关键词：
Solar energy;Photothermal conversion;Temperature difference power generation;Photothermal-thermal power
摘要：
The two-phase MoO 2 /Mo 4 O 11 NPs were prepared by dry reduction of MoO 3 NPs, which are highly regarded for their excellent solar photovoltaic conversion performance and excellent thermal stability. The materials were tested and found to have less than 10 % reflectance and up to more than 95 % absorption in the range of 200–2500 nm of the AM1.5 global standard solar spectrum. The two-phase MoO 2 /Mo 4 O 11 NPs rapidly warmed up from room temperature to the maximum equilibrium temperature of 50 °C in 20 s under one solar light intensity. And no photothermal degradation occurred under five photothermal cycle tests. The two-phase MoO 2 /Mo 4 O 11 NPs were combined with the temperature difference power generation technology to construct a photothermal power generation device. Under a standard sunlight intensity, the voltage can reach 0.27 V, and the maximum voltage that can be achieved in the photothermal power generation cycle test is almost the same.
The two-phase MoO 2 /Mo 4 O 11 NPs were prepared by dry reduction of MoO 3 NPs, which are highly regarded for their excellent solar photovoltaic conversion performance and excellent thermal stability. The materials were tested and found to have less than 10 % reflectance and up to more than 95 % absorption in the range of 200–2500 nm of the AM1.5 global standard solar spectrum. The two-phase MoO 2 /Mo 4 O 11 NPs rapidly warmed up from room temperature to the maximum equilibrium temperature of 50 °C in 20 s under one solar light intensity. And no photothermal degradation occurred under five photothermal cycle tests. The two-phase MoO 2 /Mo 4 O 11 NPs were combined with the temperature difference power generation technology to construct a photothermal power generation device. Under a standard sunlight intensity, the voltage can reach 0.27 V, and the maximum voltage that can be achieved in the photothermal power generation cycle test is almost the same.
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.
摘要：
Mechanical-thermal coupling mechanisms in silicone foam (SF) composites play a crucial role in optimizing their performance for aerospace, automotive, and construction applications, where lightweight design and thermal efficiency are essential. This study presents a comprehensive theoretical framework to evaluate the mechanical and thermal properties of SF composites reinforced by carbon fibers (CF) and aluminum particles (Al) under axial pressure. A four-phase composite model is developed to incorporate inclusions, matrix and voids, accounting for morphological changes in the foam structure. The model employs the Mori-Tanaka method to predict the elastoplastic behaviors, while effective-medium approximation is used to determine thermal conductivity. The framework also considers interfacial effects, including interfacial sliding, the Kapitza resistance, and filler-filler contact. Comparisons with experimental data validate the model and reveal that CF/Al/SF composites exhibit superior thermal and mechanical properties, with CFs demonstrating a more pronounced impact. These findings underscore the interplay between mechanical loading, void morphology, and thermal performance, highlighting the importance of tailoring CF/Al ratios and processing conditions to achieve synergistic mechanical-thermal properties of SF-based composites.
Mechanical-thermal coupling mechanisms in silicone foam (SF) composites play a crucial role in optimizing their performance for aerospace, automotive, and construction applications, where lightweight design and thermal efficiency are essential. This study presents a comprehensive theoretical framework to evaluate the mechanical and thermal properties of SF composites reinforced by carbon fibers (CF) and aluminum particles (Al) under axial pressure. A four-phase composite model is developed to incorporate inclusions, matrix and voids, accounting for morphological changes in the foam structure. The model employs the Mori-Tanaka method to predict the elastoplastic behaviors, while effective-medium approximation is used to determine thermal conductivity. The framework also considers interfacial effects, including interfacial sliding, the Kapitza resistance, and filler-filler contact. Comparisons with experimental data validate the model and reveal that CF/Al/SF composites exhibit superior thermal and mechanical properties, with CFs demonstrating a more pronounced impact. These findings underscore the interplay between mechanical loading, void morphology, and thermal performance, highlighting the importance of tailoring CF/Al ratios and processing conditions to achieve synergistic mechanical-thermal properties of SF-based composites.
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：
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.
摘要：
The development of ultra-high-performance sensing materials for monitoring the decomposition products of environmental protection insulating gases (C₄F₇N and C₅F₁₀O) under insulation failure conditions is crucial for ensuring the operational safety of gas-insulated switchgear (GIS). In this study, a Mo₂-embedded C₆N₈ (Mo₂-C₆N₈) monolayer structure (selected by a machine learning driven high-throughput computational strategy) is proposed as a novel two-dimensional sensing substrate. Mo₂ cluster doping endows C₆N₈ with enhanced thermal stability and optimized electronic properties, which are critical for gas adsorption and sensing applications. Adsorption tests show that C₂N₂ and CO exhibit strong interactions with Mo₂-C₆N₈, with adsorption energies of -6.06 and -2.98 eV, respectively. Notably, at the operating temperature threshold, the desorption times for both C₂N₂ and CO exceed 10¹⁶ s, indicating that Mo₂-C₆N₈ can serve as an auxiliary sensing device in sensor arrays to monitor C₂N₂ and CO, or as a pure adsorbent for these two gases. Mechanistic insights obtained through Total Density of States (TDOS), Partial Density of States (PDOS), Charge Density Difference (CDD), Differential Electron Density (DED) and Crystal Orbital Hamilton Population (COHP) analysis reveal orbital hybridization and binding mechanisms between Mo₂-C₆N₈ and the target gases. The significant work function modulation (6.4–8.92 %) induced by gas adsorption highlights the potential of this material for Φ-type sensor design. Among the candidate gases, HF shows outstanding monitoring potential due to its high sensitivity, tunable desorption kinetics (microsecond to millisecond scale), and unique applicability in wet insulation defects, which is in line with the requirements for GIS status assessment in wet environments. This study not only deepens the atomic-scale understanding of gas-solid interactions, but also provides a technical roadmap for the design of multifunctional sensors and catalytic systems in power plant diagnostics, while providing relevant evidence to validate the accuracy of machine learning-based screening models.
The development of ultra-high-performance sensing materials for monitoring the decomposition products of environmental protection insulating gases (C₄F₇N and C₅F₁₀O) under insulation failure conditions is crucial for ensuring the operational safety of gas-insulated switchgear (GIS). In this study, a Mo₂-embedded C₆N₈ (Mo₂-C₆N₈) monolayer structure (selected by a machine learning driven high-throughput computational strategy) is proposed as a novel two-dimensional sensing substrate. Mo₂ cluster doping endows C₆N₈ with enhanced thermal stability and optimized electronic properties, which are critical for gas adsorption and sensing applications. Adsorption tests show that C₂N₂ and CO exhibit strong interactions with Mo₂-C₆N₈, with adsorption energies of -6.06 and -2.98 eV, respectively. Notably, at the operating temperature threshold, the desorption times for both C₂N₂ and CO exceed 10¹⁶ s, indicating that Mo₂-C₆N₈ can serve as an auxiliary sensing device in sensor arrays to monitor C₂N₂ and CO, or as a pure adsorbent for these two gases. Mechanistic insights obtained through Total Density of States (TDOS), Partial Density of States (PDOS), Charge Density Difference (CDD), Differential Electron Density (DED) and Crystal Orbital Hamilton Population (COHP) analysis reveal orbital hybridization and binding mechanisms between Mo₂-C₆N₈ and the target gases. The significant work function modulation (6.4–8.92 %) induced by gas adsorption highlights the potential of this material for Φ-type sensor design. Among the candidate gases, HF shows outstanding monitoring potential due to its high sensitivity, tunable desorption kinetics (microsecond to millisecond scale), and unique applicability in wet insulation defects, which is in line with the requirements for GIS status assessment in wet environments. This study not only deepens the atomic-scale understanding of gas-solid interactions, but also provides a technical roadmap for the design of multifunctional sensors and catalytic systems in power plant diagnostics, while providing relevant evidence to validate the accuracy of machine learning-based screening models.
摘要：
We propose a theoretical scheme to realize two-dimensional (2D) asymmetric electromagnetically induced grating (EIG) in a closed four-level inverted-Y type semiconductor quantum wells (SQWs) system. The 2D asymmetric grating is formed by the interaction of a weak probe field, a circulating field, and two simultaneously acting 2D standing wave (SW) field and composite Laguerre–Gaussian (LG) vortex field. After deriving the Fraunhofer diffraction equation of the probe beam, we numerically investigated the amplitude modulation , phase modulation , and Fraunhofer diffraction characteristics of the weak probe beam under different conditions. We analyze the impact of turning on or off the circulating field on the formation of grating. By adjusting the detunings of corresponding fields, SW field intensity, high efficiency 2D asymmetric diffraction grating can be obtained under appropriate circumstances. Due to the phase sensitivity of the closed loop structure of the SQWs system, the relative phase difference between the applied fields can be used to effectively control the alterations of diffraction intensity and direction of the grating. Moreover, the diffraction pattern of the probe field can be altered by simply adjusting the OAM value of the LG beam. The diffraction energy distribution of the probe field can be manipulated and shifted in different regions especially in the higher-order direction. The scheme we presented opens up the possibility of realizing novel applications in optical transmissions, and development of new photonic devices .
We propose a theoretical scheme to realize two-dimensional (2D) asymmetric electromagnetically induced grating (EIG) in a closed four-level inverted-Y type semiconductor quantum wells (SQWs) system. The 2D asymmetric grating is formed by the interaction of a weak probe field, a circulating field, and two simultaneously acting 2D standing wave (SW) field and composite Laguerre–Gaussian (LG) vortex field. After deriving the Fraunhofer diffraction equation of the probe beam, we numerically investigated the amplitude modulation , phase modulation , and Fraunhofer diffraction characteristics of the weak probe beam under different conditions. We analyze the impact of turning on or off the circulating field on the formation of grating. By adjusting the detunings of corresponding fields, SW field intensity, high efficiency 2D asymmetric diffraction grating can be obtained under appropriate circumstances. Due to the phase sensitivity of the closed loop structure of the SQWs system, the relative phase difference between the applied fields can be used to effectively control the alterations of diffraction intensity and direction of the grating. Moreover, the diffraction pattern of the probe field can be altered by simply adjusting the OAM value of the LG beam. The diffraction energy distribution of the probe field can be manipulated and shifted in different regions especially in the higher-order direction. The scheme we presented opens up the possibility of realizing novel applications in optical transmissions, and development of new photonic devices .
作者：
Mou, Yi;Zhou, Long;Chen, Weizhen;Liu, Jianguo;Li, Teng
期刊：
Algorithms,2025年18(7):424- ISSN：1999-4893
通讯作者：
Yi Mou
作者机构：
[Chen, Weizhen; Liu, Jianguo; Li, Teng] School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430024, China;[Zhou, Long] School of Mechanical Engineering, Wuhan Polytechnic University, Wuhan 430024, China;Author to whom correspondence should be addressed.;[Mou, Yi] School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430024, China<&wdkj&>Author to whom correspondence should be addressed.
通讯机构：
[Yi Mou] S;School of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430024, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
partial least squares;regression analysis;filter learning;content prediction
摘要：
Partial Least Squares (PLS) regression has been widely used to model the relationship between predictors and responses. However, PLS may be limited in its capacity to handle complex spectral data contaminated with significant noise and interferences. In this paper, we propose a novel filter learning-based PLS (FPLS) model that integrates an adaptive filter into the PLS framework. The FPLS model is designed to maximize the covariance between the filtered spectral data and the response. This modification enables FPLS to dynamically adapt to the characteristics of the data, thereby enhancing its feature extraction and noise suppression capabilities. We have developed an efficient algorithm to solve the FPLS optimization problem and provided theoretical analyses regarding the convergence of the model, the prediction variance, and the relationships among the objective functions of FPLS, PLS, and the filter length. Furthermore, we have derived bounds for the Root Mean Squared Error of Prediction (RMSEP) and the Cosine Similarity (CS) to evaluate model performance. Experimental results using spectral datasets from Corn, Octane, Mango, and Soil Nitrogen show that the FPLS model outperforms PLS, OSCPLS, VCPLS, PoPLS, LoPLS, DOSC, OPLS, MSC, SNV, SGFilter, and Lasso in terms of prediction accuracy. The theoretical analyses align with the experimental results, emphasizing the effectiveness and robustness of the FPLS model in managing complex spectral data.
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：
Building roof plane segmentation is important for three-dimensional (3D) building model reconstruction from airborne light detection and ranging (LiDAR) point data. During the roof plane segmentation, challenges such as pseudo planes, over- and under-segmentation often arise, particularly evident in the boundary regions. To improve the accuracy of plane segmentation, various energy optimization-based methods have been proposed to refine the roof planes. However, the existing methods optimize the energy function at the point level, which may lead to getting stuck in local optima. To address these problems, we propose a coarse-to-fine boundary relabeling approach for roof plane segmentation. Starting from an initial plane segmentation result, the proposed method iteratively refines the planes by adjusting the boundaries from the voxel level to the point level. In addition, we also design a new energy function that considers accurateness, smoothness and compactness to guide the optimization. The experimental results constructed on two datasets demonstrate that the proposed method outperforms the existing roof plane segmentation methods, achieving high accuracy and smooth boundary extraction. The source code of the proposed approach will be publicly available at https://github.com/Li-Li-Whu/Coarse2FineRoofPlane.
摘要：
The rapid recombination rate of carriers and the lack of active sites are the key factors limiting the activity of CdS photocatalytic H 2 generation. Gradient P-doping could form a built-in electric field to promote carrier migration to the surface. In this work, the contact between cocatalyst CoZnS x (CZS) and P-doped CdS (P-CdS) forms interfacial heterojunction and build fast electron transfer channel which promote the transfer of carriers to cocatalyst and enrichment at the active site of hydrogen evolution. The optimal catalyst showed a visible light-driven hydrogen generation rate of 429 µmol h -1 , which was 25 times higher than that of pure CdS. Ultraviolet photoelectron spectroscopy (UPS), Mott–Schottky plots and DTF calculations indicate the photogenerated electrons prefer to transfer to the cocatalyst CZS driven by built-in electric field through fast electron transfer channel. The rapid transfer of carriers to the active site of hydrogen evolution inhibits the recombination of carriers and improves the utilization rate of carriers which greatly improves the hydrogen evolution activity of CdS.
The rapid recombination rate of carriers and the lack of active sites are the key factors limiting the activity of CdS photocatalytic H 2 generation. Gradient P-doping could form a built-in electric field to promote carrier migration to the surface. In this work, the contact between cocatalyst CoZnS x (CZS) and P-doped CdS (P-CdS) forms interfacial heterojunction and build fast electron transfer channel which promote the transfer of carriers to cocatalyst and enrichment at the active site of hydrogen evolution. The optimal catalyst showed a visible light-driven hydrogen generation rate of 429 µmol h -1 , which was 25 times higher than that of pure CdS. Ultraviolet photoelectron spectroscopy (UPS), Mott–Schottky plots and DTF calculations indicate the photogenerated electrons prefer to transfer to the cocatalyst CZS driven by built-in electric field through fast electron transfer channel. The rapid transfer of carriers to the active site of hydrogen evolution inhibits the recombination of carriers and improves the utilization rate of carriers which greatly improves the hydrogen evolution activity of CdS.
关键词：
Model-free adaptive control;Data-driven control;Unmanned surface vehicle;Sliding mode control;Heading control
摘要：
The marine environment is changeable and complex, and natural factors such as waves and wind present challenges to the heading control of Unmanned Surface Vehicles (USVs). This paper centers on the heading control problem of USVs under the influence of the marine environment. An Improved Compact Format Dynamic Linearization Based Model-free Adaptive Sliding Mode Control (ICFDL-MFASMC) method is introduced. The adaptive control law of this method takes both velocity error and position error into consideration. Compared with the data-driven compact format model-free adaptive control (MFAC) method which only includes position error, the response speed is improved and the dynamic response process is smoother. Moreover, the discrete Sliding Mode Control (SMC) method is incorporated to enhance the robustness of the heading control system of USVs and reduce the influence of measured disturbances. To avoid the problem of excessive input of SMC in the heading control system of USVs, the parameter estimation error is added as an extra corrective factor. Furthermore, the stability of the proposed closed-loop ICFDL-MFASMC system is proven theoretically. Finally, the advantages of the proposed method in tracking accuracy and anti-interference capability are verified through comparative simulation experiments.
The marine environment is changeable and complex, and natural factors such as waves and wind present challenges to the heading control of Unmanned Surface Vehicles (USVs). This paper centers on the heading control problem of USVs under the influence of the marine environment. An Improved Compact Format Dynamic Linearization Based Model-free Adaptive Sliding Mode Control (ICFDL-MFASMC) method is introduced. The adaptive control law of this method takes both velocity error and position error into consideration. Compared with the data-driven compact format model-free adaptive control (MFAC) method which only includes position error, the response speed is improved and the dynamic response process is smoother. Moreover, the discrete Sliding Mode Control (SMC) method is incorporated to enhance the robustness of the heading control system of USVs and reduce the influence of measured disturbances. To avoid the problem of excessive input of SMC in the heading control system of USVs, the parameter estimation error is added as an extra corrective factor. Furthermore, the stability of the proposed closed-loop ICFDL-MFASMC system is proven theoretically. Finally, the advantages of the proposed method in tracking accuracy and anti-interference capability are verified through comparative simulation experiments.
摘要：
This study focuses on the detection requirements for breakdown products (HF, SO(2), SO(2)F(2), and CF(4)) produced by the partial discharge of the innovative, environmentally friendly insulating gas CF(3)SO(2)F in high-voltage electrical apparatus. A doping modification technique utilizing a metal oxide (ZnO/TiO(2)) based on two-dimensional MoTe(2) is proposed. The method for enhancing gas sensing is being systematically investigated through multiscale theoretical computations. Molecular dynamics simulations are initially utilized to ascertain the structural stability of the doped systems, confirming that ZnO-MoTe(2) and TiO(2)-MoTe(2) maintain robust structural integrity in a thermodynamic equilibrium condition. Subsequently, density functional theory is utilized to compare and analyze the adsorption behaviors of the intrinsic MoTe(2) and its doped systems toward the four characteristic decomposition products. The findings indicate that doping ZnO markedly improves MoTe(2)'s adsorption capacity for SO(2). Adsorption configuration analysis reveals that doping strengthens the interactions between the material surface and SO(2) molecules. Electronic structure estimates suggest significant charge transfer and band structure modifications throughout the adsorption process. The density of electronic states in the ZnO-MoTe(2) combination exhibits significant variation, indicating that the chemical adsorption of SO(2) is predominant. Additionally, the TiO(2)-doped system shows a selective adsorption tendency for acidic gases such as HF. The comparison of total electron density and differential charge density distributions demonstrates that the charge redistribution at the interface, generated by doping, is the crucial factor enhancing gas adsorption performance. This work reveals, at the atomic scale, the mechanism by which metal oxide doping modulates the gas-sensing properties of MoTe(2), providing a theoretical foundation for developing highly selective gas sensors for detecting CF(3)SO(2)F decomposition products.
关键词：
GPd(2)/MoS2;Interfacial regulation;Adsorption;Multiphysics field
摘要：
As a promising eco-friendly alternative to SF 6 , heptafluoroisobutyronitrile (C 4 F 7 N) faces critical challenges in practical applications within high-voltage insulation equipment, primarily due to the difficulties in real-time monitoring and treatment of its decomposition byproducts. This study presents a comprehensive investigation into the specific gas sensing mechanisms for C 4 F 7 N decomposition products through the construction of a vertically aligned G Pd2 /MoS 2 heterojunction (G Pd2 /MoS 2 ) under multiphysical field coupling. The novel Nanocomposites based on G Pd2 /MoS 2 heterostructure demonstrates remarkable anisotropic carrier transport behavior (I ver /I hor = 1.65 at U = 0.6 V) while maintaining exceptional thermal stability (T > 500 K) and strong electric field tolerance (E max = 0.5 V/Å). Furthermore, this architecture exhibits promising potential for applications in field-effect transistors, high-frequency nanoelectronic devices, photocatalysis, and thermoelectric conversion. Gas sensing characterization reveals that the sensor achieves a remarkable current response rate of 48 % towards CO under a bias voltage of 0.9 V. The dynamic response characteristics can be precisely modulated through a synergistic multiphysical field regulation strategy. Under micro-aqueous environments (nH 2 O = 0–5), the theoretical desorption time can be significantly reduced through the reduction of desorption activation energy (ΔE a = 0.17 eV). Biaxial strain (ε max = 4.38 %) induces bandgap modulation (ΔE g = 0.023 eV), optimizing the density of sensitive interface states. The synergistic effect of external electric field (↓E z = 0.5 V/Å) and additional net charge (1e) significantly enhances the CO adsorption energy (E ads = −2.54 eV). This study provides interfacial engineering design for developing adaptive gas sensors under complex working conditions, offers novel insights for hazardous gas treatment and the development of new adsorption materials, and establishes a new paradigm for understanding multiphysical field regulation mechanisms.
As a promising eco-friendly alternative to SF 6 , heptafluoroisobutyronitrile (C 4 F 7 N) faces critical challenges in practical applications within high-voltage insulation equipment, primarily due to the difficulties in real-time monitoring and treatment of its decomposition byproducts. This study presents a comprehensive investigation into the specific gas sensing mechanisms for C 4 F 7 N decomposition products through the construction of a vertically aligned G Pd2 /MoS 2 heterojunction (G Pd2 /MoS 2 ) under multiphysical field coupling. The novel Nanocomposites based on G Pd2 /MoS 2 heterostructure demonstrates remarkable anisotropic carrier transport behavior (I ver /I hor = 1.65 at U = 0.6 V) while maintaining exceptional thermal stability (T > 500 K) and strong electric field tolerance (E max = 0.5 V/Å). Furthermore, this architecture exhibits promising potential for applications in field-effect transistors, high-frequency nanoelectronic devices, photocatalysis, and thermoelectric conversion. Gas sensing characterization reveals that the sensor achieves a remarkable current response rate of 48 % towards CO under a bias voltage of 0.9 V. The dynamic response characteristics can be precisely modulated through a synergistic multiphysical field regulation strategy. Under micro-aqueous environments (nH 2 O = 0–5), the theoretical desorption time can be significantly reduced through the reduction of desorption activation energy (ΔE a = 0.17 eV). Biaxial strain (ε max = 4.38 %) induces bandgap modulation (ΔE g = 0.023 eV), optimizing the density of sensitive interface states. The synergistic effect of external electric field (↓E z = 0.5 V/Å) and additional net charge (1e) significantly enhances the CO adsorption energy (E ads = −2.54 eV). This study provides interfacial engineering design for developing adaptive gas sensors under complex working conditions, offers novel insights for hazardous gas treatment and the development of new adsorption materials, and establishes a new paradigm for understanding multiphysical field regulation mechanisms.
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：
MoTe2;Lung cancer VOCs;gas-sensitive material;DFT
摘要：
Lung cancer has become one of the deadliest and most prevalent cancers worldwide, and the use of gas sensors to detect volatile organic compounds (VOCs) in the exhaled breath of lung cancer patients is gaining increasing popularity. Compared with traditional medical diagnostic methods, this method is cost-effective and less invasive. During our experiments, we employ density functional theory to explore how transition metal (Cu, Pd, Pt)-doped MoTe₂ single-molecule membranes respond to VOCs commonly found in the exhalation gas of patients with lung cancer in the early stages of the disease. All three modified systems exhibited excellent thermal stability, and the sorption of VOCs is significantly enhanced compared to the pristine MoTe₂, ensuring effective desorption and sensing performance at elevated temperatures. Moreover, the changes in the band gap before and after adsorption are notably distinct, indicating strong gas sensitivity. Among the doped structures, MoTe₂-Cu shows the highest adsorption capacity for C₅H₈, C₃H₆O, and C₃H₈O, accompanied by the largest change in the band gap. Due to the varying sensitivities of the three lung cancer biomarker sensors to different gases, cross-sensitivity can be minimised, highlighting the potential for qualitative analysis of VOC gas mixtures. This offers new insights and methods for the early detection and prevention of lung cancer.
Lung cancer has become one of the deadliest and most prevalent cancers worldwide, and the use of gas sensors to detect volatile organic compounds (VOCs) in the exhaled breath of lung cancer patients is gaining increasing popularity. Compared with traditional medical diagnostic methods, this method is cost-effective and less invasive. During our experiments, we employ density functional theory to explore how transition metal (Cu, Pd, Pt)-doped MoTe₂ single-molecule membranes respond to VOCs commonly found in the exhalation gas of patients with lung cancer in the early stages of the disease. All three modified systems exhibited excellent thermal stability, and the sorption of VOCs is significantly enhanced compared to the pristine MoTe₂, ensuring effective desorption and sensing performance at elevated temperatures. Moreover, the changes in the band gap before and after adsorption are notably distinct, indicating strong gas sensitivity. Among the doped structures, MoTe₂-Cu shows the highest adsorption capacity for C₅H₈, C₃H₆O, and C₃H₈O, accompanied by the largest change in the band gap. Due to the varying sensitivities of the three lung cancer biomarker sensors to different gases, cross-sensitivity can be minimised, highlighting the potential for qualitative analysis of VOC gas mixtures. This offers new insights and methods for the early detection and prevention of lung cancer.
摘要：
针对在学校、公园等具有复杂飞行背景的公共场所中无人机尺寸较小所导致的检测精度低以及错检、漏检问题，提出了一种基于改进YOLOv8n的小目标无人机检测方法。首先，采集不同飞行背景中的无人机图像构建实验数据集；其次，重新设计了多尺度特征融合网络，引入TFE模块和SSFF模块对颈部网络的多尺度特征融合方法进行改进，并在此基础上添加小目标检测层，提升网络抗背景干扰的能力以及对小目标的检测精度；最后，将Inner-CIoU作为改进模型的损失函数，提升模型检测精度和收敛速度。在自建的无人机数据集Anti-Drone上的实验结果表明，所提方法与YOLOv5s、YOLOv7-tiny、YOLOv7和YOLOv8s相比，mAP50值分别提升了0.8、15.5、9.8和5.2个百分点，验证了所提方法对复杂背景中小目标无人机检测的有效性。 您的浏览器不支持 audio 元素。 AI语音播报 To address the issues of low detection accuracy and false or missed detection of small-sized drones in complex flying environments such as schools and parks, a small target drone detection method based on the improved YOLOv8n is proposed. Firstly, drone images in different flight backgrounds are collected to build an experimental dataset. Secondly, the multi-scale feature fusion network is redesigned, introducing TPE and SSFF modules to improve the multi-scale feature fusion method of the neck network, and a small target detection layer is added to enhance the network ability to resist background interference and the detection accuracy for small targets. Finally, Inner-CIoU is used as the loss function of the improved model to enhance the model detection accuracy and convergence speed. Experimental results on the self-built drone Anti-Drone dataset show that compared with YOLOv5s, YOLOv7-tiny, YOLOv7, and YOLOv8s algorithms, the proposed method increases the value of mAP50 by 0.8, 15.5, 9.8, and 5.2 percentage points respectively, which demonstrates the effectiveness of the improved method in detecting small-scale drones in complex backgrounds.
