期刊：
Journal of Colloid and Interface Science,2025年679(Pt B):569-577 ISSN：0021-9797
通讯作者：
Ren, Xiaohui;Ni, Hongwei
作者机构：
[Cao, Wenzhe; Zou, Haoran; Jiang, Xingxin; Zhang, Hua; Zhang, Tian] The State Key Laboratory of Refractories and Metallurgy, Key Laboratory for Ferrous Metallurgy and Resources Utilization of Ministry of Education & Hubei Provincial Key Laboratory for New Processes of Ironmaking and Steel Making, Faculty of Materials, Wuhan University of Science and Technology, Wuhan 430081, China;[Ren, Xiaohui] The State Key Laboratory of Refractories and Metallurgy, Key Laboratory for Ferrous Metallurgy and Resources Utilization of Ministry of Education & Hubei Provincial Key Laboratory for New Processes of Ironmaking and Steel Making, Faculty of Materials, Wuhan University of Science and Technology, Wuhan 430081, China. Electronic address: xhren@wust.edu.cn;[Ma, Feng; Chen, Rongsheng] School of Chemical Engineering and Technology, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China;[Qiao, Hui] Hunan Key Laboratory for Micro-Nano Energy Materials and Devices, and School of Physics and Optoelectronic, Xiangtan University, Hunan 411105, China;[Zhang, Ye] Lab of Optoelectronic Technology for Low Dimensional Nanomaterials, School of Chemistry and Chemical Engineering, University of South China, Hengyang 421001, China
通讯机构：
[Ren, Xiaohui; Ni, Hongwei] T;The State Key Laboratory of Refractories and Metallurgy, Key Laboratory for Ferrous Metallurgy and Resources Utilization of Ministry of Education & Hubei Provincial Key Laboratory for New Processes of Ironmaking and Steel Making, Faculty of Materials, Wuhan University of Science and Technology, Wuhan 430081, China. Electronic address:
摘要：
The exploration of multiphases and 0D/2D heterojunction in transition metal phosphides (TMPs) and transition metal sulfides (TMDs) is of major interest for hydrogen evolution reaction (HER). Herein, a novel combination route where 0D mixed-phased 1T/2H molybdenum sulfide quantum dots (MoS 2 QDs) are uniformly anchored on the 2D CoP x nanosheets is developed. MoS 2 QDs and CoP x were prepared via hydrothermal method and mixed with different ratios (Mo:Co ratios of 2:1, 1:1, and 1:2) and annealed under different temperatures to modulate their application in acidic HER processes. Specifically, 2Mo/1Co exhibited advanced performance for HER in 0.5 M H 2 SO 4 solution and required 14 mV to deliver 10 mA cm −2 and revealed a descended Tafel slope of 75.42 mV dec −1 with 240 h stability except obvious deactivation. The successful design and construction of 0D/2D mixed-dimensional materials would broaden the application of MoS 2 and CoP x for electrocatalytic hydrogen evolution.
The exploration of multiphases and 0D/2D heterojunction in transition metal phosphides (TMPs) and transition metal sulfides (TMDs) is of major interest for hydrogen evolution reaction (HER). Herein, a novel combination route where 0D mixed-phased 1T/2H molybdenum sulfide quantum dots (MoS 2 QDs) are uniformly anchored on the 2D CoP x nanosheets is developed. MoS 2 QDs and CoP x were prepared via hydrothermal method and mixed with different ratios (Mo:Co ratios of 2:1, 1:1, and 1:2) and annealed under different temperatures to modulate their application in acidic HER processes. Specifically, 2Mo/1Co exhibited advanced performance for HER in 0.5 M H 2 SO 4 solution and required 14 mV to deliver 10 mA cm −2 and revealed a descended Tafel slope of 75.42 mV dec −1 with 240 h stability except obvious deactivation. The successful design and construction of 0D/2D mixed-dimensional materials would broaden the application of MoS 2 and CoP x for electrocatalytic hydrogen evolution.
关键词：
Image deblurring;self-attention mechanism;lightweight model;hinge loss function
摘要：
The acquisition of clear images is a critical aspect in various fields including computer vision, aerial detection, and medical imaging. The issue of image blur caused by object motion poses a challenge in obtaining clear images. To address this, an improved AT-DGAN network model is proposed in this paper. This model integrates the pyramid generator module of the DeblurGAN-v2 network with a self-attention mechanism. The feature pyramid is employed for image feature extraction and representation, while the self-attention mechanism dynamically adjusts the weight of important features in each pyramid layer and performs weighted fusion, thereby compensating for the information loss during feature extraction in the feature pyramid network. Additionally, a hinge loss function is designed for the proposed model to balance the discriminator and the generator, enhancing the stability and training efficiency of the generative adversarial network. The experimental results show that compared to other algorithms of the same type, this improved algorithm has increased the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) of restored images by 0.58dB and 1.5%, respectively.
摘要：
Elemental diffusion and reactions at interfaces significantly increase resistivity and reduce stability, particularly in thermoelectric (TE) systems containing highly diffusive elements like Te. Using thermodynamically stable phases as contact layers have eliminated interfacial reactions but have not been able to completely stop elemental diffusion. This work introduces an innovative approach to effectively increase the activation energy of cross-interface atom diffusion by creating a continuously interfacial symmetric strain field via a high density of interfacial edge dislocations. Specifically, a dense interfacial strain barrier layer is constructed using Ni 0.5 Te as contact layer, resulting in an atomically continuous Te 0.985 Sb 0.015 /Ni 0.5 Te interface. This design achieves a notable reduction in contact resistivity to 9 μΩ cm 2 while maintaining more than 75 % of the theoretical device efficiency at a hot-end temperature, ( T h ) of 523 K even after 21,600 min of aging. This method of optimizing both the interfacial microstructure and chemical composition provides a new avenue for constructing stably high-performance heterostructure devices.
Elemental diffusion and reactions at interfaces significantly increase resistivity and reduce stability, particularly in thermoelectric (TE) systems containing highly diffusive elements like Te. Using thermodynamically stable phases as contact layers have eliminated interfacial reactions but have not been able to completely stop elemental diffusion. This work introduces an innovative approach to effectively increase the activation energy of cross-interface atom diffusion by creating a continuously interfacial symmetric strain field via a high density of interfacial edge dislocations. Specifically, a dense interfacial strain barrier layer is constructed using Ni 0.5 Te as contact layer, resulting in an atomically continuous Te 0.985 Sb 0.015 /Ni 0.5 Te interface. This design achieves a notable reduction in contact resistivity to 9 μΩ cm 2 while maintaining more than 75 % of the theoretical device efficiency at a hot-end temperature, ( T h ) of 523 K even after 21,600 min of aging. This method of optimizing both the interfacial microstructure and chemical composition provides a new avenue for constructing stably high-performance heterostructure devices.
摘要：
Interface engineering has become a new research field recently. Transition metal dichalcogenides, as a kind of graphenelike two-dimensional semiconductor layered material, can be constructed as rich heterostructures with various other materials, which helps to fully explore the modulation effect of interlayer interaction. Based on first-principles calculation, it is found that MoS2/FeCl2 is a typical metal-semiconductor contact heterostructure with a variety of novel physical properties, including unconventional band alignment, the coexistence of spintronics and valleytronics, and the abnormal valley Hall effect. The change of interlayer interaction leads to the effective regulation of band structure in the system, and the interlayer coupling transforms between weak vdWs force and covalentlike quasibonding interaction depending on the interlayer distance. The transition from n-type to p-type Schottky contact at the interface of the system is also achieved by interlayer engineering. Meanwhile, under the influence of magnetic proximity effect, the heterostructure presents a robust ferromagnetic ground state, but the magnetic anisotropy energy can be transferred from in-plane to out-of-plane. Remarkably, manipulating interlayer coupling through magnetization direction or interlayer proximity can result in alterations of spin and valley polarization. Once synthesized, the MoS2/FeCl2 heterostructure is a potential candidate for multifunctional applications.
摘要：
The large-N limit is a crucial property in many-body quantum systems, playing a important role in advancing quantum theories and technologies. This paper explores the large-N limit of quantum Fisher information (QFI), an experimentally accessible quantum information measure, in one-dimensional (1D) translation-invariant quantum systems. We demonstrate that QFI generally scales as ϱ2N2+ϱ1N in the large-N limit for these systems. Notably, we present a method to extract the scaling coefficients {ϱi} using triangular-matrix-product-operator theory and infinite tensor-network algorithms, circumventing the need for finite-size scaling fittings. By analyzing ground states in infinite-size transverse-field Ising chains and cluster chains, we reveal that {ϱi} offer a concise and informative approach to characterize the achievable precision limit in parameter estimations, metrologically useful multipartite entanglement, quantum criticality, and their relationship in these systems in the large-N limit.
The large-N limit is a crucial property in many-body quantum systems, playing a important role in advancing quantum theories and technologies. This paper explores the large-N limit of quantum Fisher information (QFI), an experimentally accessible quantum information measure, in one-dimensional (1D) translation-invariant quantum systems. We demonstrate that QFI generally scales as ϱ2N2+ϱ1N in the large-N limit for these systems. Notably, we present a method to extract the scaling coefficients {ϱi} using triangular-matrix-product-operator theory and infinite tensor-network algorithms, circumventing the need for finite-size scaling fittings. By analyzing ground states in infinite-size transverse-field Ising chains and cluster chains, we reveal that {ϱi} offer a concise and informative approach to characterize the achievable precision limit in parameter estimations, metrologically useful multipartite entanglement, quantum criticality, and their relationship in these systems in the large-N limit.
通讯机构：
[Mou, Y ] W;Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
关键词：
PLS;Regression;Quantitative analysis
摘要：
The quantitative analysis model for infrared spectroscopy primarily relies on regression methods. Partial Least Squares (PLS) is proposed to overcome the small sample problem through dimensionality reduction. However, spectral data may still include orthogonal variation components. Orthogonal Signal Correction (OSC) methods are developed to remove these orthogonal components, improving analysis accuracy, but they require orthogonality assumptions. Total Least Squares (TLS) regression is introduced to suppress noise and perturbations in both predictor and response variables, yet it does not solve the small sample size issue. Therefore, we propose Total Partial Least Squares Regression (TPLS) and its extended model (TPLSE). These models address both small sample sizes and non-orthogonal noise. We present algorithms, time complexity analysis, and bounds analysis. Validation using four public datasets shows that TPLS and TPLSE outperform PLS, OSC, and TLS in prediction accuracy. We also verify the impact of regularization coefficients on model performance and robustness against noise.
The quantitative analysis model for infrared spectroscopy primarily relies on regression methods. Partial Least Squares (PLS) is proposed to overcome the small sample problem through dimensionality reduction. However, spectral data may still include orthogonal variation components. Orthogonal Signal Correction (OSC) methods are developed to remove these orthogonal components, improving analysis accuracy, but they require orthogonality assumptions. Total Least Squares (TLS) regression is introduced to suppress noise and perturbations in both predictor and response variables, yet it does not solve the small sample size issue. Therefore, we propose Total Partial Least Squares Regression (TPLS) and its extended model (TPLSE). These models address both small sample sizes and non-orthogonal noise. We present algorithms, time complexity analysis, and bounds analysis. Validation using four public datasets shows that TPLS and TPLSE outperform PLS, OSC, and TLS in prediction accuracy. We also verify the impact of regularization coefficients on model performance and robustness against noise.
摘要：
Tea bud localization detection not only ensures tea quality, improves picking efficiency, and advances intelligent harvesting, but also fosters tea industry upgrades and enhances economic benefits. To solve the problem of the high computational complexity of deep learning detection models, we developed the Tea Bud DSCF-YOLOv8n (TBF-YOLOv8n)lightweight detection model. Improvement of the Cross Stage Partial Bottleneck Module with Two Convolutions(C2f) module via efficient Distributed Shift Convolution (DSConv) yields the C2f module with DSConv(DSCf)module, which reduces the model's size. Additionally, the coordinate attention (CA) mechanism is incorporated to mitigate interference from irrelevant factors, thereby improving mean accuracy. Furthermore, the SIOU_Loss (SCYLLA-IOU_Loss) function and the Dynamic Sample(DySample)up-sampling operator are implemented to accelerate convergence and enhance both average precision and detection accuracy. The experimental results show that compared to the YOLOv8n model, the TBF-YOLOv8n model has a 3.7% increase in accuracy, a 1.1% increase in average accuracy, a 44.4% reduction in gigabit floating point operations (GFLOPs), and a 13.4% reduction in the total number of parameters included in the model. In comparison experiments with a variety of lightweight detection models, the TBF-YOLOv8n still performs well in terms of detection accuracy while remaining more lightweight. In conclusion, the TBF-YOLOv8n model achieves a commendable balance between efficiency and precision, offering valuable insights for advancing intelligent tea bud harvesting technologies.
关键词：
Bouguer gravity anomaly;Red River fault;apparent density imaging;bilinear interpolation;gravity field model data
摘要：
The geological structure in the Red River fault zone (RRF) and adjacent areas is complex. Due to the lack of high-precision gravity data in the study area, it is difficult to obtain the distribution of materials within the Earth's crust. In this study, a gravity data-fused method is proposed. The Moho depth model data are utilized to construct the gravity anomaly trend, and the mapping relation between the gravity field model data and the measured gravity data is established. Using 934 high-precision measured gravity data as control points, the bilinear interpolation method is used to calculate high-precision grid data of the RRF. Finally, the apparent density inversion method is used to obtain clear crustal density images across the RRF. The experimental results show that the fuses data not only reflect the regional anomaly trend but also maintain the local anomaly information; the root-mean-square error of the fused data is less than 5% and the correlation coefficient is greater than 90%. Through an in-depth comparative analysis of density images, it is found that the low-density anomalous zones, with depths of ~20 km in the northern and southern sections of the RRF, are shallower than those in the middle. The data-fused method provides a new way to process geophysical data more efficiently.
摘要：
Multipath errors ( MP ) can seriously affect positioning accuracy. Extracting and analyzing the variation characteristics of MP can provide a basis for mitigating it, but the current studies primarily focus on the characteristics of long-term variation of multipath errors while ignoring its short-term variation, which leads to incomplete understanding of the MP . Code and carrier phase dual-frequency observation combination and moving average method are combined to achieve accurate extraction of short-term code multipath error variation ( MP var ), and different moving average strategies are adopted to satisfy the needs of real-time and after-the-fact extraction. The variation characteristics of MP var between sea and land, among different GNSS systems, among different orbits of the BDS system are compared and analyzed. Study indicates that the carrier-to-noise ratio (C/N0) at sea is low and fluctuates greatly compared with the C/N0 at land, but the MP var at sea is much smoother. There are differences in the magnitude of MP var for each GNSS system, but they are all correlated with the elevation angle. For BDS GEO satellites, although the elevation angle variations are minimal, the MP var has significant variations. Therefore, this study suggests that the MP variations of the BDS GEO satellites cannot be regarded as a smooth process when MP sources exist in the vicinity of the static observation stations. The extraction method and analysis results in this study helps to provide ideas for mitigating MP from a perspective of short-term variation.
Multipath errors ( MP ) can seriously affect positioning accuracy. Extracting and analyzing the variation characteristics of MP can provide a basis for mitigating it, but the current studies primarily focus on the characteristics of long-term variation of multipath errors while ignoring its short-term variation, which leads to incomplete understanding of the MP . Code and carrier phase dual-frequency observation combination and moving average method are combined to achieve accurate extraction of short-term code multipath error variation ( MP var ), and different moving average strategies are adopted to satisfy the needs of real-time and after-the-fact extraction. The variation characteristics of MP var between sea and land, among different GNSS systems, among different orbits of the BDS system are compared and analyzed. Study indicates that the carrier-to-noise ratio (C/N0) at sea is low and fluctuates greatly compared with the C/N0 at land, but the MP var at sea is much smoother. There are differences in the magnitude of MP var for each GNSS system, but they are all correlated with the elevation angle. For BDS GEO satellites, although the elevation angle variations are minimal, the MP var has significant variations. Therefore, this study suggests that the MP variations of the BDS GEO satellites cannot be regarded as a smooth process when MP sources exist in the vicinity of the static observation stations. The extraction method and analysis results in this study helps to provide ideas for mitigating MP from a perspective of short-term variation.
关键词：
deep learning;cotton pests and diseases;lightweight model;C2f
摘要：
To address the challenges of detecting cotton pests and diseases in natural environments, as well as the similarities in the features exhibited by cotton pests and diseases, a Lightweight Cotton Disease Detection in Natural Environment (LCDDN-YOLO) algorithm is proposed. The LCDDN-YOLO algorithm is based on YOLOv8n, and replaces part of the convolutional layers in the backbone network with Distributed Shift Convolution (DSConv). The BiFPN network is incorporated into the original architecture, adding learnable weights to evaluate the significance of various input features, thereby enhancing detection accuracy. Furthermore, it integrates Partial Convolution (PConv) and Distributed Shift Convolution (DSConv) into the C2f module, called PDS-C2f. Additionally, the CBAM attention mechanism is incorporated into the neck network to improve model performance. A Focal-EIoU loss function is also integrated to optimize the model's training process. Experimental results show that compared to YOLOv8, the LCDDN-YOLO model reduces the number of parameters by 12.9% and the floating-point operations (FLOPs) by 9.9%, while precision, mAP@50, and recall improve by 4.6%, 6.5%, and 7.8%, respectively, reaching 89.5%, 85.4%, and 80.2%. In summary, the LCDDN-YOLO model offers excellent detection accuracy and speed, making it effective for pest and disease control in cotton fields, particularly in lightweight computing scenarios.
期刊：
Surfaces and Interfaces,2025年57:105743 ISSN：2468-0230
通讯作者：
Zhao, X;Wu, SJ
作者机构：
[Chen, Luocheng] Hubei Sino Australian Nano Mat Technol Ltd Co, Guangshui 432700, Peoples R China.;[Zhao, X; Zhao, Xu; Chen, Luocheng] Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.;[Wu, Sujuan; Cai, Hengzhuo; Wu, SJ; Luo, Xinyi] South China Normal Univ, Inst Adv Mat, South China Acad Adv Optoelect, Guangzhou 510006, Peoples R China.;[Wu, Sujuan; Cai, Hengzhuo; Wu, SJ; Luo, Xinyi] South China Normal Univ, South China Acad Adv Optoelect, Guangdong Prov Key Lab Quantum Engn & Quantum Mat, Guangzhou 510006, Peoples R China.
通讯机构：
[Zhao, X ] W;[Wu, SJ ] S;Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.;South China Normal Univ, Inst Adv Mat, South China Acad Adv Optoelect, Guangzhou 510006, Peoples R China.;South China Normal Univ, South China Acad Adv Optoelect, Guangdong Prov Key Lab Quantum Engn & Quantum Mat, Guangzhou 510006, Peoples R China.
关键词：
1-butyl-3-methylimidazolium p-;toluenesulfonate;PEDOT:PSS/perovskite interface;photoelectrical properties;Pure Sn-based perovskite solar cells
摘要：
Poly(3,4-ethylenedioxythiophene)-polystyrene sulfonate (PEDOT:PSS) film can absorb moisture from the air, which will become a potential challenge to accelerate the degradation of perovskite solar cells (PSCs). The acidity of PEDOT:PSS can damage the adjacent ITO and perovskite layer, resulting in deteriorating the stability of PSCs. Moreover, the level energy misalignment at the interface will lead to carrier recombination and reducing the efficiency of device. To resolve these problem in PEDOT:PSS layer, the 1‑butyl‑3-methylimidazolium p-toluenesulfonate (BMT) with imidazole and sulfonic acid dual-functional groups is used to modify the PEDOT:PSS film in pure tin-based perovskite solar cells (T-PSCs). The effects of BMT modification on the micrograph and photoelectrical properties of PEDOT:PSS and pure Sn-based perovskite layer, and the photovoltaic performance of T-PSCs have been thoroughly studied. It is found that the BMT modification not only improves the conductivity of the PEDOT:PSS layer but also adjusts its work function, facilitates the hole extraction and transport at the PEDTO:PSS/perovskite interface, leading to significantly increasing the open-circuit voltage. In addition, the perovskite films based on BMT-modified PEDOT:PSS (BMT-PEDOT:PSS) exhibit the improved microstructure and higher Sn²⁺/Sn 4 ⁺ ratio. Thus, the T-PSCs based on the BMT-PEDOT:PSS (BMT-PSCs) at the optimized BMT concentration reach a champion efficiency of 8.74 %, which is higher than the 6.94 % of reference device. Furthermore, the BMT-PSCs demonstrate better long-term stability in N 2 atmosphere. This work provides a simple strategy to regulate the burried PEDOT:PSS/perovkite interface and improve the photovoltaic performance and stability of T-PSCs by a dual-functional material.
Poly(3,4-ethylenedioxythiophene)-polystyrene sulfonate (PEDOT:PSS) film can absorb moisture from the air, which will become a potential challenge to accelerate the degradation of perovskite solar cells (PSCs). The acidity of PEDOT:PSS can damage the adjacent ITO and perovskite layer, resulting in deteriorating the stability of PSCs. Moreover, the level energy misalignment at the interface will lead to carrier recombination and reducing the efficiency of device. To resolve these problem in PEDOT:PSS layer, the 1‑butyl‑3-methylimidazolium p-toluenesulfonate (BMT) with imidazole and sulfonic acid dual-functional groups is used to modify the PEDOT:PSS film in pure tin-based perovskite solar cells (T-PSCs). The effects of BMT modification on the micrograph and photoelectrical properties of PEDOT:PSS and pure Sn-based perovskite layer, and the photovoltaic performance of T-PSCs have been thoroughly studied. It is found that the BMT modification not only improves the conductivity of the PEDOT:PSS layer but also adjusts its work function, facilitates the hole extraction and transport at the PEDTO:PSS/perovskite interface, leading to significantly increasing the open-circuit voltage. In addition, the perovskite films based on BMT-modified PEDOT:PSS (BMT-PEDOT:PSS) exhibit the improved microstructure and higher Sn²⁺/Sn 4 ⁺ ratio. Thus, the T-PSCs based on the BMT-PEDOT:PSS (BMT-PSCs) at the optimized BMT concentration reach a champion efficiency of 8.74 %, which is higher than the 6.94 % of reference device. Furthermore, the BMT-PSCs demonstrate better long-term stability in N 2 atmosphere. This work provides a simple strategy to regulate the burried PEDOT:PSS/perovkite interface and improve the photovoltaic performance and stability of T-PSCs by a dual-functional material.
通讯机构：
[Gao, Y ] D;Dezhou Univ, Coll Phys & Elect Informat, Dezhou 253023, Peoples R China.;Int Ctr Supernovae, Yunnan Key Lab, Kunming 650216, Yunnan, Peoples R China.
摘要：
We present the analysis of a comprehensive sample of 352 early-type galaxies using public data, to investigate the correlations between CO luminosities and mid-infrared luminosities observed by Wide-field Infrared Survey Explorer. We find strong correlations between both CO (1-0) and CO (2-1) luminosities and 12 mu m luminosity, boasting a correlation coefficient greater than 0.9 and an intrinsic scatter smaller than 0.1 dex. The consistent slopes observed for the relationships of CO (1-0) and CO (2-1) suggest that the line ratio R21 lacks correlation with mid-infrared emission in early-type galaxies, which is significantly different from star-forming galaxies. Moreover, the slopes of LCO(1-0)-L12 mu m and LCO(2-1)-L12 mu m relations in early-type galaxies are steeper than those observed in star-forming galaxies. Given the absence of correlation with color, morphology, or specific star formation rate (sSFR), the correlation between deviations and the molecular gas mass surface density could be eliminated by correcting the possible 12 mu m emission from old stars or adopting a systematically different alpha CO. The latter, on average, is equivalent to adding a constant CO brightness density, specifically 2.8-0.6+0.8[Kkms-1] and 4.4-1.4+2.2[Kkms-1] for CO (1-0) and (2-1), respectively. These explorations will serve as useful tools for estimating the molecular gas content in gas-poor galaxies and understanding associated quenching processes.
摘要：
Noncontact trapping of micro objects has great application potential in fields like material science and biomedical engineering due to its label-freeness and biocompatibility. In this paper, an automated acoustic micro-particle trapping system implemented with phased transducer array (PTA) is prototyped. The system is incorporated with a stereo vision to provide visual feedback benefited from localization of the invisible acoustic field through hydrophone scanning. Binocular vision calibration and stereo matching are realized using image Jacobian matrix. An efficient phase modulation algorithm is proposed for the calculation of desired PTA phase profile in real-time and a spatiotemporal multiplexing control strategy is adopted to dynamically generate multiple trappings. Experimental results well demonstrated that the stable trapping of multiple particles can be robustly realized by the system, leading to the improvements of robotic noncontact manipulation with invisible acoustic end-effector. Note to Practitioners—This paper is motivated by the problem that previous classic acoustic trapping was achieved as a physical phenomenon that particles within the trapping zone would be automatically trapped and thus required people to place the particle into the invisible trapping zone, which is neither precision nor efficient. Such problem is a crucial factor that limits acoustic tweezer to be further readily usable in bioengineering, surface manufacturing, and quantitative micromechanical characterization. In this work, automated acoustic trapping is presented in the context of robotics, as grasping task in conventional industrial robots, that can generate the acoustic trap exactly in the location where particles are detected (by microscopic vision, or micro-CT or acoustic imaging, etc.). This paper proposes a full pipeline to automatically trap multiple particles using ultrasonic transducer array and binocular microscopic vision. The experiments verified the ability of proposed method in simultaneously trapping three micro particles with opposite acoustic properties. Such trapping method is the foundational technology for further acoustic manipulation such as arraying and sorting, which will be the tasks in our future work.
摘要：
High-voltage gas-insulated switchgear (GIS) experiences insulation aging and related issues during prolonged operation, which significantly reduces the stability and safety of energy power equipment. Therefore, real-time monitoring and assessment of the insulation condition of these devices is essential. This study, based on first-principles calculations, reveals the gas-sensitive properties of decomposition gases of SF 6 and its five insulation defects on the surfaces of AlN and Pt 2 –AlN at the quantum level by calculating the electronic density (total electronic density, differential electronic density, and spin density), density of states (total and partial density of states), and the Integrated Crystal Orbital Hamilton Population (ICOHP). Notably, the modification of the Pt 2 cluster significantly enhances the electronic properties of AlN, improving the overall conductivity of the system. Furthermore, the adsorption properties of the p-type semiconductor Pt 2 -ALN for H 2 S are improved, and calculations of the work function, band gap, and its rate of change indicate that the doped structure possesses the potential to be used as a sensor. Additionally, we explored the feasibility of AlN (targeting SO 2 ) and Pt 2 –AlN (targeting H 2 S) as insulation defect warning materials under different environmental conditions. The results of the sensor application calculations demonstrate that AlN possesses the capability for SO 2 purification and can function as a disposable embedded sensor array, while Pt 2 –AlN shows promise as a sustainable sensor material for H 2 S monitoring at room temperature (300 K), with a desorption time of 0.46 s. Our research aims to provide new insights into semiconductor sensor monitoring of SF 6 gas-insulated equipment and offers guidance for the development of novel materials and sensor devices.
High-voltage gas-insulated switchgear (GIS) experiences insulation aging and related issues during prolonged operation, which significantly reduces the stability and safety of energy power equipment. Therefore, real-time monitoring and assessment of the insulation condition of these devices is essential. This study, based on first-principles calculations, reveals the gas-sensitive properties of decomposition gases of SF 6 and its five insulation defects on the surfaces of AlN and Pt 2 –AlN at the quantum level by calculating the electronic density (total electronic density, differential electronic density, and spin density), density of states (total and partial density of states), and the Integrated Crystal Orbital Hamilton Population (ICOHP). Notably, the modification of the Pt 2 cluster significantly enhances the electronic properties of AlN, improving the overall conductivity of the system. Furthermore, the adsorption properties of the p-type semiconductor Pt 2 -ALN for H 2 S are improved, and calculations of the work function, band gap, and its rate of change indicate that the doped structure possesses the potential to be used as a sensor. Additionally, we explored the feasibility of AlN (targeting SO 2 ) and Pt 2 –AlN (targeting H 2 S) as insulation defect warning materials under different environmental conditions. The results of the sensor application calculations demonstrate that AlN possesses the capability for SO 2 purification and can function as a disposable embedded sensor array, while Pt 2 –AlN shows promise as a sustainable sensor material for H 2 S monitoring at room temperature (300 K), with a desorption time of 0.46 s. Our research aims to provide new insights into semiconductor sensor monitoring of SF 6 gas-insulated equipment and offers guidance for the development of novel materials and sensor devices.
关键词：
All data in this work were obtained from the Web of Science database. Keywords searched include “SF6;decomposition;gas sensor;adsorption;DFT;etc.”. After an initial manual screening;a total of 52 research efforts from the past decade (from 2014 to 2024) were selected;focusing on the exploration of gas-sensitive materials for SF6 decomposition products;yielding 250 sets of adsorption data [26];[42] With the help of VOS Viewer software;the collected papers on SF6 decomposition products were clustered and analyzed;and the data were visualized by the keyword co-occurrence mapping module;as shown in Fig. S1 (a);(b) and (c). Fig. S1 (a) presents the changes in the research hotspots related to SF6 decomposition products during the five-year period from 2018 to 2022. Obviously;until 2020;some keywords can be observed;such as ‘decomposition’;‘voltage’;‘characteristic’;‘gas mixture’.
摘要：
The man-made gas sulfur hexafluoride (SF6) is an excellent and stable insulating medium. However, some insulation defects can cause SF6 to decompose, threatening the safe operation of power grids. Based on this, it is of great significance to find and effectively control the decomposition products of SF6 in time. Gas sensors have proven to be an effective way to detect these decomposition gases (SO2, SOF2, SO2F2, H2S, and HF). Nanomaterials with gas-sensitive properties are at the heart of gas sensors. In recent years, data-driven machine learning (ML) has been widely used to predict material properties and discover new materials. However, it has become a major challenge to establish a common model between material properties derived from various types of calculations and intelligent algorithms. In order to make some progress in addressing this challenge. In this work, 250 data sets were extracted from 52 publications exploring the detection of SF6 decomposition products by nanocomposites based on relevant work over the past 10 years, and the adsorption behavior of SF6 decomposition products can be predictively analyzed. By comparing six different algorithmic models, the best model for predicting the adsorption distance (XGBoost: R2 = 91.94 %) and adsorption energy (GBR: R2 = 78.63 %) of SF6 decomposed gas was identified. Subsequently, the importance of each of the selected feature descriptors in predicting the gas adsorption effect was explained. This work combines first-principles computational results and machine-learning algorithms with each other to provide a new research idea for evaluating the gas sensing capability of nanocomposites.
The man-made gas sulfur hexafluoride (SF6) is an excellent and stable insulating medium. However, some insulation defects can cause SF6 to decompose, threatening the safe operation of power grids. Based on this, it is of great significance to find and effectively control the decomposition products of SF6 in time. Gas sensors have proven to be an effective way to detect these decomposition gases (SO2, SOF2, SO2F2, H2S, and HF). Nanomaterials with gas-sensitive properties are at the heart of gas sensors. In recent years, data-driven machine learning (ML) has been widely used to predict material properties and discover new materials. However, it has become a major challenge to establish a common model between material properties derived from various types of calculations and intelligent algorithms. In order to make some progress in addressing this challenge. In this work, 250 data sets were extracted from 52 publications exploring the detection of SF6 decomposition products by nanocomposites based on relevant work over the past 10 years, and the adsorption behavior of SF6 decomposition products can be predictively analyzed. By comparing six different algorithmic models, the best model for predicting the adsorption distance (XGBoost: R2 = 91.94 %) and adsorption energy (GBR: R2 = 78.63 %) of SF6 decomposed gas was identified. Subsequently, the importance of each of the selected feature descriptors in predicting the gas adsorption effect was explained. This work combines first-principles computational results and machine-learning algorithms with each other to provide a new research idea for evaluating the gas sensing capability of nanocomposites.
摘要：
The rapid changes in the global environment have led to an unprecedented decline in biodiversity, with over 28% of species facing extinction. This includes snakes, which are key to ecological balance. Detecting snakes is challenging due to their camouflage and elusive nature, causing data loss and feature extraction difficulties in ecological monitoring. To address these challenges, we propose an enhanced snake detection model, Snake-DETR, based on RT-DETR, specifically designed for snake detection in complex natural environments. First, we designed the Enhanced Generalized Efficient Layer Aggregation Network Based on Context Anchor Attention, which enhances the feature extraction capability for occluded snakes by aggregating critical layer information and strengthening context-dependent feature extraction. Additionally, we introduced the Enhanced Feature Extraction Backbone Network Based on Context Anchor Attention, which manages input information using multiple Enhanced Generalized Efficient Layer Aggregation Networks to retain essential spatial and semantic information. Subsequently, a lightweight Group-Shuffle Convolution is used to optimize the encoder, which reduces dependency on large-scale training data, thereby making it suitable for deployment on edge devices. Finally, we incorporated the Powerful-IoU loss function to improve regression path accuracy. Experimental results on a custom dataset covering 27 snake species demonstrate that Snake-DETR achieves a good balance between model efficiency and detection performance, meeting the requirements for fine-grained snake object detection. Compared to other state-of-the-art models, Snake-DETR achieved an accuracy of 97.66%, a recall rate of 93.92%, mAP@0.5 of 95.23%, and mAP@0.5:0.95 of 72.15%, all outperforming other algorithms in the comparative tests. Furthermore, the computational load and parameter count of the model are reduced by 47.2 and 52.2%, respectively, compared to the benchmark model. Additionally, the real-time processing capability is 43.5 frames per second, meeting the demand for real-time processing. Snake-DETR demonstrates excellent performance in complex environments and is suitable for wild snake fauna monitoring and edge device deployment, providing key technical support for ecological research.
关键词：
Dissolved gases in oil;Machine learning;Transition metal dichalcogenides;Gas sensor materials;DFT
摘要：
Rapid advances in 2D materials and elemental doping technologies have developed a wide range of potential sensor materials for detecting gases. However, the challenge is to explore possible material combinations efficiently. While first-principles calculations can accurately predict molecular-scale properties and guide experimental material design, they are too computationally expensive to evaluate all possible transition metal-doped 2D materials. Data-based machine learning methods bring a new approach to this dilemma. In our work, for the exploration of gas-sensitive materials for oil-immersed transformer failures, we constructed datasets of adsorption energies of four transformer dissolved gases on transition-metal dichalcogenides (TMDs) doped with transition metals and trained ten common classification models for adsorption energy prediction. By comparing precision, accuracy, recall, and f1-score, we identified three well-performing models: Bagging, Voting, and RandomForest. The ROC curves and confusion matrix analyses show that the Bagging has the most balanced performance among all prediction categories. The results of the three models analyzed by SHAP show that the newly introduced CM significantly impact the adsorption energy prediction and can be used as a new descriptor for adsorption energy prediction. Finally, we used models to predict the adsorption energies on TM-doped WSe 2 substrates, and a series of analyses identified Au–WSe 2 (C 2 H 2 and C 2 H 4 ), Cu–WSe 2 (CO), and Ni–WSe 2 (H 2 ) as promising sensor materials. Our work provides a powerful tool for the efficient screening of promising materials for dissolved gas sensors in oil, and the application of machine learning in materials science provides experience and methodology.
Rapid advances in 2D materials and elemental doping technologies have developed a wide range of potential sensor materials for detecting gases. However, the challenge is to explore possible material combinations efficiently. While first-principles calculations can accurately predict molecular-scale properties and guide experimental material design, they are too computationally expensive to evaluate all possible transition metal-doped 2D materials. Data-based machine learning methods bring a new approach to this dilemma. In our work, for the exploration of gas-sensitive materials for oil-immersed transformer failures, we constructed datasets of adsorption energies of four transformer dissolved gases on transition-metal dichalcogenides (TMDs) doped with transition metals and trained ten common classification models for adsorption energy prediction. By comparing precision, accuracy, recall, and f1-score, we identified three well-performing models: Bagging, Voting, and RandomForest. The ROC curves and confusion matrix analyses show that the Bagging has the most balanced performance among all prediction categories. The results of the three models analyzed by SHAP show that the newly introduced CM significantly impact the adsorption energy prediction and can be used as a new descriptor for adsorption energy prediction. Finally, we used models to predict the adsorption energies on TM-doped WSe 2 substrates, and a series of analyses identified Au–WSe 2 (C 2 H 2 and C 2 H 4 ), Cu–WSe 2 (CO), and Ni–WSe 2 (H 2 ) as promising sensor materials. Our work provides a powerful tool for the efficient screening of promising materials for dissolved gas sensors in oil, and the application of machine learning in materials science provides experience and methodology.
摘要：
Graph clustering is a critical task in network analysis, aimed at grouping nodes based on their structural or attributed similarities. In particular, attributed graph clustering, which considers both structural links and node attributes, is essential for complex networks where additional node information is available. Nonnegative Matrix Factorization (NMF) has shown promise in graph clustering; however, it faces limitations when applied to attributed graph clustering, such as an inability to detect outliers, distortion of geometric data point structures, and disregard for attributed information. Moreover, many existing attributed graph clustering methods overlook distant node relationships due to network sparsity, which hinders further performance improvements. To address these challenges, this paper introduces Weighted Symmetric NMF with graph-Boosting for attributed Graph Clustering (WSBGC), an innovative extension of NMF. WSBGC leverages attribute similarity among nodes to create a weighted version of NMF, enabling outlier detection while preserving the geometric structure of data points. Additionally, WSBGC employs graph-boosting, leveraging attribute information to account for distant node relationships and improve clustering accuracy. A graph attention auto-encoder is then used to construct the final clustering model. The effectiveness of WSBGC is validated through extensive experiments on real-world datasets. Notably, our algorithm improves accuracy by 2.5% compared to the best available method, demonstrating its superior clustering performance in attributed graphs.
Graph clustering is a critical task in network analysis, aimed at grouping nodes based on their structural or attributed similarities. In particular, attributed graph clustering, which considers both structural links and node attributes, is essential for complex networks where additional node information is available. Nonnegative Matrix Factorization (NMF) has shown promise in graph clustering; however, it faces limitations when applied to attributed graph clustering, such as an inability to detect outliers, distortion of geometric data point structures, and disregard for attributed information. Moreover, many existing attributed graph clustering methods overlook distant node relationships due to network sparsity, which hinders further performance improvements. To address these challenges, this paper introduces Weighted Symmetric NMF with graph-Boosting for attributed Graph Clustering (WSBGC), an innovative extension of NMF. WSBGC leverages attribute similarity among nodes to create a weighted version of NMF, enabling outlier detection while preserving the geometric structure of data points. Additionally, WSBGC employs graph-boosting, leveraging attribute information to account for distant node relationships and improve clustering accuracy. A graph attention auto-encoder is then used to construct the final clustering model. The effectiveness of WSBGC is validated through extensive experiments on real-world datasets. Notably, our algorithm improves accuracy by 2.5% compared to the best available method, demonstrating its superior clustering performance in attributed graphs.
作者机构：
[Zhao, Xu; Li, Shaozhen] Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.;[Wu, Sujuan; Wu, SJ; Wu, Shengcheng; Gao, Naitao] South China Normal Univ, Inst Adv Mat, South China Acad Adv Optoelect, Guangzhou 510006, Peoples R China.;[Wu, Sujuan; Wu, SJ; Wu, Shengcheng] South China Normal Univ, South China Acad Adv Optoelect, Guangdong Prov Key Lab Quantum Engn & Quantum Mat, Guangzhou 510006, Peoples R China.
通讯机构：
[Wu, SJ ] S;[Li, SZ ] W;Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.;South China Normal Univ, Inst Adv Mat, South China Acad Adv Optoelect, Guangzhou 510006, Peoples R China.;South China Normal Univ, South China Acad Adv Optoelect, Guangdong Prov Key Lab Quantum Engn & Quantum Mat, Guangzhou 510006, Peoples R China.
关键词：
polyethylene oxide-modified TiO 2 film;low-temperature process;CsPbI 2 Br-based all-inorganic perovskite solar cells;photovoltaic performance
摘要：
CsPbX3-based (X = I, Br, Cl) inorganic perovskite solar cells (PSCs) prepared by low-temperature process have attracted much attention because of their low cost and excellent thermal stability. However, the high trap state density and serious charge recombination between low-temperature processed TiO2 film and inorganic perovskite layer interface seriously restrict the performance of all-inorganic PSCs. Here a thin polyethylene oxide (PEO) layer is employed to modify TiO2 film to passivate traps and promote carrier collection. The impacts of PEO layer on microstructure and photoelectric characteristics of TiO2 film and related devices are systematically studied. Characterization results suggest that PEO modification can reduce the surface roughness of TiO2 film, decrease its average surface potential, and passivate trap states. At optimal conditions, the champion efficiency of CsPbI2Br PSCs with PEO-modified TiO2 (PEO-PSCs) has been improved to 11.24% from 9.03% of reference PSCs. Moreover, the hysteresis behavior and charge recombination have been suppressed in PEO-PSCs.
