial impact velocity and plate thickness are the crucial factors that dominate the self-distributed multipeak overpressure effect.Three formation patterns of multipeak overpressure are proposed.展开更多
基金supported by the grants from National Natural Science Foundation of China(Nos.22278091,22278046)Young Elite Sci-entists Sponsorship Program by CAST(No.2024QNRC0387)+2 种基金the Guangxi Natural Science Foundation of China(Nos.2025GXNSFBA069146,2023GXNSFGA026001,GKAD25069076)the Foundation(No.202403)of Tianjin Key Laboratory of Pulp&Paper(Tianjin University of Science&Technology)P.R.China,and the Foundation of Guangxi Key Laboratory of Clean Pulp&Papermaking and Pollution Control,College of Light Industry and Food Engineering,Guangxi University(No.2021KF01).
文摘With the continuous advancement of social technology and the increasing awareness of health management,biomass-based triboelectric nanogenerator(TENG)displayed significant potential as flexible wearable electronics for continuous foot gait monitoring.Nevertheless,existing biomass-based TENG often faces challenges of insufficient mechanical robustness and durability in practical applications,where they are prone to surface abrasion and structural fracture under continuous compression and friction,severely limiting their long-term performances.In order to address these challenges,this work proposed a multiscale crosslinking strategy,which strengthened the noncovalent interactions within the polymer by constructing multiple reinforcement networks,successfully fabricating a dual-network C-lignin-based triboelectric material(CLTM)with excellent durability and crack resistance.Among them,the optimal CLTM(PSGCL-0.2)exhibited high mechanical strength(strain 445%,tensile strength 41.56 MPa,Young's modulus 41.25 MPa,toughness 159.67 MJ/m^(3))and excellent cyclic stability(300 cycles)with versatile functionalities,including antibacterial,antioxidant,and UV-shielding properties,water stabilization(255.51 g/m^(2)/d),efficient photothermal conversion,and full recyclability.Furthermore,biomass-based TENG device assembled from PSGCL-0.2 achieved stable triboelectric output properties(102.5 V,2.9μA,and 61.3 nC),and sustainable for 2000 cycles,fast response time(68 ms),and excellent power density(325.9 mW/m^(2)),effectively converting mechanical energy into electrical energy.Especially,PSGCL 0.2 was also integrated into the wireless self-powered smart insole,successfully enabling real-time visual monitoring of plantar pressure distribution and dynamic gait.Meanwhile,combined with the machine learning algorithm,the self-powered smart insole achieved precise recognition and classification of eight different motion states with an accuracy of 98%.This study provides the feasible strategy for developing extremely stable and durable biomass-based TENG,aimed at advancing sustainable intelligent healthcare systems.
基金supported by the National Key Research and Development Program(No.2022YFA1603300)the National Natural Science Foundation of China(Nos.U2230133,12305266,11921006,12405282)National Grand Instrument Project(No.2019YFF01014400)。
文摘The illicit trafficking of special nuclear materials(SNMs)poses a grave threat to global security and necessitates the development of effective nuclear material identification methods.This study investigated a method to isotopically identify the SNMs,including^(233,235,238)U,^(239-242)Pu,and^(232)Th,based on the detection of delayedγ-rays from photofission fragments.The delayedγ-ray spectra resulting from the photofission of SNMs irradiated by a 14 MeVγbeam with a total of 10~9 were simulated using Geant4.Three high-yield fission fragments,namely^(138)Cs,^(89)Rb,and^(94)Y,were selected as candidate fragments for SNM identification.The yield ratios of these three fragments were calculated,and the results from the different SNMs were compared.The yield ratio of^(138)Cs/^(89)Rb was used to identify most SNMs,including^(233,235,238)U,^(242)Pu,and^(232)Th,with a confidence level above 95%.To identify^(239-241)Pu with the same confidence,a higher total number of 10^(11)γbeams is required.However,although the^(94)Y/^(89)Rb ratio is suitable for elementally identifying SNMs,isotopic identification is difficult.In addition,the count rate of the delayedγabove 3 MeV can be used to rapidly detect the presence of nuclear materials.
基金Supported by Drug Research and Development Plan Project of Drug Administration of Guangxi Zhuang Autonomous Region(GYJKZS[2023]033)Research Project on the Formulation and Revision of National Drug Standards by National Pharmacopoeia Commission(BZ2022134).
文摘[Objectives]To further revise and enhance the quality standards of the Yao medicinal material Gendarussa vulgaris,and to provide empirical data to ensure the safety of G.vulgaris medicinal resources and the advancement of medicinal material standards.[Methods]The identification of G.vulgaris was conducted through morphological,microscopic,and physicochemical analyses.Parameters such as moisture content,total ash,acid-insoluble ash,extract content,heavy metal concentrations,and organochlorine pesticide residues were determined in accordance with the 2025 edition of the Chinese Pharmacopoeia.[Results]The distinguishing characteristics of G.vulgaris were clearly evident,allowing for the establishment of an identification method for this species.The thin-layer chromatogram exhibited clear and well-resolved separation.Additionally,the permissible ranges for moisture content,total ash,and water-soluble extract in the medicinal materials were defined,while new limit standards for acid-insoluble ash and heavy metal content were concurrently established.[Conclusions]The test method established in this study is both accurate and reliable,thereby enhancing the quality standards of the medicinal material G.vulgaris.
基金Supported by Guangxi Degree and Postgraduate Education Reform Project(JGY2023213,JGY2023211).
文摘In order to improve the traditional teaching model of traditional Chinese medicine identification course for graduate students of pharmacy,this paper described the research on constructing and practicing the blended teaching model of"online+offline"based on the teaching concept of TfU by taking the course of"Authentic Medicinal Materials and Quality of Traditional Chinese Medicine"as an example.In the preparatory phase,through resource integration and course content decomposition,it identifies"generative topics"to engage students in pre-class online discussions.During the instructional phase,"comprehension-oriented objectives"are established based on learning analytics,followed by the implementation of"understanding-focused activities"for guided inquiry in offline classrooms.The post-class phase employs online extended materials to conduct"sustained assessment"through evaluations and summaries,thereby continuously optimizing subsequent teaching practices.This pedagogical framework not only effectively cultivates investigative research thinking among graduate students but also enhances standardized management and scientific development of the teaching team.The practical research outcomes and experiences derived from this model can provide valuable references for analogous course reforms.
基金financially supported by Natural Science Foundation of Guangdong province(2024A1515010228)CATARC Automotive Inspection Center Excellent Engineer Program(2023B0909050007).
文摘To address the challenge of balancing thermal management and thermal runaway mitigation,it is crucial to explore effective methods for enhancing the safety of lithium-ion battery systems.Herein,an innovative hydrated salt composite phase change material(HSCPCM)with dual phase transition temperature zones has been proposed.This HSCPCM,denoted as SDMA10,combines hydrophilic modified expanded graphite,an acrylic emulsion coating,and eutectic hydrated salts to achieve leakage prevention,enhanced thermal stability,cycling stability,and superior phase change behavior.Battery modules incorporating SDMA10 demonstrate significant thermal control capabilities.Specifically,the cylindrical battery modules with SDMA10 can maintain maximum operating temperatures below 55°C at 4 C discharge rate,while prismatic battery modules can keep maximum operating temperatures below 65°C at 2 C discharge rate.In extreme battery overheating conditions simulated using heating plates,SDMA10 effectively suppresses thermal propagation.Even when the central heating plate reaches 300°C,the maximum temperature at the module edge heating plates remains below 85°C.Further,compared to organic composite phase change materials(CPCMs),the battery module with SDMA10 can further reduce the peak thermal runaway temperature by 93°C and delay the thermal runaway trigger time by 689 s,thereby significantly decreasing heat diffusion.Therefore,the designed HSCPCM integrates excellent latent heat storage and thermochemical storage capabilities,providing high thermal energy storage density within the thermal management and thermal runaway threshold temperature range.This research will offer a promising pathway for improving the thermal safety performance of battery packs in electric vehicles and other energy storage systems.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(No.52325506)the Fundamental Research Funds for the Central Universities(No.DUT22LAB501)。
文摘Ultrasonic-Assisted Grinding(UAG)is a novel manufacturing technology that shows promising promise for use in processing Ceramic Matrix Composites(CMCs).Nevertheless,analyzing the material removal process of CMCs with multidirectional structure during UAG is challenging,impeding the progress and improvement of the UAG process.This work examined the impact of ultrasonic vibration on the dynamic mechanical characteristics during processing.Additionally,we experimentally elucidated the material removal mechanism of CMCs during the scratching process under the influence of vertical vibration.The results indicate that the introduction of ultrasonic vibration causes a strain rate effect,resulting in a modification of the material removal mechanism,subsequently impacting the processing quality.Ultrasonic vibration increases the dynamic strength and brittleness of the fibers in CMCs,leading to more cracks at fracture,which changes from the original bending fracture to shear fracture.In addition,ultrasonic vibration can effectively inhibit the impact of scratching depth and anisotropy on the removal mechanism of CMCs,resulting in a more uniform surface of CMCs after processing.
基金Supported by the National Natural Science Foundation of China(62204250)Autonomous deployment project of State Key Laboratory of Materials for Integrated Circuits(SKLJC-Z2024-A05).
文摘In this paper,we present a broadband,high-extinction-ratio,nonvolatile 2×2 Mach-Zehnder interfer⁃ometer(MZI)optical switch based on the phase change material Sb_(2)Se_(3).The insertion loss(IL)is 0.84 dB and the extinction ratio(ER)reaches 28.8 dB at the wavelength of 1550 nm.The 3 dB bandwidth is greater than 150 nm.Within the 3 dB bandwidth,the ER is greater than 20.3 dB and 16.3 dB at bar and cross states,respectively.The power consumption for crystallization and amorphization of Sb_(2)Se_(3) is 105.86 nJ and 49 nJ,respectively.The switch holds significant promise for optical interconnects and optical computing applications.
文摘Standardization is necessary for the early industrialization of the new materials and technology.It is achieved by having agreed practices for the measurement of properties and other characteristics.The promising use of graphene-based materials in fields like electronics,energy,and composites has resulted in standards for their nomenclature,the measurement of key characteristics,and their specification,etc.Among these,standards for measuring the key characteristics are crucial.The critical parameters are the number of layers,the type and concentration of defects and functional groups,elemental composition,sheet resistance,and carrier mobility.Standards for characterizing these have been analyzed by the International Organization for Standardization Technical Committee in ISO/TC229 and the International Electrotechnical Commission Technical Committee in IEC/TC113.These give details of applicable or preferred samples,the fundamental principles of the techniques,specific precautions,and points for attention in the relevant standards.The pivotal role of the ISO/TC229 and IEC/TC113 standards is considered and challenges and future trends are outlined.
文摘In response to the global energy crisis and environmental challenges,photocatalytic hydrogen(H_(2))production has emerged as a sustainable alternative toward clean energy conversion.Among diverse photocatalysts investigated,TiO_(2)-based nanomaterials have attracted significant attention due to their unique physicochemical properties,such as high chemical stability,strong redox capacity and tunable electronic structures,along with high cost-effectiveness.Extensive research on TiO_(2)-based photocatalysts proves their enormous potential in the field of H2 production.This timely and critical review explores the recent advances in TiO_(2)-based photocatalysts,discussing their distinctive advantages and synthesis methods in photocatalytic H2 production.Modification strategies,such as elemental doping(e.g.,precious metals,non-precious metals and non-metals),morphology engineering and composite formation,are summarised to improve photocatalytic efficiency.Advanced in/ex situ characterization techniques employed to probe photocatalytic mechanisms are also highlighted.Finally,major challenges,such as limited visible-light activity and charge recombination,are outlined,with perspectives on emerging TiO_(2)-based nanomaterials and design strategies to overcome current bottlenecks.And the research focus in the future is prospected,such as atomic interface engineering,machine learning auxiliary material design and large-scale preparation technology.This work aims to provide insights into the rational design of TiO_(2)-based photocatalysts for next-generation H2 production systems.
基金supported by the National Natural Science Foundation of China(62371465)Taishan Scholar Project of Shandong Province(ts201511020)。
文摘In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform classincremental training.This study proposes a class-incremental open-set SEI approach.The open-set SEI model calculates radiofrequency fingerprints(RFFs)prototypes for known signals and employs a self-attention mechanism to enhance their discriminability.Detection thresholds are set through Gaussian fitting for each class.For class-incremental learning,the algorithm freezes the parameters of the previously trained model to initialize the new model.It designs specific losses:the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss,which force the new model to retain old knowledge while learning new knowledge.The training loss enables learning of new class RFFs.Experimental results demonstrate that the open-set SEI model achieves state-of-theart performance and strong noise robustness.Moreover,the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge,acquire new device RFFs knowledge,and detect unknown devices simultaneously.
基金Supported by National Key R&D Program of China(2025YFE0109700)the National Natural Science Foundation of China(52106150)。
文摘CO_(2) capture and utilization(CCU)technologies have been recognized as crucial strategies for mitigating global warming,reducing carbon emission,and promoting resource circularity.As such,the design and development of related materials have attracted considerable research attention.Carbon-based materials,characterized by tunable pore structures,abundant active sites,high specific surface area,and excellent chemical stability,demonstrate significant potential for applications in CO_(2) capture and utilization.This review systematically analyzes the adsorption behaviors and performance variations of typical carbon materials,including activated carbon,porous carbon,graphene,and carbon nanotubes during CO_(2) capture processes.Concerning CO_(2) utilization,emphasis is placed on recent advances in the catalytic applications of carbon-based materials in key reactions such as methanation,reverse water-gas shift,dry reforming of methane,and alcohol synthesis.Moreover,the benefits and drawbacks of carbon materials in terms of CO_(2) adsorption capacity,catalytic activity,and stability are thoroughly evaluated,and their potential applications in integrated CO_(2) capture and utilization technologies are discussed.Finally,key strategies for enhancing the performance of carbonaceous materials through structural modulation and surface modification are elucidated.This review aims to provide theoretical guidance for the future development and large-scale implementation of carbon-based materials in CCU technologies.
基金supported by the National Natural Science Foundation of China(52305388,BE0200030)Shanghai Pujiang Program(22PJ1407600)+1 种基金SJTU Explore X programShanghai Jiao Tong University Initiative Scientific Research Program(WH220402021)。
文摘Moisture electricity generation(MEG)has emerged as a sustainable and versatile energy-harvesting technology capable of converting ubiquitous environmental moisture into electrical energy,which holds great promise for renewable energy and constructing self-powered electronics.In this review,we begin by outlining the fundamental mechanisms—ion diffusion,electric double layer formation,and streaming potential—that govern charge transport for MEG in moist environments.A comprehensive survey of material innovations follows,highlighting breakthroughs in carbon-based materials,conductive polymers,hydrogels,and bio-inspired systems that enhance MEG performance,scalability,and biocompatibility.We then explore a range of device architectures,from planar and layered systems to flexible,miniaturized,and textile-integrated designs,engineered for both energy conversion and sensor integration.Key challenges are analyzed,along with strategies for overcoming them.We conclude with a forward-looking perspective on future directions,including hybrid energy systems,AI-assisted material design,and real-world deployment.This review presents a timely and comprehensive overview of MEG technologies and their trajectory toward practical and sustainable energy solutions.
基金supported by the National Natural Science Foundation of China(No.52374301)Hebei Provincial Natural Science Foundation(No.E2024501010)+2 种基金Shijiazhuang Basic Research Project(No.241790667A)the Fundamental Research Funds for the Central Universities(No.N2423054)the Performance Subsidy Fund for Key Laboratory of Dielectric and Electrolyte Functional Material Hebei Province(No.22567627H).
文摘Bi-based transition metal oxide(Bi_(5)Nb_(3)O_(15))has become a highly hopeful anode material for lithium-ion batteries(LIBs)due to its large theoretical capacity and affordable availability.Unfortunately,poor conductivity,as well as volume expansion and pulverization during repeated reactions will result in bad specific capacity and inferior cycling stability.Hence,Bi_(5)Nb_(3)O_(15)@C anode materials for LIBs were successfully synthesized using sucrose as a carbon source through a two-step high-temperature solid-phase method.Physical characterizations and electrochemical tests suggest that the highly conductive carbon shell derived from sucrose provides fast channels for Li^(+)transport and greatly reduces the charge transfer resistance.Moreover,ex situ scanning electron microscopy(SEM)indicates that the presence of carbon effectively suppresses the aggregation and pulverization of Bi_(5)Nb_(3)O_(15) particles in the reaction process,effectively ensuring the integrity of Bi_(5)Nb_(3)O_(15) particles.Benefiting from the above merits,the C-modified Bi_(5)Nb_(3)O_(15),especially Bi_(5)Nb_(3)O_(15)@8%C(BNO-C3),holds charge capacity of 414.6 and 281.4 mAh·g^(−1) at 0.1 and 0.5 A·g^(−1),respectively.Additionally,the high specific capacity of 379.5 mAh·g^(−1) is much greater than that of the bare Bi_(5)Nb_(3)O_(15)(only 158.7 mAh·g^(−1))after 200 cycles.Importantly,cyclic voltammetry(CV)combined with ex situ X-ray diffraction(XRD)confirms the conversion reaction between Bi_(5)Nb_(3)O_(15) and Bi during cycling.This work provides a method for suppressing volume expansion and pulverization during cycling of Bi-based transition metal oxides and constructing high-performance LIBs anode materials.
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.
文摘Investments in eco-friendly,recyclable material solutions and innovation in bio-based nonwovens are increasingly shaping the next generation of automotive interiors.The development of nonwoven materials and associated technologies is likely to lead to even wider adoption in the automotive industry,driven by rising global vehicle production,particularly in the growing electric vehicle(EV)segment,and an intensified focus on sustainable solutions.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金supported by the Research Program of State Key Laboratory of Heavy Ion Science and Technology,Institute of Modern Physics,Chinese Academy of Sciences(No.HIST2025CS06)the National Natural Science Foundation of China(Nos.12405402,12475106,12105327,and 12405337)the Guangdong Basic and Applied Basic Research Foundation,China(No.2023B1515120067)。
文摘Cosmic-ray muon sources exhibit distinct scattering angle distributions when interacting with materials of different atomic numbers(Z values),facilitating the identification of various Z-class materials,particularly radioactive high-Z nuclear elements.Most traditional identification methods are based on complex statistical iterative reconstruction or simple trajectory approximation.Supervised machine learning methods offer some improvement but rely heavily on prior knowledge of the target materials,significantly limiting their practical applicability in detecting concealed materials.To the best of our knowledge,this is the first study to introduce transfer learning into muon tomography.We propose two lightweight neural network models for fine-tuning and adversarial transfer learning,utilizing muon scattering data of bare materials to predict the Z-class of materials coated by typical shieldings(e.g.,aluminum or polyethylene),simulating practical scenarios such as cargo inspection and arms control.By introducing a novel inverse cumulative distribution-based sampling method,more accurate scattering angle distributions could be obtained from the data,leading to an improvement of nearly 4% in prediction accuracy compared with the traditional random sampling-based training.When applied to coated materials with limited labeled or even unlabeled muon tomography data,the proposed method achieved an overall prediction accuracy exceeding 96%,with high-Z materials reaching nearly 99%.The simulation results indicate that transfer learning improves the prediction accuracy by approximately 10% compared to direct prediction without transfer.This study demonstrates the effectiveness of transfer learning in overcoming the physical challenges associated with limited labeled/unlabeled data and highlights the promising potential of transfer learning in the field of muon tomography.
基金supported in part by the National Natural Science Foundation of China (62431018)in part by the Guangzhou Municipal Science and Technology Bureau (SL2023A04J00435)in part by the One Hundred Youth Project of Guangdong University of Technology (263113873)。
文摘Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously monitor transient changes in both sensor conductivity and temperature. The distinct response profiles of H_(2) and NH_(3) gases were attributed to differences in their redox rates and enthalpy changes during chemical reactions, which provided an opportunity for gas identification using machine learning(ML) algorithms. The test results indicate that preprocessing the extracted calorimetric and chemi-resistive parameters using the principal component analysis(PCA), followed by the application of ML classifiers for identification,enables a 100% accuracy for both target analytes. This work presents a facile gas identification method that enhances chiplevel sensor applications while minimizing the need for complex sensor arrays.
文摘In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digestive system worldwide. Currently, CRC clinical diagnosis and treatment face challenges such as high costs and persistently high recurrence rates. Traditional quantification of tumor-infiltrating lymphocytes (TILs) relies on manual analysis and judgment, resulting in low diagnostic efficiency and susceptibility to subjective factors, leading to missed or misdiagnosed cases. To enhance the efficiency and quality of CRC clinical diagnosis and treatment, this study explores domestic and international research on the automatic identification of CRC cells using machine learning strategies. It analyzes the morphological heterogeneity and prognostic value in the application of this strategy, aiming to deepen the understanding of intelligent tool applications in precise diagnosis, treatment, and prognostic evaluation of colorectal cancer, comprehend the current research status and development trends, and provide references for addressing and addressing the gaps in related research.
基金the support received from the National Natural Science Foundation of China(Grant No.12302460)the State Key Laboratory of Explosion Science and Safety Protection(Grant No.YBKT24-02)。
文摘The reactive materials filled structure(RMFS)is a structural penetrator that replaces high explosive(HE)with reactive materials,presenting a novel self-distributed initiation,multiple deflagrations behavior during penetrating multi-layered plates,and generating a multipeak overpressure behind the plates.Here analytical models of RMFS self-distributed energy release and equivalent deflagration are developed.The multipeak overpressure formation model based on the single deflagration overpressure expression was promoted.The impact tests of RMFS on multi-layered plates at 584 m/s,616 m/s,and819 m/s were performed to validate the analytical model.Further,the influence of a single overpressure peak and time intervals versus impact velocity is discussed.The analysis results indicate that the deflagration happened within 20.68 mm behind the plate,the initial impact velocity and plate thickness are the crucial factors that dominate the self-distributed multipeak overpressure effect.Three formation patterns of multipeak overpressure are proposed.
