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
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
Optimizing chemical reaction parameters is an expensive optimization problem. Each experiment takes a long time and the raw materials are expensive. High-throughput methods combined with the parallel Efficient Global ...Optimizing chemical reaction parameters is an expensive optimization problem. Each experiment takes a long time and the raw materials are expensive. High-throughput methods combined with the parallel Efficient Global Optimization algorithm can effectively improve the efficiency of the search for optimal chemical reaction parameters. In this paper, we propose a multi-objective populated expectation improvement criterion for providing multiple near-optimal solutions in high-throughput chemical reaction optimization. An l-NSGA2, employing the Pseudo-power transformation method, is utilized to maximize the expected improvement acquisition function, resulting in a Pareto solution set comprising multiple designs. The approximation of the cost function can be calculated by the ensemble Gaussian process model, which greatly reduces the cost of the exact Gaussian process model. The proposed optimization method was tested on a SNAr benchmark problem. The results show that compared with the previous high-throughput experimental methods, our method can reduce the number of experiments by almost half. At the same time, it theoretically enhances temporal and spatial yields while minimizing by-product formation, potentially guiding real chemical reaction optimization.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
Pyrrolnitrin(PRN),a natural halogenated phenylpyrrole derivative,exhibits a broad spectrum of antimicrobial activity against a wide range of bacteria and fungi.In this study,we isolated a strain of Pseudomonas protege...Pyrrolnitrin(PRN),a natural halogenated phenylpyrrole derivative,exhibits a broad spectrum of antimicrobial activity against a wide range of bacteria and fungi.In this study,we isolated a strain of Pseudomonas protegens JP2-4390 from the rhizosphere soil of rice plants,which showed strong inhibitory activity against Rhizoctonia solani.展开更多
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
Shock wave caused by a sudden release of high-energy,such as explosion and blast,usually affects a significant range of areas.The utilization of a uniform fine mesh to capture sharp shock wave and to obtain precise re...Shock wave caused by a sudden release of high-energy,such as explosion and blast,usually affects a significant range of areas.The utilization of a uniform fine mesh to capture sharp shock wave and to obtain precise results is inefficient in terms of computational resource.This is particularly evident when large-scale fluid field simulations are conducted with significant differences in computational domain size.In this work,a variable-domain-size adaptive mesh enlargement(vAME)method is developed based on the proposed adaptive mesh enlargement(AME)method for modeling multi-explosives explosion problems.The vAME method reduces the division of numerous empty areas or unnecessary computational domains by adaptively suspending enlargement operation in one or two directions,rather than in all directions as in AME method.A series of numerical tests via AME and vAME with varying nonintegral enlargement ratios and different mesh numbers are simulated to verify the efficiency and order of accuracy.An estimate of speedup ratio is analyzed for further efficiency comparison.Several large-scale near-ground explosion experiments with single/multiple explosives are performed to analyze the shock wave superposition formed by the incident wave,reflected wave,and Mach wave.Additionally,the vAME method is employed to validate the accuracy,as well as to investigate the performance of the fluid field and shock wave propagation,considering explosive quantities ranging from 1 to 5 while maintaining a constant total mass.The results show a satisfactory correlation between the overpressure versus time curves for experiments and numerical simulations.The vAME method yields a competitive efficiency,increasing the computational speed to 3.0 and approximately 120,000 times in comparison to AME and the fully fine mesh method,respectively.It indicates that the vAME method reduces the computational cost with minimal impact on the results for such large-scale high-energy release problems with significant differences in computational domain size.展开更多
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.
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.
基金the Nature Foundation(Basic Research)Special Project of Shenyang(22-315-6-20)Liaoning Province Artificial Intelligence Innovation and Development Program Project(2023JH26/10300014)Basic Research Program of Shenyang Institute of Automation,Chinese Academy of Sciences(2023JC2K03).
文摘Optimizing chemical reaction parameters is an expensive optimization problem. Each experiment takes a long time and the raw materials are expensive. High-throughput methods combined with the parallel Efficient Global Optimization algorithm can effectively improve the efficiency of the search for optimal chemical reaction parameters. In this paper, we propose a multi-objective populated expectation improvement criterion for providing multiple near-optimal solutions in high-throughput chemical reaction optimization. An l-NSGA2, employing the Pseudo-power transformation method, is utilized to maximize the expected improvement acquisition function, resulting in a Pareto solution set comprising multiple designs. The approximation of the cost function can be calculated by the ensemble Gaussian process model, which greatly reduces the cost of the exact Gaussian process model. The proposed optimization method was tested on a SNAr benchmark problem. The results show that compared with the previous high-throughput experimental methods, our method can reduce the number of experiments by almost half. At the same time, it theoretically enhances temporal and spatial yields while minimizing by-product formation, potentially guiding real chemical reaction optimization.
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.
基金supported by the Key Technology R&D Program of Zhejiang Province,China(Grant No.2021C02006).
文摘Pyrrolnitrin(PRN),a natural halogenated phenylpyrrole derivative,exhibits a broad spectrum of antimicrobial activity against a wide range of bacteria and fungi.In this study,we isolated a strain of Pseudomonas protegens JP2-4390 from the rhizosphere soil of rice plants,which showed strong inhibitory activity against Rhizoctonia solani.
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(Grant Nos.12302435 and 12221002)。
文摘Shock wave caused by a sudden release of high-energy,such as explosion and blast,usually affects a significant range of areas.The utilization of a uniform fine mesh to capture sharp shock wave and to obtain precise results is inefficient in terms of computational resource.This is particularly evident when large-scale fluid field simulations are conducted with significant differences in computational domain size.In this work,a variable-domain-size adaptive mesh enlargement(vAME)method is developed based on the proposed adaptive mesh enlargement(AME)method for modeling multi-explosives explosion problems.The vAME method reduces the division of numerous empty areas or unnecessary computational domains by adaptively suspending enlargement operation in one or two directions,rather than in all directions as in AME method.A series of numerical tests via AME and vAME with varying nonintegral enlargement ratios and different mesh numbers are simulated to verify the efficiency and order of accuracy.An estimate of speedup ratio is analyzed for further efficiency comparison.Several large-scale near-ground explosion experiments with single/multiple explosives are performed to analyze the shock wave superposition formed by the incident wave,reflected wave,and Mach wave.Additionally,the vAME method is employed to validate the accuracy,as well as to investigate the performance of the fluid field and shock wave propagation,considering explosive quantities ranging from 1 to 5 while maintaining a constant total mass.The results show a satisfactory correlation between the overpressure versus time curves for experiments and numerical simulations.The vAME method yields a competitive efficiency,increasing the computational speed to 3.0 and approximately 120,000 times in comparison to AME and the fully fine mesh method,respectively.It indicates that the vAME method reduces the computational cost with minimal impact on the results for such large-scale high-energy release problems with significant differences in computational domain size.
