Low-dimensional(LD)halide perovskites have attracted considerable attention due to their distinctive structures and exceptional optoelectronic properties,including high absorption coefficients,extended charge carrier ...Low-dimensional(LD)halide perovskites have attracted considerable attention due to their distinctive structures and exceptional optoelectronic properties,including high absorption coefficients,extended charge carrier diffusion lengths,suppressed non-radiative recombination rates,and intense photoluminescence.A key advantage of LD perovskites is the tunability of their optical and electronic properties through the precise optimization of their structural arrangements and dimensionality.This review systematically examines recent progress in the synthesis and optoelectronic characterizations of LD perovskites,focusing on their structural,optical,and photophysical properties that underpin their versatility in diverse applications.The review further summarizes advancements in LD perovskite-based devices,including resistive memory,artificial synapses,photodetectors,light-emitting diodes,and solar cells.Finally,the challenges associated with stability,scalability,and integration,as well as future prospects,are discussed,emphasizing the potential of LD perovskites to drive breakthroughs in device efficiency and industrial applicability.展开更多
Carbon-based low-dimensional materials(CLDM)with elemental carbon as the main component have unique physical and chemical properties,and become the focus of research in many fields including energy,environmental prote...Carbon-based low-dimensional materials(CLDM)with elemental carbon as the main component have unique physical and chemical properties,and become the focus of research in many fields including energy,environmental protection,and information technology.Notably,cellulose acetate,the main component of cigarette butts(CBs),is a one-dimensional precursor with a large specific surface area and aspect ratio.Still,their usefulness as building fillers has often been underestimated before.This review summarizes recent advances in CBs recycling and provides suggested guidelines for its use as a CLDM material in renewable energy.Specifically,we first describe the harmful effects of CBs as pollutants in our lives to emphasize the importance of proper recycling.We then summarize previous methods of recycling CBs waste,including clay bricks,asphalt concrete pavement,gypsum,acoustic materials,chemisorption,vector control,and corrosion control.The potential applications of CBs include triboelectric nanogenerator applications,flexible batteries,enhanced metal-organic framework material energy storage devices,and carbon-based hydrogen storage.Finally,the advantages of utilizing CBs-derived CLDM materials over conventional solutions in the energy field are discussed.This review will provide new avenues for solving the intractable problem of CBs and reducing the manufacturing costs of renewable materials.展开更多
Linearly polarized photodetectors(PDs),leveraging the inherent structural and material information encoded in light's polarization state,hold transformative potential for applications ranging from remote sensing t...Linearly polarized photodetectors(PDs),leveraging the inherent structural and material information encoded in light's polarization state,hold transformative potential for applications ranging from remote sensing to biomedical imaging.Traditional systems that rely on external polarizing elements face challenges in miniaturization and efficiency,driving interest in materials with intrinsic anisotropy.Low-dimensional metal halide perovskites,distinguished by their tunable bandgaps,high carrier mobility,and quantum confinement effects,have emerged as a groundbreaking platform for next-generation polarized PDs.This review comprehensively summarizes the theory,materials,and device engineering of linearly polarized PDs based on low-dimensional perovskites.It aims to elucidate polarization mechanisms across dimensions by establishing a rigorous theoretical foundation for linearly polarized PDs of low-dimensional perovskites.Beyond theoretical insights,the review also highlights cutting-edge fabrication techniques for one-dimensional nano wires and two-dimensional heterostructures,along with performance benchmarks of state-of-the-art devices.By integrating experimental advancements with theoretical insights,this work not only advances the fundamental understanding of polarization mechanisms but also outlines actionable pathways for optimizing device performance,stability,and scalability,which may serve as a critical resource for researchers aiming to harness the full potential of low-dimensional perovskites in polarized optoelectronics.展开更多
Bacterial infections have always been a major threat to human health.Skin wounds are frequently exposed to the external environment,and they may become contaminated by bacteria derived from the surrounding skin,the lo...Bacterial infections have always been a major threat to human health.Skin wounds are frequently exposed to the external environment,and they may become contaminated by bacteria derived from the surrounding skin,the local environment,and the patient’s own endogenous sources.Contaminated wounds may enter a state of chronic inflammation that impedes healing.Urgent development of antibacterial wound dressings capable of effectively combating bacteria and overcoming resistance is necessary.Nanotechnology and nanomaterials present promising potential as innovative strategies for antimicrobial wound dressings,owing to their robust antibacterial characteristics and the inherent advantage of avoiding antibiotic resistance.Therefore,this review provides a concise overview of the antimicrobial mechanisms exhibited by low-dimensional nanomaterials.It further categorizes common low-dimensional antimicrobial nanomaterials into zero-dimensional(0D),one-dimensional(1D)and two-dimensional(2D)nanomaterials based on their structural characteristics,and gives a detailed compendium of the latest research advances and applications of different low-dimensional antimicrobial nanomaterials in wound healing,which could be helpful for the development of more effective wound dressings.展开更多
The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware...The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware with on-chip parameter tunability,which directly accelerates machine learning functions.This work demonstrates a continuously tunable mixed-kernel function physically realized within a van der Waals heterostructure.We designed and fabricated a MoTe_(2)/MoS_(2)type-Ⅱvertical heterojunction phototransistor,which exhibits a non-monotonic,Gaussian-like optoelectronic response owing to its unique inter-layer charge transfer mechanism.This intrinsic physical behavior directly maps to a mixed-kernel function combining Gaussian and Sigmoid characteristics.Furthermore,the hardware kernel can be continuously modulated by in-situ tuning of external opti-cal stimuli.The mixed-kernel exhibited exceptional performance,achieving precision,accuracy,and area under the curve(AUC)values of 95.8%,96%,and 0.9986,respectively,significantly outperforming conventional kernels.By successfully embedding a complex,adaptable mathematical function into the intrinsic physical properties of a single device,this work pioneers a novel pathway toward next-generation,energy-efficient intelligent systems with hardware-level adaptability.展开更多
Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural netwo...Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.展开更多
The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differ...The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differential privacy.Specifically,the authors introduce personalized differentially private support vector machines to meet different individuals'privacy requirements,using a reweighting strategy and the Laplace mechanism.Theoretical analysis demonstrates that the proposed methods simultaneously satisfy the requirements of personalized differential privacy and ensure model prediction accuracy at these privacy levels.Extensive experiments demonstrate that the proposed methods outperform the existing methods.展开更多
The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates ...The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.展开更多
Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme ...Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme of co-directional secondary flow was designed based on a 30 kgf thrust turbojet engine,an equivalent rudder deflection control variable of Mass Flow Combination(MFC)was proposed,and a control model was established to form a FTV control system scheme,which was integrated with the flight control system of a 100 kg tailless flying wing with medium aspect ratio to achieve closed-loop control of the yaw attitude based on FTV.The heading stability augmentation and maneuvering control characteristics and time response characteristics of tailless flying wing by FTV were quantitatively studied through virtual flight test in a wind tunnel at a wind speed of 35 m/s.The results show that the control strategy based on MFC achieves bidirectional continuous and stable control of thrust vector angle in a range of±11°,and the thrust vector angle varies monotonically with MFC;the co-directional FTV realizes bidirectional continuous and stable control of the yaw attitude of tailless flying wing,without longitudinal/lateral coupling moment.The increment of the maximum yawing moment coefficient is 0.0029,the maximum yaw rate is 7.55(°)/s,and the response time of the yaw rate of the vectoring nozzle actuated by the secondary flow is about 0.06 s,which satisfies the heading stability augmentation and maneuvering control response requirements of the aircraft with statically unstable heading,and provides new control means for the heading rudderless attitude control of tailless flying wing.展开更多
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc...The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.展开更多
In position-sensorless brushless direct current(DC)motors(BLDCMs)fed by a four-switch three-phase(FSTP)inverter,only two phases are fully controlled,while the remaining phase is tied to the midpoint of the split DC-li...In position-sensorless brushless direct current(DC)motors(BLDCMs)fed by a four-switch three-phase(FSTP)inverter,only two phases are fully controlled,while the remaining phase is tied to the midpoint of the split DC-link capacitors.The voltage pulses required by inductance-based initial position detection can cause unequal discharge of the series capacitors,shifting the neutral-point voltage away from half of DC-link voltage(U_(dc)/2).This neutral-point drift breaks the spatial symmetry of the inverter voltage vectors,so the 360°electrical period can no longer be evenly partitioned into six sectors during initial rotor position detection.To address this issue,this paper proposes a detection-pulse injection sequence that explicitly accounts for the asymmetric voltage vectors of the FSTP inverter.With the proposed sequence,the initial rotor position can be identified within a 30°electrical sector.The method requires no additional voltage or current sensors,and experimental results confirm its feasibility.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonne...In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.展开更多
Some kinds of low-dimensional nanostructures can be formed by irradiation of laser on the pure silicon sample and the SiGe alloy sample. This paper has studied the photoluminescence （PL） of the hole-net structure of...Some kinds of low-dimensional nanostructures can be formed by irradiation of laser on the pure silicon sample and the SiGe alloy sample. This paper has studied the photoluminescence （PL） of the hole-net structure of silicon and the porous structure of SiGe where the PL intensity at 706nm and 725nm wavelength increases obviously. The effect of intensity-enhancing in the PL peaks cannot be explained within the quantum confinement alone. A mechanism for increasing PL emission in the above structures is proposed, in which the trap states of the interface between SiO2 and nanocrystal play an important role.展开更多
Metal halide perovskites are crystalline materials originally developed out of scientific curiosity. They have shown great potential as active materials in optoelectronic applications. In the last 6 years, their certi...Metal halide perovskites are crystalline materials originally developed out of scientific curiosity. They have shown great potential as active materials in optoelectronic applications. In the last 6 years, their certified photovoltaic efficiencies have reached 22.1%. Compared to bulk halide perovskites, low-dimensional ones exhibited novel physical properties. The photoluminescence quantum yields of perovskite quantum dots are close to 100%. The external quantum efficiencies and current efficiencies of perovskite quantum dot light-emitting diodes have reached 8% and 43 cd A^(-1),respectively, and their nanowire lasers show ultralow-threshold room-temperature lasing with emission tunability and ease of synthesis. Perovskite nanowire photodetectors reached a responsivity of 10 A W^(-1)and a specific normalized detectivity of the order of 10^(12 )Jones. Different from most reported reviews focusing on photovoltaic applications, we summarize the rapid progress in the study of low-dimensional perovskite materials, as well as their promising applications in optoelectronic devices. In particular, we review the wide tunability of fabrication methods and the state-of-the-art research outputs of low-dimensional perovskite optoelectronic devices. Finally, the anticipated challenges and potential for this exciting research are proposed.展开更多
In recent years,low-dimensional materials have received extensive attention in the field of electronics and optoelectronics.Among them,photoelectric devices based on photoconductive effect in low-dimensional materials...In recent years,low-dimensional materials have received extensive attention in the field of electronics and optoelectronics.Among them,photoelectric devices based on photoconductive effect in low-dimensional materials have a broad development space.In contrast to positive photoconductivity,negative photoconductivity(NPC)refers to a phenomenon that the conductivity decreases under illumination.It has novel application prospects in the field of optoelectronics,memory,and gas detection,etc.In this paper,we review reports about the NPC effect in low-dimensional materials and systematically summarize the mechanisms to form the NPC effect in existing low-dimensional materials.展开更多
The photovoltaic(PV)market is currently dominated by silicon based solar cells.However technological diversification is essential to promote competition,which is the driving force for technological growth.Historically...The photovoltaic(PV)market is currently dominated by silicon based solar cells.However technological diversification is essential to promote competition,which is the driving force for technological growth.Historically,the choice of PV materials has been limited to the three-dimensional(3D)compounds with a high crystal symmetry and direct band gap.However,to meet the strict demands for sustainable PV applications,material space has been expanded beyond 3D compounds.In this perspective we discuss the potential of low-dimensional materials(2D,1D)for application in PVs.We present unique features of low-dimensional materials in context of their suitability in the solar cells.The band gap,absorption,carrier dynamics,mobility,defects,surface states and growth kinetics are discussed and compared to 3D counterparts,providing a comprehensive view of prospects of low-dimensional materials.Structural dimensionality leads to a highly anisotropic carrier transport,complex defect chemistry and peculiar growth dynamics.By providing fundamental insights into these challenges we aim to deepen the understanding of low-dimensional materials and expand the scope of their application.Finally,we discuss the current research status and development trend of solar cell devices made of low-dimensional materials.展开更多
NH_(3)-SCR performances were explored to the relationship between structure morphology and physio-chemical properties over low-dimensional ternary Mn-based catalysts prepared by one-step synthesis method.Due to its st...NH_(3)-SCR performances were explored to the relationship between structure morphology and physio-chemical properties over low-dimensional ternary Mn-based catalysts prepared by one-step synthesis method.Due to its strong oxidation performance,Sn-MnO_(x) was prone to side reactions between NO,NH_(3)and O_(2),resulting in the generation of more NO_(2)and N_(2)O,here most of N_(2)O was driven from the non-selective oxidation of NH_(3),while a small part generated from the side reaction between NH_(3)and NO_(2).Co or Ni doping into Sn-MnO_(x) as solid solution components obviously stronged the electronic interaction for actively mobilization and weakened the oxidation performance for signally reducing the selective tendency of side reactions to N_(2)O.The optimal modification resulted in improving the surface area and enhancing the strong interaction between polyvalent cations in Co/Ni-Mn-SnO_(2)to provide more surface adsorbed oxygen,active sites of Mn^(3+) and Mn^(4+),high-content Sn^(4+) and plentiful Lewis-acidity for more active intermediates,which significantly broadened the activity window of Sn-MnOx,improved the N^(2) selectivity by inhibiting N_(2)O formation,and also contributed to an acceptable resistances to water and sulfur.At low reaction temperatures,the SCR reactions over three catalysts mainly obeyed the typical Elye-rideal(E-R)routs via the reactions of adsorbed L-NH_(x)(x=3,2,1)and B-NH_(4)^(+) with the gaseous NO to generate N_(2) but also N_(2)O by-products.Except for the above basic E-R reactions,as increasing the reaction temperature,the main adsorbed NO_(x)-species were bidentate nitrates that were also active in the Langmuir-Hinshelwood reactions with adsorbed L-NH_(x) species over Co/Ni modified Mn-SnO_(2) catalyst.展开更多
Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.Nevertheless,the conventional"trial and error"method for producing advanced electrocatalysts is not only cost-ineffecti...Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.Nevertheless,the conventional"trial and error"method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive.Fortunately,the advancement of machine learning brings new opportunities for electrocatalysts discovery and design.By analyzing experimental and theoretical data,machine learning can effectively predict their hydrogen evolution reaction(HER)performance.This review summarizes recent developments in machine learning for low-dimensional electrocatalysts,including zero-dimension nanoparticles and nanoclusters,one-dimensional nanotubes and nanowires,two-dimensional nanosheets,as well as other electrocatalysts.In particular,the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted.Finally,the future directions and perspectives for machine learning in electrocatalysis are discussed,emphasizing the potential for machine learning to accelerate electrocatalyst discovery,optimize their performance,and provide new insights into electrocatalytic mechanisms.Overall,this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.展开更多
基金funding from FCT(Fundagao para a Ciencia e Tecnologia,I.P.)under the projects LA/P/0037/2020,UIDP/50025/2020 and UIDB/50025/2020 of the Associate Laboratory Institute of Nanostructures,Nanomodelling and Nanofabrication-i3Nby the projects FlexSolar(PTDC/CTM-REF/1008/2020),and SpaceFlex(2022.01610.PTDC,DOI:10.54499/2022.01610.PTDC)+1 种基金supported by the project M-ECO2-Industrial Cluster for advanced biofuel production,Ref.C644930471-00000041,R2U Technologies and Befunding from the European Union via the project X-STREAM(Horizon EU,ERC CoG,No 101124803)the support of a fellowship from the"la Caixa"Foundation(ID 100010434)。
文摘Low-dimensional(LD)halide perovskites have attracted considerable attention due to their distinctive structures and exceptional optoelectronic properties,including high absorption coefficients,extended charge carrier diffusion lengths,suppressed non-radiative recombination rates,and intense photoluminescence.A key advantage of LD perovskites is the tunability of their optical and electronic properties through the precise optimization of their structural arrangements and dimensionality.This review systematically examines recent progress in the synthesis and optoelectronic characterizations of LD perovskites,focusing on their structural,optical,and photophysical properties that underpin their versatility in diverse applications.The review further summarizes advancements in LD perovskite-based devices,including resistive memory,artificial synapses,photodetectors,light-emitting diodes,and solar cells.Finally,the challenges associated with stability,scalability,and integration,as well as future prospects,are discussed,emphasizing the potential of LD perovskites to drive breakthroughs in device efficiency and industrial applicability.
基金supported by Hubei Province Technology Innovation Program Project(2024BCB073)the National Natural Science Foundation of China(52402249)the China Postdoctoral Science Foundation(2021M690930).
文摘Carbon-based low-dimensional materials(CLDM)with elemental carbon as the main component have unique physical and chemical properties,and become the focus of research in many fields including energy,environmental protection,and information technology.Notably,cellulose acetate,the main component of cigarette butts(CBs),is a one-dimensional precursor with a large specific surface area and aspect ratio.Still,their usefulness as building fillers has often been underestimated before.This review summarizes recent advances in CBs recycling and provides suggested guidelines for its use as a CLDM material in renewable energy.Specifically,we first describe the harmful effects of CBs as pollutants in our lives to emphasize the importance of proper recycling.We then summarize previous methods of recycling CBs waste,including clay bricks,asphalt concrete pavement,gypsum,acoustic materials,chemisorption,vector control,and corrosion control.The potential applications of CBs include triboelectric nanogenerator applications,flexible batteries,enhanced metal-organic framework material energy storage devices,and carbon-based hydrogen storage.Finally,the advantages of utilizing CBs-derived CLDM materials over conventional solutions in the energy field are discussed.This review will provide new avenues for solving the intractable problem of CBs and reducing the manufacturing costs of renewable materials.
基金financially supported by the National Natural Science Foundation of China(Nos.62205011,52473305,92256202,and 12261131500)the Fundamental Research Funds for the Central Universities(No.PY2507)Qian Xuesen Youth Innovation Fund of CASC
文摘Linearly polarized photodetectors(PDs),leveraging the inherent structural and material information encoded in light's polarization state,hold transformative potential for applications ranging from remote sensing to biomedical imaging.Traditional systems that rely on external polarizing elements face challenges in miniaturization and efficiency,driving interest in materials with intrinsic anisotropy.Low-dimensional metal halide perovskites,distinguished by their tunable bandgaps,high carrier mobility,and quantum confinement effects,have emerged as a groundbreaking platform for next-generation polarized PDs.This review comprehensively summarizes the theory,materials,and device engineering of linearly polarized PDs based on low-dimensional perovskites.It aims to elucidate polarization mechanisms across dimensions by establishing a rigorous theoretical foundation for linearly polarized PDs of low-dimensional perovskites.Beyond theoretical insights,the review also highlights cutting-edge fabrication techniques for one-dimensional nano wires and two-dimensional heterostructures,along with performance benchmarks of state-of-the-art devices.By integrating experimental advancements with theoretical insights,this work not only advances the fundamental understanding of polarization mechanisms but also outlines actionable pathways for optimizing device performance,stability,and scalability,which may serve as a critical resource for researchers aiming to harness the full potential of low-dimensional perovskites in polarized optoelectronics.
基金financially supportsed by National Natural Science Foundation of China(No.52173150)Guangzhou Science and Technology Program City-University Joint Funding Project(No.2023A03J0001)Postdoctoral Science Foundation(No.2022M723670).
文摘Bacterial infections have always been a major threat to human health.Skin wounds are frequently exposed to the external environment,and they may become contaminated by bacteria derived from the surrounding skin,the local environment,and the patient’s own endogenous sources.Contaminated wounds may enter a state of chronic inflammation that impedes healing.Urgent development of antibacterial wound dressings capable of effectively combating bacteria and overcoming resistance is necessary.Nanotechnology and nanomaterials present promising potential as innovative strategies for antimicrobial wound dressings,owing to their robust antibacterial characteristics and the inherent advantage of avoiding antibiotic resistance.Therefore,this review provides a concise overview of the antimicrobial mechanisms exhibited by low-dimensional nanomaterials.It further categorizes common low-dimensional antimicrobial nanomaterials into zero-dimensional(0D),one-dimensional(1D)and two-dimensional(2D)nanomaterials based on their structural characteristics,and gives a detailed compendium of the latest research advances and applications of different low-dimensional antimicrobial nanomaterials in wound healing,which could be helpful for the development of more effective wound dressings.
基金co-supported by the National Natural Science Foundation of China(Grant Nos.62222404,T2450054,62304084,62504087,62361136587 and 92248304)the National Key Research and Development Plan of China(Grant No.2021YFB3601200)+3 种基金the Major Program of Hubei Province(Grant No.2023BAA009)the Research Grants Council of Hong Kong Postdoctoral Fellowship Scheme(Grant No.PDFS2223-4S06)the China Postdoctoral Science Foundation funded project(Grant No.2025M770530)the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20250136).
文摘The von Neumann bottleneck in conventional computing architectures presents a significant challenge for data-inten-sive artificial intelligence applications.A promising approach involves designing specialized hardware with on-chip parameter tunability,which directly accelerates machine learning functions.This work demonstrates a continuously tunable mixed-kernel function physically realized within a van der Waals heterostructure.We designed and fabricated a MoTe_(2)/MoS_(2)type-Ⅱvertical heterojunction phototransistor,which exhibits a non-monotonic,Gaussian-like optoelectronic response owing to its unique inter-layer charge transfer mechanism.This intrinsic physical behavior directly maps to a mixed-kernel function combining Gaussian and Sigmoid characteristics.Furthermore,the hardware kernel can be continuously modulated by in-situ tuning of external opti-cal stimuli.The mixed-kernel exhibited exceptional performance,achieving precision,accuracy,and area under the curve(AUC)values of 95.8%,96%,and 0.9986,respectively,significantly outperforming conventional kernels.By successfully embedding a complex,adaptable mathematical function into the intrinsic physical properties of a single device,this work pioneers a novel pathway toward next-generation,energy-efficient intelligent systems with hardware-level adaptability.
基金supported by the Gansu Provincial Natural Science Foundation(grant number 25JRRA074)the Gansu Provincial Key R&D Science and Technology Program(grant number 24YFGA060)the National Natural Science Foundation of China(grant number 62161019).
文摘Modern intelligent systems,such as autonomous vehicles and face recognition,must continuously adapt to new scenarios while preserving their ability to handle previously encountered situations.However,when neural networks learn new classes sequentially,they suffer from catastrophic forgetting—the tendency to lose knowledge of earlier classes.This challenge,which lies at the core of class-incremental learning,severely limits the deployment of continual learning systems in real-world applications with streaming data.Existing approaches,including rehearsalbased methods and knowledge distillation techniques,have attempted to address this issue but often struggle to effectively preserve decision boundaries and discriminative features under limited memory constraints.To overcome these limitations,we propose a support vector-guided framework for class-incremental learning.The framework integrates an enhanced feature extractor with a Support Vector Machine classifier,which generates boundary-critical support vectors to guide both replay and distillation.Building on this architecture,we design a joint feature retention strategy that combines boundary proximity with feature diversity,and a Support Vector Distillation Loss that enforces dual alignment in decision and semantic spaces.In addition,triple attention modules are incorporated into the feature extractor to enhance representation power.Extensive experiments on CIFAR-100 and Tiny-ImageNet demonstrate effective improvements.On CIFAR-100 and Tiny-ImageNet with 5 tasks,our method achieves 71.68%and 58.61%average accuracy,outperforming strong baselines by 3.34%and 2.05%.These advantages are consistently observed across different task splits,highlighting the robustness and generalization of the proposed approach.Beyond benchmark evaluations,the framework also shows potential in few-shot and resource-constrained applications such as edge computing and mobile robotics.
基金supported by the National Key R&D Program of China under Grant No.2023YFA1008702the National Natural Science Foundation of China under Grant No.12571300。
文摘The support vector machine,a widely used binary classification method,may expose sensitive information during training.To address this,the authors propose a personalized differential privacy method that extends differential privacy.Specifically,the authors introduce personalized differentially private support vector machines to meet different individuals'privacy requirements,using a reweighting strategy and the Laplace mechanism.Theoretical analysis demonstrates that the proposed methods simultaneously satisfy the requirements of personalized differential privacy and ensure model prediction accuracy at these privacy levels.Extensive experiments demonstrate that the proposed methods outperform the existing methods.
基金supported by grants PID2020-120308RB-I00 and PID2023-147802OB-I00 funded by MICIU/AEI/10.13039/501100011033FEDER,UE,by Aligning Science Across Parkinson’s(ref.ASAP-020505)through the Michael J.Fox Foundation for Parkinson’s Research+1 种基金by CiberNed Intramural Collaborative Projects(ref.PI2020/09)by the Spanish Fundación Mutua Madrile?a de Investigación Médica(to JLL)。
文摘The development of clinical candidates that modify the natural progression of sporadic Parkinson's disease and related synucleinopathies is a praiseworthy endeavor,but extremely challenging.Therapeutic candidates that were successful in preclinical Parkinson's disease animal models have repeatedly failed when tested in clinical trials.While these failures have many possible explanations,it is perhaps time to recognize that the problem lies with the animal models rather than the putative candidate.In other words,the lack of adequate animal models of Parkinson's disease currently represents the main barrier to preclinical identification of potential disease-modifying therapies likely to succeed in clinical trials.However,this barrier may be overcome by the recent introduction of novel generations of viral vectors coding for different forms of alpha-synuclein species and related genes.Although still facing several limitations,these models have managed to mimic the known neuropathological hallmarks of Parkinson's disease with unprecedented accuracy,delineating a more optimistic scenario for the near future.
文摘Fluidic Thrust Vectoring(FTV)is used for the yaw attitude control of tailless flying wing,which can significantly improve stealth performance,maneuverability and lateral/heading maneuverability.The FTV control scheme of co-directional secondary flow was designed based on a 30 kgf thrust turbojet engine,an equivalent rudder deflection control variable of Mass Flow Combination(MFC)was proposed,and a control model was established to form a FTV control system scheme,which was integrated with the flight control system of a 100 kg tailless flying wing with medium aspect ratio to achieve closed-loop control of the yaw attitude based on FTV.The heading stability augmentation and maneuvering control characteristics and time response characteristics of tailless flying wing by FTV were quantitatively studied through virtual flight test in a wind tunnel at a wind speed of 35 m/s.The results show that the control strategy based on MFC achieves bidirectional continuous and stable control of thrust vector angle in a range of±11°,and the thrust vector angle varies monotonically with MFC;the co-directional FTV realizes bidirectional continuous and stable control of the yaw attitude of tailless flying wing,without longitudinal/lateral coupling moment.The increment of the maximum yawing moment coefficient is 0.0029,the maximum yaw rate is 7.55(°)/s,and the response time of the yaw rate of the vectoring nozzle actuated by the secondary flow is about 0.06 s,which satisfies the heading stability augmentation and maneuvering control response requirements of the aircraft with statically unstable heading,and provides new control means for the heading rudderless attitude control of tailless flying wing.
基金supported by the China Agriculture Research System of MOF and MARAthe National Natural Science Foundation of China (31872337 and 31501919)the Agricultural Science and Technology Innovation Project,China (ASTIP-IAS02)。
文摘The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS.
基金supported in part by the National Natural Science Foundation of China under Grant 52477060in part by the Tianjin Natural Science Foundation Project under Grant 24JCZDJC00250in part by the Zhejiang Leading Innovation and Entrepreneurship Team Project under Grant 2024R01012.
文摘In position-sensorless brushless direct current(DC)motors(BLDCMs)fed by a four-switch three-phase(FSTP)inverter,only two phases are fully controlled,while the remaining phase is tied to the midpoint of the split DC-link capacitors.The voltage pulses required by inductance-based initial position detection can cause unequal discharge of the series capacitors,shifting the neutral-point voltage away from half of DC-link voltage(U_(dc)/2).This neutral-point drift breaks the spatial symmetry of the inverter voltage vectors,so the 360°electrical period can no longer be evenly partitioned into six sectors during initial rotor position detection.To address this issue,this paper proposes a detection-pulse injection sequence that explicitly accounts for the asymmetric voltage vectors of the FSTP inverter.With the proposed sequence,the initial rotor position can be identified within a 30°electrical sector.The method requires no additional voltage or current sensors,and experimental results confirm its feasibility.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
基金The Pre-Research Foundation of National Ministries andCommissions (No9140A16050109DZ01)the Scientific Research Program of the Education Department of Shanxi Province (No09JK701)
文摘In order to overcome the shortcomings that the reconstructed spectral reflectance may be negative when using the classic principal component analysis （PCA）to reduce the dimensions of the multi-spectral data, a nonnegative constrained principal component analysis method is proposed to construct a low-dimensional multi-spectral space and accomplish the conversion between the new constructed space and the multispectral space. First, the reason behind the negative data is analyzed and a nonnegative constraint is imposed on the classic PCA. Then a set of nonnegative linear independence weight vectors of principal components is obtained, by which a lowdimensional space is constructed. Finally, a nonlinear optimization technique is used to determine the projection vectors of the high-dimensional multi-spectral data in the constructed space. Experimental results show that the proposed method can keep the reconstructed spectral data in [ 0, 1 ]. The precision of the space created by the proposed method is equivalent to or even higher than that by the PCA.
基金Project supported by the National Natural Science Foundation of China （Grant No 10547006）.
文摘Some kinds of low-dimensional nanostructures can be formed by irradiation of laser on the pure silicon sample and the SiGe alloy sample. This paper has studied the photoluminescence （PL） of the hole-net structure of silicon and the porous structure of SiGe where the PL intensity at 706nm and 725nm wavelength increases obviously. The effect of intensity-enhancing in the PL peaks cannot be explained within the quantum confinement alone. A mechanism for increasing PL emission in the above structures is proposed, in which the trap states of the interface between SiO2 and nanocrystal play an important role.
基金supported by the Doctoral Program of Higher Education(20130142120075)the Fundamental Research Funds for the Central Universities(HUST:2016YXMS032)National Key Research and Development Program of China(Grant No.2016YFB0700702)
文摘Metal halide perovskites are crystalline materials originally developed out of scientific curiosity. They have shown great potential as active materials in optoelectronic applications. In the last 6 years, their certified photovoltaic efficiencies have reached 22.1%. Compared to bulk halide perovskites, low-dimensional ones exhibited novel physical properties. The photoluminescence quantum yields of perovskite quantum dots are close to 100%. The external quantum efficiencies and current efficiencies of perovskite quantum dot light-emitting diodes have reached 8% and 43 cd A^(-1),respectively, and their nanowire lasers show ultralow-threshold room-temperature lasing with emission tunability and ease of synthesis. Perovskite nanowire photodetectors reached a responsivity of 10 A W^(-1)and a specific normalized detectivity of the order of 10^(12 )Jones. Different from most reported reviews focusing on photovoltaic applications, we summarize the rapid progress in the study of low-dimensional perovskite materials, as well as their promising applications in optoelectronic devices. In particular, we review the wide tunability of fabrication methods and the state-of-the-art research outputs of low-dimensional perovskite optoelectronic devices. Finally, the anticipated challenges and potential for this exciting research are proposed.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61574011 and 51761145025)the Key Program of the National Natural Science Foundation of China(Grant No.No.61731019)the Natural Science Foundation of Beijing,China(Grant Nos.4182015 and 4182014)。
文摘In recent years,low-dimensional materials have received extensive attention in the field of electronics and optoelectronics.Among them,photoelectric devices based on photoconductive effect in low-dimensional materials have a broad development space.In contrast to positive photoconductivity,negative photoconductivity(NPC)refers to a phenomenon that the conductivity decreases under illumination.It has novel application prospects in the field of optoelectronics,memory,and gas detection,etc.In this paper,we review reports about the NPC effect in low-dimensional materials and systematically summarize the mechanisms to form the NPC effect in existing low-dimensional materials.
基金supported by the National Natural Science Foundation of China(61725401,61904058,61904058)the National Key R&D Program of China(2016YFA0204000)+1 种基金China Postdoctoral Science Foundation Project(2019M662623)the National Postdoctoral Program for Innovative Talent(BX20190127).
文摘The photovoltaic(PV)market is currently dominated by silicon based solar cells.However technological diversification is essential to promote competition,which is the driving force for technological growth.Historically,the choice of PV materials has been limited to the three-dimensional(3D)compounds with a high crystal symmetry and direct band gap.However,to meet the strict demands for sustainable PV applications,material space has been expanded beyond 3D compounds.In this perspective we discuss the potential of low-dimensional materials(2D,1D)for application in PVs.We present unique features of low-dimensional materials in context of their suitability in the solar cells.The band gap,absorption,carrier dynamics,mobility,defects,surface states and growth kinetics are discussed and compared to 3D counterparts,providing a comprehensive view of prospects of low-dimensional materials.Structural dimensionality leads to a highly anisotropic carrier transport,complex defect chemistry and peculiar growth dynamics.By providing fundamental insights into these challenges we aim to deepen the understanding of low-dimensional materials and expand the scope of their application.Finally,we discuss the current research status and development trend of solar cell devices made of low-dimensional materials.
基金financially supported by National Natural Science Foundation of China (Nos. U20A20130, 21806009)China Postdoctoral Science Foundation (2019T120049)Fundamental Research Funds for the Central Universities (No. 06500152).
文摘NH_(3)-SCR performances were explored to the relationship between structure morphology and physio-chemical properties over low-dimensional ternary Mn-based catalysts prepared by one-step synthesis method.Due to its strong oxidation performance,Sn-MnO_(x) was prone to side reactions between NO,NH_(3)and O_(2),resulting in the generation of more NO_(2)and N_(2)O,here most of N_(2)O was driven from the non-selective oxidation of NH_(3),while a small part generated from the side reaction between NH_(3)and NO_(2).Co or Ni doping into Sn-MnO_(x) as solid solution components obviously stronged the electronic interaction for actively mobilization and weakened the oxidation performance for signally reducing the selective tendency of side reactions to N_(2)O.The optimal modification resulted in improving the surface area and enhancing the strong interaction between polyvalent cations in Co/Ni-Mn-SnO_(2)to provide more surface adsorbed oxygen,active sites of Mn^(3+) and Mn^(4+),high-content Sn^(4+) and plentiful Lewis-acidity for more active intermediates,which significantly broadened the activity window of Sn-MnOx,improved the N^(2) selectivity by inhibiting N_(2)O formation,and also contributed to an acceptable resistances to water and sulfur.At low reaction temperatures,the SCR reactions over three catalysts mainly obeyed the typical Elye-rideal(E-R)routs via the reactions of adsorbed L-NH_(x)(x=3,2,1)and B-NH_(4)^(+) with the gaseous NO to generate N_(2) but also N_(2)O by-products.Except for the above basic E-R reactions,as increasing the reaction temperature,the main adsorbed NO_(x)-species were bidentate nitrates that were also active in the Langmuir-Hinshelwood reactions with adsorbed L-NH_(x) species over Co/Ni modified Mn-SnO_(2) catalyst.
基金This work was supported by the National Natural Science Foundation of China(Grant No.22008098,52122408)the Program for Science&Technology Innovation Talents in Universities of Henan Province(No.22HASTIT008)+3 种基金the Programs for Science and Technology Development of Henan Province,China(No.222102320065)the Key Specialized Research and Development Breakthrough(Science and Technology)in Henan Province(No.212102210214)the Natural Science Foundations of Henan Province(No.222300420502)the Key Scientific Research Projects of University in Henan Province(No.23B430002).
文摘Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.Nevertheless,the conventional"trial and error"method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive.Fortunately,the advancement of machine learning brings new opportunities for electrocatalysts discovery and design.By analyzing experimental and theoretical data,machine learning can effectively predict their hydrogen evolution reaction(HER)performance.This review summarizes recent developments in machine learning for low-dimensional electrocatalysts,including zero-dimension nanoparticles and nanoclusters,one-dimensional nanotubes and nanowires,two-dimensional nanosheets,as well as other electrocatalysts.In particular,the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted.Finally,the future directions and perspectives for machine learning in electrocatalysis are discussed,emphasizing the potential for machine learning to accelerate electrocatalyst discovery,optimize their performance,and provide new insights into electrocatalytic mechanisms.Overall,this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.
