Wide-temperature applications of sodium-ion batteries(SIBs)are severely limited by the sluggish ion insertion/diffusion kinetics of conversion-type anodes.Quantum-sized transition metal dichalcogenides possess unique ...Wide-temperature applications of sodium-ion batteries(SIBs)are severely limited by the sluggish ion insertion/diffusion kinetics of conversion-type anodes.Quantum-sized transition metal dichalcogenides possess unique advantages of charge delocalization and enrich uncoordinated electrons and short-range transfer kinetics,which are crucial to achieve rapid low-temperature charge transfer and high-temperature interface stability.Herein,a quantum-scale FeS_(2) loaded on three-dimensional Ti_(3)C_(2) MXene skeletons(FeS_(2) QD/MXene)fabricated as SIBs anode,demonstrating impressive performance under wide-temperature conditions(−35 to 65).The theoretical calculations combined with experimental characterization interprets that the unsaturated coordination edges of FeS_(2) QD can induce delocalized electronic regions,which reduces electrostatic potential and significantly facilitates efficient Na+diffusion across a broad temperature range.Moreover,the Ti_(3)C_(2) skeleton reinforces structural integrity via Fe-O-Ti bonding,while enabling excellent dispersion of FeS_(2) QD.As expected,FeS_(2) QD/MXene anode harvests capacities of 255.2 and 424.9 mAh g^(−1) at 0.1 A g^(−1) under−35 and 65,and the energy density of FeS_(2) QD/MXene//NVP full cell can reach to 162.4 Wh kg^(−1) at−35,highlighting its practical potential for wide-temperatures conditions.This work extends the uncoordinated regions induced by quantum-size effects for exceptional Na^(+)ion storage and diffusion performance at wide-temperatures environment.展开更多
Strategically coupling nanoparticle hybrids and internal thermosensitive molecular switches establishes an innovative paradigm for constructing micro/nanoscale-reconfigurable robots,facilitating energyefficient CO_(2)...Strategically coupling nanoparticle hybrids and internal thermosensitive molecular switches establishes an innovative paradigm for constructing micro/nanoscale-reconfigurable robots,facilitating energyefficient CO_(2) management in life-support systems of confined space.Here,a micro/nano-reconfigurable robot is constructed from the CO_(2) molecular hunters,temperature-sensitive molecular switch,solar photothermal conversion,and magnetically-driven function engines.The molecular hunters within the molecular extension state can capture 6.19 mmol g^(−1) of CO_(2) to form carbamic acid and ammonium bicarbonate.Interestingly,the molecular switch of the robot activates a molecular curling state that facilitates CO_(2) release through nano-reconfiguration,which is mediated by the temperature-sensitive curling of Pluronic F127 molecular chains during the photothermal desorption.Nano-reconfiguration of robot alters the amino microenvironment,including increasing surface electrostatic potential of the amino group and decreasing overall lowest unoccupied molecular orbital energy level.This weakened the nucleophilic attack ability of the amino group toward the adsorption product derivatives,thereby inhibiting the side reactions that generate hard-to-decompose urea structures,achieving the lowest regeneration temperature of 55℃ reported to date.The engine of the robot possesses non-contact magnetically-driven micro-reconfiguration capability to achieve efficient photothermal regeneration while avoiding local overheating.Notably,the robot successfully prolonged the survival time of mice in the sealed container by up to 54.61%,effectively addressing the issue of carbon suffocation in confined spaces.This work significantly enhances life-support systems for deep-space exploration,while stimulating innovations in sustainable carbon management technologies for terrestrial extreme environments.展开更多
车辆检测是智能交通系统和自动驾驶的重要组成部分。然而,实际交通场景中存在许多不确定因素,导致车辆检测模型的准确率低实时性差。为了解决这个问题,提出了一种快速准确的车辆检测算法——YOLOv8-DEL。使用DGCST(dynamic group convol...车辆检测是智能交通系统和自动驾驶的重要组成部分。然而,实际交通场景中存在许多不确定因素,导致车辆检测模型的准确率低实时性差。为了解决这个问题,提出了一种快速准确的车辆检测算法——YOLOv8-DEL。使用DGCST(dynamic group convolution shuffle transformer)模块代替C2f模块来重构主干网络,以增强特征提取能力并使网络更轻量;添加的P2检测层能使模型更敏锐地定位和检测小目标,同时采用Efficient RepGFPN进行多尺度特征融合,以丰富特征信息并提高模型的特征表达能力;通过结合GroupNorm和共享卷积的优点,设计了一种轻量型共享卷积检测头,在保持精度的前提下,有效减少参数量并提升检测速度。与YOLOv8相比,提出的YOLOv8-DEL在BDD100K数据集和KITTI数据集上,mAP@0.5分别提高了4.8个百分点和1.2个百分点,具有实时检测速度(208.6 FPS和216.4 FPS),在检测精度和速度方面实现了更有利的折中。展开更多
Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
现有基于红外图像的绝缘子发热缺陷识别方法,存在目标区域提取精度有限、温度提取受环境因素影响较大等问题。为此,本文提出一种复合绝缘子过热识别新方法:首先改进单阶段绝缘子实例分割算法You Only Look At CoefficienTs(YOLACT),引...现有基于红外图像的绝缘子发热缺陷识别方法,存在目标区域提取精度有限、温度提取受环境因素影响较大等问题。为此,本文提出一种复合绝缘子过热识别新方法:首先改进单阶段绝缘子实例分割算法You Only Look At CoefficienTs(YOLACT),引入嵌有Efficient Local Attention(ELA)机制的MobileNetV2作主干网络提升检测速度,融合特征金字塔网络(Feature Pyramid Network,FPN)各层特征图并加入聚焦纯卷积特征提取模块提高特征图质量;然后使用改进算法识别红外图像中复合绝缘子外轮廓,定位其棒芯位置;最后依据红外图像热矩阵获取棒芯温度矩阵,对比温度变化判断是否异常。实际生产环境中,本文方法整体准确率达到975,算法总耗时125ms;改进实例分割算法平均交互比(mIOU)为9297,平均像素准确率(mPA)为9615,每秒帧数(FPS)为19。结果显示,此方法分割定位效果好,能滤除多数环境因素导致的温度识别误差,为绝缘子温度异常识别提供新方案。展开更多
Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequent...Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.展开更多
Graphdiyne(GDY)and its derivatives have been considered ideal supporting materials for nanoscale active particles because of their unique atomic and electronic structure.An efficient bi-metal Cu-Pd catalyst was added ...Graphdiyne(GDY)and its derivatives have been considered ideal supporting materials for nanoscale active particles because of their unique atomic and electronic structure.An efficient bi-metal Cu-Pd catalyst was added to produce the uniform deposition of Pd nano-clusters with an average size of~0.95 nm on hydrogen-substituted GDY(HGDY)nanosheets.With the assistance of NaBH4,the resulting Pd/H-GDY was very effective in the degradation of 4-nitrophenol(4-NP),whose conversion was sharply increased to 97.21%in 100 s with a rate constant per unit mass(k`)of 8.97×10^(5)min−1 g^(−1).Additionally,dyes such as methyl orange(MO)and Congo red(CR)were completely degraded within 180 and 90 s,respectively.The Pd/H-GDY maintained this activity after 5 reduction cycles.These results highlight the promising performance of Pd/H-GDY in catalyzing the degradation of various pollutants,which is attributed to the combined effect of the largeπ-conjugated structure of the H-GDY nanosheets and the evenly distributed Pd nanoclusters.展开更多
针对复杂交通场景目标检测方法的不足,特别是对小目标和遮挡目标的漏检及多尺度目标检测和模型鲁棒性方面的不足,提出了一种改进后的YOLOv8s-SRCEM(You Only Look Once version 8 small model with Small object detection head,Residua...针对复杂交通场景目标检测方法的不足,特别是对小目标和遮挡目标的漏检及多尺度目标检测和模型鲁棒性方面的不足,提出了一种改进后的YOLOv8s-SRCEM(You Only Look Once version 8 small model with Small object detection head,Residual Convolutional block attention module,Efficient channel attention module,and Multi-scale block)模型:引入小目标检测头,使模型能够更加敏感地捕捉小尺寸目标,提高对小目标的检测能力;在小目标检测头上集成Res-CBAM(Residual Convolutional Block Attention Module),进一步提高特征学习的显著性;在骨干网络中加入ECA(Efficient Channel Attention)模块,强化模型对特征通道重要性的关注,提升特征选择和模型的鲁棒性;将原始的SPPF(Spatial Pyramid Pooling-Fast)模块替换为MS-Block(Multi-Scale Block),模型在不同尺度上的特征捕捉和融合能力得到增强。在KITTI数据集上,改进后的模型相比于YOLOv8s模型mAP(mean Average Precision)值提高了6.6%。实验结果表明,多种改进方案的组合使模型在复杂交通场景中的检测性能得到全面提升。展开更多
During the crystallization of perovskite films,defects at Pb and I sites are generated,causing lattice mismatch and adversely affecting the performance of perovskite solar cells(PSCs).Herein,we introduced aπ-conjugat...During the crystallization of perovskite films,defects at Pb and I sites are generated,causing lattice mismatch and adversely affecting the performance of perovskite solar cells(PSCs).Herein,we introduced aπ-conjugated molecule with a dual-ring structure,namely CCA(3,4-ethylenedioxythiophene-2,5-dicarboxylic acid),as an additive to regulate the crystallization of perovskite films and passivate defects.As a bidentate Lewis base,CCA coordinates the bidentate carboxyl groups with free Pb^(2+)through the delocalized electrons of its conjugated ring.This coordination regulates lattice stress,repairs the 3D[PbI6]octahedra,stabilizes the perovskite framework,and guides the vertical orientation growth of grains.Simultaneously,the addition of CCA shifts the perovskite’s band structure towards p-type,achieving better energy-level alignment with the doped hole-transport layer(HTL)and suppressing non-radiative recombination.Consequently,the prepared CCA-doped single-junction devices exhibit an outstanding power conversion efficiency(PCE)of 24.90%,with a high open-circuit voltage(VOC)of 1.195,a high fill factor(FF)of 84.60%,and low FF loss.The optimized films and devices show enhanced long-term stability,retaining 80.1%of the initial efficiency after continuous illumination for 560 h.Additionally,a PCE of 21.6%was achieved in devices with a bandgap of 1.68 eV,which were further extended to perovskite/silicon tandem devices,achieving high-performance devices with PCEs of 30.96%(aperture area:1.05 cm^(2))and 25.96%(aperture area:20.06 cm^(2)).展开更多
Superhydrophobic glass has inspiring development prospects in endoscopes,solar panels and other engineering and medical fields.However,the surface topography required to achieve superhydrophobicity will inevitably aff...Superhydrophobic glass has inspiring development prospects in endoscopes,solar panels and other engineering and medical fields.However,the surface topography required to achieve superhydrophobicity will inevitably affect the surface transparency and limit the application of glass materials.To resolve the contradiction between the surface transparency and the robust superhydrophobicity,an efficient and low-cost laser-chemical surface functionalization process was utilized to fabricate superhydrophobic glass surface.The results show that the air can be effectively trapped in surface micro/nanostructure induced by laser texturing,thus reducing the solid-liquid contact area and interfacial tension.The deposition of hydrophobic carbon-containing groups on the surface can be accelerated by chemical treatment,and the surface energy is significantly reduced.The glass surface exhibits marvelous robust superhydrophobicity with a contact angle of 155.8°and a roll-off angle of 7.2°under the combination of hierarchical micro/nanostructure and low surface energy.Moreover,the surface transparency of the prepared superhydrophobic glass was only 5.42%lower than that of the untreated surface.This superhydrophobic glass with high transparency still maintains excellent superhydrophobicity after durability and stability tests.The facile fabrication of superhydrophobic glass with high transparency and robustness provides a strong reference for further expanding the application value of glass materials.展开更多
Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the pun...Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.展开更多
Covalent organic frameworks(COFs),as a burgeoning class of crystalline porous materials have attracted widespread interest due to their designable structures and customized functions.However,the solvothermal synthesis...Covalent organic frameworks(COFs),as a burgeoning class of crystalline porous materials have attracted widespread interest due to their designable structures and customized functions.However,the solvothermal synthesis of COFs is often time-consuming and conducted at a high temperature within a sealed vessel,and also requires a large amount of poisonous solvents,which is generally not available for scaling-up production and commercial application.In recent years,great efforts have been made to explore simple,green,and efficient approaches for COFs synthesis.In this comprehensive review,we summarized the advances in emergent strategies by highlighting their distinct features.Fundamental issues and future directions are also discussed with the object of bringing implications for large-scale and sustainable fabrication of COFs.展开更多
基金supported by the National Nature Science Foundation of China(Nos.52202335 and 52171227)Natural Science Foundation of Jiangsu Province(No.BK20221137)National Key R&D Program of China(2024YFE0108500).
文摘Wide-temperature applications of sodium-ion batteries(SIBs)are severely limited by the sluggish ion insertion/diffusion kinetics of conversion-type anodes.Quantum-sized transition metal dichalcogenides possess unique advantages of charge delocalization and enrich uncoordinated electrons and short-range transfer kinetics,which are crucial to achieve rapid low-temperature charge transfer and high-temperature interface stability.Herein,a quantum-scale FeS_(2) loaded on three-dimensional Ti_(3)C_(2) MXene skeletons(FeS_(2) QD/MXene)fabricated as SIBs anode,demonstrating impressive performance under wide-temperature conditions(−35 to 65).The theoretical calculations combined with experimental characterization interprets that the unsaturated coordination edges of FeS_(2) QD can induce delocalized electronic regions,which reduces electrostatic potential and significantly facilitates efficient Na+diffusion across a broad temperature range.Moreover,the Ti_(3)C_(2) skeleton reinforces structural integrity via Fe-O-Ti bonding,while enabling excellent dispersion of FeS_(2) QD.As expected,FeS_(2) QD/MXene anode harvests capacities of 255.2 and 424.9 mAh g^(−1) at 0.1 A g^(−1) under−35 and 65,and the energy density of FeS_(2) QD/MXene//NVP full cell can reach to 162.4 Wh kg^(−1) at−35,highlighting its practical potential for wide-temperatures conditions.This work extends the uncoordinated regions induced by quantum-size effects for exceptional Na^(+)ion storage and diffusion performance at wide-temperatures environment.
基金supported by the National Natural Science Foundation of China(22168008,22378085)the Guangxi Natural Science Foundation(2024GXNSFDA010053)+1 种基金the Technology Development Project of Guangxi Bossco Environmental Protection Technology Co.,Ltd(202100039)Innovation Project of Guangxi Graduate Education(YCBZ2024065).
文摘Strategically coupling nanoparticle hybrids and internal thermosensitive molecular switches establishes an innovative paradigm for constructing micro/nanoscale-reconfigurable robots,facilitating energyefficient CO_(2) management in life-support systems of confined space.Here,a micro/nano-reconfigurable robot is constructed from the CO_(2) molecular hunters,temperature-sensitive molecular switch,solar photothermal conversion,and magnetically-driven function engines.The molecular hunters within the molecular extension state can capture 6.19 mmol g^(−1) of CO_(2) to form carbamic acid and ammonium bicarbonate.Interestingly,the molecular switch of the robot activates a molecular curling state that facilitates CO_(2) release through nano-reconfiguration,which is mediated by the temperature-sensitive curling of Pluronic F127 molecular chains during the photothermal desorption.Nano-reconfiguration of robot alters the amino microenvironment,including increasing surface electrostatic potential of the amino group and decreasing overall lowest unoccupied molecular orbital energy level.This weakened the nucleophilic attack ability of the amino group toward the adsorption product derivatives,thereby inhibiting the side reactions that generate hard-to-decompose urea structures,achieving the lowest regeneration temperature of 55℃ reported to date.The engine of the robot possesses non-contact magnetically-driven micro-reconfiguration capability to achieve efficient photothermal regeneration while avoiding local overheating.Notably,the robot successfully prolonged the survival time of mice in the sealed container by up to 54.61%,effectively addressing the issue of carbon suffocation in confined spaces.This work significantly enhances life-support systems for deep-space exploration,while stimulating innovations in sustainable carbon management technologies for terrestrial extreme environments.
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
文摘现有基于红外图像的绝缘子发热缺陷识别方法,存在目标区域提取精度有限、温度提取受环境因素影响较大等问题。为此,本文提出一种复合绝缘子过热识别新方法:首先改进单阶段绝缘子实例分割算法You Only Look At CoefficienTs(YOLACT),引入嵌有Efficient Local Attention(ELA)机制的MobileNetV2作主干网络提升检测速度,融合特征金字塔网络(Feature Pyramid Network,FPN)各层特征图并加入聚焦纯卷积特征提取模块提高特征图质量;然后使用改进算法识别红外图像中复合绝缘子外轮廓,定位其棒芯位置;最后依据红外图像热矩阵获取棒芯温度矩阵,对比温度变化判断是否异常。实际生产环境中,本文方法整体准确率达到975,算法总耗时125ms;改进实例分割算法平均交互比(mIOU)为9297,平均像素准确率(mPA)为9615,每秒帧数(FPS)为19。结果显示,此方法分割定位效果好,能滤除多数环境因素导致的温度识别误差,为绝缘子温度异常识别提供新方案。
基金supported by the National Natural Science Foundation of China(No.62203256)。
文摘Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A^(*)Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time.
基金National Natural Science Foundation of China(52072336,51902285)Zhejiang Provincial Natural Science Foundation(LR23E020002)+1 种基金Fundamental Research Funds for the Central Universities(226-2023-00064,226-2024-00146)Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering(2022SZ-FR001)。
文摘Graphdiyne(GDY)and its derivatives have been considered ideal supporting materials for nanoscale active particles because of their unique atomic and electronic structure.An efficient bi-metal Cu-Pd catalyst was added to produce the uniform deposition of Pd nano-clusters with an average size of~0.95 nm on hydrogen-substituted GDY(HGDY)nanosheets.With the assistance of NaBH4,the resulting Pd/H-GDY was very effective in the degradation of 4-nitrophenol(4-NP),whose conversion was sharply increased to 97.21%in 100 s with a rate constant per unit mass(k`)of 8.97×10^(5)min−1 g^(−1).Additionally,dyes such as methyl orange(MO)and Congo red(CR)were completely degraded within 180 and 90 s,respectively.The Pd/H-GDY maintained this activity after 5 reduction cycles.These results highlight the promising performance of Pd/H-GDY in catalyzing the degradation of various pollutants,which is attributed to the combined effect of the largeπ-conjugated structure of the H-GDY nanosheets and the evenly distributed Pd nanoclusters.
文摘针对复杂交通场景目标检测方法的不足,特别是对小目标和遮挡目标的漏检及多尺度目标检测和模型鲁棒性方面的不足,提出了一种改进后的YOLOv8s-SRCEM(You Only Look Once version 8 small model with Small object detection head,Residual Convolutional block attention module,Efficient channel attention module,and Multi-scale block)模型:引入小目标检测头,使模型能够更加敏感地捕捉小尺寸目标,提高对小目标的检测能力;在小目标检测头上集成Res-CBAM(Residual Convolutional Block Attention Module),进一步提高特征学习的显著性;在骨干网络中加入ECA(Efficient Channel Attention)模块,强化模型对特征通道重要性的关注,提升特征选择和模型的鲁棒性;将原始的SPPF(Spatial Pyramid Pooling-Fast)模块替换为MS-Block(Multi-Scale Block),模型在不同尺度上的特征捕捉和融合能力得到增强。在KITTI数据集上,改进后的模型相比于YOLOv8s模型mAP(mean Average Precision)值提高了6.6%。实验结果表明,多种改进方案的组合使模型在复杂交通场景中的检测性能得到全面提升。
基金the generous financial support provided by Sichuan Science and Technology Program(2024ZDZX0030)Chengdu Science and Technology Program(2024-JB00-00010-GX)。
文摘During the crystallization of perovskite films,defects at Pb and I sites are generated,causing lattice mismatch and adversely affecting the performance of perovskite solar cells(PSCs).Herein,we introduced aπ-conjugated molecule with a dual-ring structure,namely CCA(3,4-ethylenedioxythiophene-2,5-dicarboxylic acid),as an additive to regulate the crystallization of perovskite films and passivate defects.As a bidentate Lewis base,CCA coordinates the bidentate carboxyl groups with free Pb^(2+)through the delocalized electrons of its conjugated ring.This coordination regulates lattice stress,repairs the 3D[PbI6]octahedra,stabilizes the perovskite framework,and guides the vertical orientation growth of grains.Simultaneously,the addition of CCA shifts the perovskite’s band structure towards p-type,achieving better energy-level alignment with the doped hole-transport layer(HTL)and suppressing non-radiative recombination.Consequently,the prepared CCA-doped single-junction devices exhibit an outstanding power conversion efficiency(PCE)of 24.90%,with a high open-circuit voltage(VOC)of 1.195,a high fill factor(FF)of 84.60%,and low FF loss.The optimized films and devices show enhanced long-term stability,retaining 80.1%of the initial efficiency after continuous illumination for 560 h.Additionally,a PCE of 21.6%was achieved in devices with a bandgap of 1.68 eV,which were further extended to perovskite/silicon tandem devices,achieving high-performance devices with PCEs of 30.96%(aperture area:1.05 cm^(2))and 25.96%(aperture area:20.06 cm^(2)).
基金Projects(52105175,52305149)supported by the National Natural Science Foundation of ChinaProject(2242024RCB0035)supported by the Zhishan Young Scholar Program of Southeast University,China+5 种基金Project(BK20210235)supported by the Natural Science Foundation of Jiangsu Province,ChinaProject(2023MK042)supported by the State Administration for Market Regulation,ChinaProject(KJ2023003)supported by the Jiangsu Administration for Market Regulation,ChinaProjects(KJ(Y)202429,KJ(YJ)2023001)supported by the Jiangsu Province Special Equipment Safety Supervision Inspection Institute,ChinaProject(JSSCBS20210121)supported by the Jiangsu Provincial Innovative and Entrepreneurial Doctor Program,ChinaProject(1102002310)supported by the Technology Innovation Project for Returnees in Nanjing,China。
文摘Superhydrophobic glass has inspiring development prospects in endoscopes,solar panels and other engineering and medical fields.However,the surface topography required to achieve superhydrophobicity will inevitably affect the surface transparency and limit the application of glass materials.To resolve the contradiction between the surface transparency and the robust superhydrophobicity,an efficient and low-cost laser-chemical surface functionalization process was utilized to fabricate superhydrophobic glass surface.The results show that the air can be effectively trapped in surface micro/nanostructure induced by laser texturing,thus reducing the solid-liquid contact area and interfacial tension.The deposition of hydrophobic carbon-containing groups on the surface can be accelerated by chemical treatment,and the surface energy is significantly reduced.The glass surface exhibits marvelous robust superhydrophobicity with a contact angle of 155.8°and a roll-off angle of 7.2°under the combination of hierarchical micro/nanostructure and low surface energy.Moreover,the surface transparency of the prepared superhydrophobic glass was only 5.42%lower than that of the untreated surface.This superhydrophobic glass with high transparency still maintains excellent superhydrophobicity after durability and stability tests.The facile fabrication of superhydrophobic glass with high transparency and robustness provides a strong reference for further expanding the application value of glass materials.
文摘Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.
基金financially supported by the National Natural Science Foundation of China(Nos.22322801,22108010,22278124)Fundamental Research Funds for the Central Universities(No.buctrc202135)。
文摘Covalent organic frameworks(COFs),as a burgeoning class of crystalline porous materials have attracted widespread interest due to their designable structures and customized functions.However,the solvothermal synthesis of COFs is often time-consuming and conducted at a high temperature within a sealed vessel,and also requires a large amount of poisonous solvents,which is generally not available for scaling-up production and commercial application.In recent years,great efforts have been made to explore simple,green,and efficient approaches for COFs synthesis.In this comprehensive review,we summarized the advances in emergent strategies by highlighting their distinct features.Fundamental issues and future directions are also discussed with the object of bringing implications for large-scale and sustainable fabrication of COFs.
