Laser induced breakdown spectroscopy (LIBS) is promising for on site assessment of high voltage insulator contamination, yet quantitative accuracy is limited by spectral fluctuations and complex field conditions. This paper presents a dual modality fusion approach that combines LIBS spectra with transient plasma optical responses captured by a low bandwidth dynamic vision sensor (DVS). The DVS event stream is reconstructed into plasma images and encoded by a 2D CNN, while spectra are encoded by a 1D CNN. “A fusion module with channel attention then adaptively weights the two embeddings, and an MLP jointly regresses equivalent salt deposit density (ESDD) and non soluble deposit density (NSDD). Experiments on 20 artificial contamination levels show that CADBF-Net achieves a test $\mathbf{R}^{\mathbf{2}}$ of $\mathbf{0. 9 9 5 4}$ with an RMSEP of $\mathbf{0. 0 0 8 9}$ for ESDD. For NSDD, the test $\mathbf{R}^{\mathbf{2}}$ reaches 0.9958 with an RMSEP of 0.0102, outperforming representative single modality regressors. The proposed model improves the precision and stability of LIBS for on site pollution assessment and supports contamination monitoring of power equipment.

This study aims to address the problems of performance variability and low yield in flash-sintered ZnO ceramics by systematically comparing three post-treatment methods:heat treatment alone, cold isostatic pressing, and hot isostatic pressing. Particular attention was given to the effect of HIP temperature (900 degrees C, 1000 degrees C, and 1100 degrees C) on material properties. The results show that heat treatment alone and cold isostatic pressing have minimal effects on improving the density and hardness of ZnO ceramics. In contrast, hot isostatic pressing significantly enhances material performance, achieving a maximum relative density of 99.6% and an increase in Vickers hardness of 0.5-0.9 GPa, with particularly pronounced improvements in samples with initially low density. Temperature optimization indicates that 1000 degrees C is the preferable hot isostatic pressing temperature, under which the ceramic exhibits moderate grain size and optimal comprehensive mechanical and electrical properties. This study confirms that hot isostatic pressing is an effective post-treatment method for enhancing the consistency and performance stability of flash-sintered ZnO ceramics, providing important insights for its engineering applications.

Electric energy metering cabinets serve as critical nodes in power grid operations, providing essential protection for key components in distribution networks. Under environmental stressors, the non-metallic casings of electric energy metering cabinets are susceptible to aging-induced performance degradation, which may result in electrical safety hazards. However, rapid and precise methods for evaluating the performance of these non-metallic casings are still lacking. Laser-Induced Breakdown Spectroscopy (LIBS), capable of rapid multi-element detection with non-contact analytical advantages, was employed in this study. Thermal aging experiments were conducted to investigate the performance degradation mechanisms of sheet molding compound (SMC)-a representative non-metallic cabinet material. The research analyzed time-dependent trends in material performance and microstructural evolution during aging. By integrating LIBS with multi-analytical techniques, this study further explored the feasibility of quantitatively evaluating the bending strength of thermally aged SMC, which has rarely been reported in previous studies. Based on LIBS spectral data, bending strength characterization revealed its attenuation patterns with aging duration. The relationships between bending strength and plasma temperature, as well as the characteristic line intensity ratios of K, Al, and Ca, were systematically examined. A multivariate linear regression model incorporating these key variables was subsequently developed, yielding a high coefficient of determination (R2 = 0.9657) between the predicted and measured bending strength values. This model represents a promising initial step, but further validation with a larger dataset is necessary to enhance its reliability and generalizability.

Power distribution switchgear is critical for control and protection in electrical networks, with its operational status directly affecting system safety and reliability. Mechanical parameters provide essential indicators for evaluating operational performance and health conditions of distribution switches. Previous research has demonstrated that Dynamic Vision Sensing (DVS) technology enables non-contact dynamic measurement of key mechanisms in high-voltage circuit breakers under healthy operating conditions. However, existing studies have primarily focused on factory testing and normal operations, leaving its effectiveness under fault conditions uncertain. This work pioneers the application of event-based vision technology to fault simulation experiments in distribution switchgear. We developed a multi-condition mechanical measurement platform for 10 kV SF6 fully-insulated distribution switches, designed to characterize and detect two typical faults: armature sticking and mechanism seizure. Algorithmically, the Optical Flow via Event-to-Image Reconstruction (OFEIR) framework is enhanced by integrating SPADE-E2VID, improving image detail restoration and temporal consistency. In parallel, we propose the Direct Event-to-Flow Estimation (DEFE) model. This end-to-end neural network regresses dense optical flow fields directly from event data, bypassing intermediate image reconstruction. Both algorithms achieve high measurement accuracy for opening and closing times, with errors below 2%. OFEIR preserves detailed motion features for comprehensive analysis, while DEFE improves computational speed and resource efficiency for real-time performance. This study demonstrates that event vision technology can reliably capture high-speed dynamics under fault conditions, offering a new approach for mechanical fault analysis and online monitoring. It also advances DVS applications from reconstruction-based analysis toward end-to-end dynamic perception in power equipment detection.

ABSTRACT Lithium manganese iron phosphate (LiMn x Fe 1‐ x PO 4 , LMFP) offers higher energy density than LiFePO 4 but suffers from sluggish kinetics and unfavorable cycling stability caused by Fe/Mn segregation and severe Jahn‐Teller distortion of Mn 3+ . Herein, a precursor‐engineered strategy is proposed using a homogeneous spinel FeMn 2 O 4 precursor synthesized via solution combustion. Molecular‐level chelation and rapid combustion pre‐lock Fe and Mn cations into a uniform solid‐solution framework, enabling the formation of LiMn 0.67 Fe 0.33 PO 4 with highly homogeneous cation distribution and reduced lattice distortion. As a result, the LMFP cathode exhibits markedly reduced polarization (38 mV and 101 mV for Fe and Mn plateaus), enhanced Li + diffusion coefficients (up to 6.75 × 10 −12 cm 2 s −1 ), and excellent rate capability, delivering ∼76 mAh g −1 at 10 C. Superior cycling stability is achieved with 89.2% capacity retention after 1000 cycles at 5 C. This work demonstrates precursor‐level cation organization as an effective route to overcoming the kinetic and stability bottlenecks of manganese‐based olivine cathodes.

Laser-induced breakdown spectroscopy (LIBS) is attractive for rapid elemental quantification of silicone rubber insulators, but surface heterogeneity, matrix effects, and shot-to-shot plasma fluctuations can degrade its calibration performance. To improve the robustness of Al and Si determination, this study proposes a tri-modal chemometric fusion strategy integrating LIBS, dynamic vision sensing (DVS), and laser-induced plasma acoustics (LIPA). Spectral emission, event-reconstructed plasma images, and LIPA signals generated from the same laser–sample interaction were synchronously acquired and used as complementary descriptors of the plasma event. A Plasma-informed Tensor Fusion Boosting Network (PTFB-Net) was developed to fuse the heterogeneous modal features for multivariate regression. Eighteen field-retired silicone rubber insulator samples with XPS-derived sample-level surface reference values were analyzed, yielding 3600 paired tri-modal records for model development and validation. Compared with the best single-modal models, PTFB-Net reduced the RMSEP by 80.3% for Si and 97.1% for Al. Compared with the best bimodal models, the corresponding reductions were 78.1% and 89.9%, respectively. The final test-set Rp2, RMSEP, and MAEP values were 0.9945, 0.0836 wt%, and 0.0193 wt% for Si, and 0.9992, 0.0084 wt%, and 0.0021 wt% for Al. Sample-level LOOCV yielded R2 values of 0.9739 for Si and 0.9808 for Al, while 20 repetitions of three-fold grouped cross-validation gave R2 values of 0.9560±0.0082 and 0.9754±0.0113, respectively. These results demonstrate that plasma-event-informed tri-modal fusion can improve the quantitative reliability of LIBS for elemental analysis of heterogeneous aged silicone rubber surfaces.

Alongside conventional salt contamination, metallic particulate deposits on outdoor high-voltage insulators can aggravate pollution-induced flashover, demanding rapid in situ quantification of trace metals. Laser-induced breakdown spectroscopy (LIBS) is well suited for the rapid, in situ multi-element assessment of insulator contamination; however, its quantitative accuracy is often limited by plasma fluctuations and matrix effects. Prior works on insulator contamination reported only moderate calibration performance for several trace metals (e.g., Ni : R2 = 0.773, Cu : R2 = 0.747, and Mn : R2 = 0.615). To address these limitations, a dynamic vision sensor (DVS) is integrated with LIBS to record plasma emission dynamics and generate event-reconstructed plasma images that complement the spectra. Simulated contamination samples were prepared with Ca contents of 10.59-22.47 wt% and trace Fe, Cu and Zn contents of 0.10-0.32, 0.05-0.37 and 0.32-1.24 wt%, respectively, yielding 2000 paired LIBS-DVS measurements. A sample-group-wise leave-one-out cross-validation strategy was adopted to evaluate the generalization to unseen sample groups. GFMT Net combines a transformer spectral encoder, a 2D-CNN image branch, gated feature fusion and multi-task regression for the simultaneous prediction of Ca, Fe, Cu and Zn. Under LOOCV, GFMT Net achieved R2 values of 0.9736, 0.9649, 0.9712 and 0.9741 with RMSEs of 0.5440, 0.0111, 0.0159 and 0.0402 wt%, respectively. Compared with Transformer-LIBS, the best LIBS-only baseline, GFMT Net decreased the RMSEs by 63.28%, 58.10%, 63.41% and 61.24% for Ca, Fe, Cu and Zn, respectively. Ablation and interpretability analyses confirmed that DVS images provided complementary plasma-state information to LIBS spectra. These results demonstrate that event-assisted gated fusion improves robust trace metal quantification on power insulation equipment.

Lightning is a brief discharge event that occurs in the atmosphere, characterized by tremendous energy and electromagnetic effects. Lightning current is a critical parameter holding great importance for both lightning physics research and protection design. However, direct field measurements of lightning current face considerable challenges. Therefore, inversion based on optical signals has become a primary method for obtaining lightning current information: by measuring the optical radiation from the discharge channel and establishing a luminosity-current relationship, the transient current process can be estimated indirectly. Existing optical monitoring methods encounter issues such as large data volumes, high power consumption, limited interference resistance, and restricted monitoring coverage. Dynamic Vision Sensing (DVS), as a neuromorphic imaging technology, offers advantages such as ultra-high dynamic range, low data redundancy, and high temporal resolution, demonstrating potential for monitoring fast transient events. This study introduces DVS technology to the impulse-current discharge experiments. An impulse current generator was used to produce decaying oscillatory impulse currents with slow-rising fronts, applied to a 5 mm point-to-point discharge gap to trigger gas discharge. Simultaneously, dynamic vision data and discharge channel current were measured to investigate the correlation between impulse currents and event data. The DVS generates ON/OFF events based on changes in light intensity. The results reveal that the cumulative value of ON events exhibits a positive correlation with the peak current, with a Pearson correlation coefficient greater than 0.92. Conversely, the cumulative values of OFF events and ALL events show a negative correlation with the peak current, with Pearson correlation coefficients greater than 0.95, and quadratic correlation coefficients greater than 0.97 in both cases. More importantly, this study derives a mathematical relationship between impulse currents and event counts, achieving preliminary inversion of impulse currents. This work marks the first application of dynamic vision sensing technology to investigate the inversion of large impulse currents via event data, providing a novel approach for monitoring impulse currents and even lightning currents.

Lithium manganese iron phosphate (LiMnxFe1- xPO4, LMFP) offers higher energy density than LiFePO4 but suffers from sluggish kinetics and unfavorable cycling stability caused by Fe/Mn segregation and severe Jahn-Teller distortion of Mn3+. Herein, a precursor-engineered strategy is proposed using a homogeneous spinel FeMn2O4 precursor synthesized via solution combustion. Molecular-level chelation and rapid combustion pre-lock Fe and Mn cations into a uniform solid-solution framework, enabling the formation of LiMn0.67Fe0.33PO4 with highly homogeneous cation distribution and reduced lattice distortion. As a result, the LMFP cathode exhibits markedly reduced polarization (38 mV and 101 mV for Fe and Mn plateaus), enhanced Li+ diffusion coefficients (up to 6.75 & times; 10-12 cm2 s-1), and excellent rate capability, delivering similar to 76 mAh g-1 at 10 C. Superior cycling stability is achieved with 89.2% capacity retention after 1000 cycles at 5 C. This work demonstrates precursor-level cation organization as an effective route to overcoming the kinetic and stability bottlenecks of manganese-based olivine cathodes.

Laser-induced breakdown spectroscopy (LIBS) has broad application potential, yet its analytical accuracy is often limited by poor spectral stability. Since plasma optical signals directly reflect plasma fluctuations, they offer a promising basis for spectral correction. In a novel approach, we introduce a neuromorphic dynamic vision sensor (DVS) to capture plasma dynamics with microsecond temporal resolution. The DVS provides a 120 dB dynamic range and a low data rate (∼10 MB/s), enabling acquisition of plasma optical signals over a wide range of conditions. We further propose an event-enhanced spectroscopy correction network (EESCN), which employs a dual-stream convolutional neural network (CNN) to extract key features from spectra and plasma images, respectively. A multihead attention module then performs cross-modal fusion by dynamically weighting spectral and image features to predict and correct signal fluctuations. To emulate challenging conditions, we introduced laser energy fluctuations and selected spectral lines affected by self-absorption. EESCN substantially suppressed spectral fluctuations arising from self-absorption and laser energy fluctuations for C(I) 493.202 nm and Mn(I) 403.076 nm in carbon steel, and for Cu(I) 327.395 nm and Zn(I) 328.233 nm in copper alloys, reducing the mean relative standard deviations by 70.52%, 79.33%, 80.76%, and 72.09%, respectively. Calibration curves constructed from the corrected spectra all achieved R2 values above 0.99, markedly outperforming the original spectra, normalization, and other correction methods. By integrating a low-cost, high-speed DVS with a cross-modal fusion model, this work provides a practical and powerful solution for mitigating spectral instability in LIBS and supports robust on-site analytical applications.

Sodium Superionic Conductor (NASICON) cathodes possess a favorable Na+ diffusion pathway and structural stability, making them promising cathode materials for sodium-ion batteries. However, upon electrochemical cycling V-based and Mn-based NASICON-type cathodes still hold apparent drawbacks of lattice distortion and structural instability. In this work, a strategy of negative enthalpy doping has been developed to address these issues. Na3.25VMn0.5Ti0.25Al0.25(PO4)(3) (NVMTAP) is proposed and prepared through multi-element doping, which fully utilizes the reversible activation of the high-voltage platforms of V4+/V5+ and Mn3+/Mn4+. Moreover, excellent lattice structure stability with a cell volume variation of only 1.74% is achieved. The as-designed NVMTAP can deliver a specific capacity of 136.3 mAh g(-1) at 0.1 C with an energy density of 430 Wh kg(-1) and a high-capacity retention of 74.8% over 4000 cycles under an ultra-high rate of 30 C (4.2 A g(-1)). Furthermore, the cathode also demonstrates excellent low-temperature performance, with a capacity retention of 93.5% after 1000 cycles at 15 C and -20 degrees C. This research provides a beneficial reference for enhancing the energy density of NASICON-type electrode materials and simultaneously realizing robust structural stability.

Laser-induced breakdown spectroscopy (LIBS) has attracted considerable research interest and found wide application across diverse fields. However, LIBS signals often suffer from poor stability due to several factors, most notably the matrix effect, which remains a major obstacle to achieving accurate quantitative analysis. Existing methods are frequently ineffective, complex, or costly. To address these challenges, this study proposes a novel and cost-effective method for high-precision spectral correction. This study utilized event data from a dynamic vision sensor (DVS) to extract plasma features, specifically the number of events and plasma area, which characterize the plasma temperature and total particle number density, respectively. Based on these features, the DVS-T1 correction model was developed and applied to carbon steel and brass samples. The calibration curves obtained after correction for the Fe I 355.851 nm, Mn I 403.076 nm, Cu I 327.396 nm, and Zn I 328.233 nm lines achieved R2 values of 0.994, 0.999, 0.995, and 0.999, respectively, surpassing those of the original data and spectral normalization. The mean relative standard deviation of the corrected signals decreased by 82.7 %, 81.3 %, 79.4 %, and 32.9 %, respectively, compared to the original data and by 77.8 %, 68.1 %, 78.1 %, and 25.8 %, respectively, compared to data with spectral normalization. Leave-one-out cross-validation demonstrated a significant reduction in the absolute relative error, mean absolute error, and root mean square error. The application of DVS-extracted plasma parameters in the DVS-T1 model significantly reduces signal fluctuations and enhances analytical accuracy. This low-cost, efficient approach provides new insights for LIBS development and application.

Aiming at the urgent demand for multifunctional materials integrating thermal protection, stealth performance and load-bearing capacity in the field of next-generation hypersonic aircraft, this study develops a facile, template-free self-expansion foaming route to fabricate carbon fiber-reinforced porous (Hf, Zr, Ti)C medium-entropy ceramics, denoted as Cf/(Hf, Zr, Ti)C (HZT). The influence of carbon fiber content (0–10 wt%) on the microstructure, thermophysical properties, mechanical performance, and electromagnetic wave absorption characteristics is systematically investigated. The synergistic mechanism among lattice distortion-induced polarization enhancement, porous structure-mediated multiple scattering, and carbon fiber-constructed conductive networks is elucidated, which effectively breaks the inherent trade-off between thermal insulation, mechanical strength, and microwave absorption in conventional porous ultra-high temperature ceramics. The sample with 6 wt% carbon fiber (HZT-06) possesses a porosity of 70.2% and a low density of 0.67 g/cm3. It exhibits a room-temperature thermal conductivity of 0.253 W·m-1·K-1, exceptional broadband microwave absorption with a minimum reflection loss (RLmin) of −59.69 dB and an effective absorption bandwidth (EAB, RL≤−10 dB) of 6.32 GHz at a matching thickness of 2.3 mm, and a high compressive strength of 49.62 MPa. This work realizes the multifunctional integration of low thermal conductivity, high load-bearing capacity and strong broadband microwave absorption at room temperature, and provides promising design strategies and fundamental data support for the development of advanced multifunctional thermal protection materials for hypersonic vehicles.

Abstract The closing resistor is a key component for suppressing closing inrush current and overvoltage in extra-/ultra-high voltage power grids. To address the issues of high energy consumption, long production cycles, and difficulties in performance control in existing closing resistor manufacturing, this study employed a room-temperature flash sintering technique that requires no external heating source to successfully prepare carbon-ceramic composite closing resistor materials. Using a stacked carbon electrode configuration, rapid densification of green bodies was achieved within 3 min by leveraging the local discharge and plasma effects of graphite felt. Experimental results show that the extremely fast heating rate of the flash sintering process effectively suppressed grain growth, leading to the formation of a dense bulk material (density 2.803 g/cm 3 ) with a fine-grained structure. The obtained samples exhibited a resistivity of 4.467 Ω·cm and a temperature coefficient of resistance of –0.032 %·°C −1 , meeting the basic requirements for closing resistors. This study confirms the feasibility of flash sintering for manufacturing closing resistors and provides a new technical pathway for low-carbon and high-efficiency production.

Line voltage measurement can be used for status monitoring of power grids, fault diagnosis of lines and other scenarios. Compared with traditional contact measurement methods using transformers, non-contact voltage measurement (NCVM) technology has many advantages such as small size, non-intrusive installation, low cost, and low energy consumption. The existing NCVMs, like D-dot voltage sensor, optical voltage sensor etc., exhibited high measurement performance. However, their measurement accuracy are easily affected by sensor position, so the calibration have to be performed. In this study, we proposed a high-precision non-contact voltage sensor based on injected dual-frequency signals. The manuscript introduces the principle of non-contact voltage sensor based on coupling capacitance and designs an anti-interference algorithm without influence of sensor position. Then, the hardware structure, self-power supply module, and prototype of the voltage sensor are produced. We performed to study the measurement accuracy in the laboratory and power distribution room. The experimental results showed that the measurement accuracy error is less than 1 %, and the sensor has high measurement accuracy, high linearity, and high measurement stability.

RGB-Event tracking has become a promising trend in visual object tracking to leverage the complementary strengths of both RGB images and dynamic spike events for improved performance. However, existing artificial neural networks (ANNs) struggle to fully exploit the sparse and asynchronous nature of event streams. Recent efforts toward hybrid architectures combining ANNs and spiking neural networks (SNNs) have emerged as a promising solution in RGB-Event perception, yet effectively fusing features across heterogeneous paradigms remains a challenge. In this work, we propose ISTASTrack, the first transformer-based ANN-SNN hybrid Tracker equipped with ISTA adapters for RGB-Event tracking. The two-branch model employs a vision transformer to extract spatial context from RGB inputs and a spiking transformer to capture spatio-temporal dynamics from event streams. To bridge the modality and paradigm gap between ANN and SNN features, we systematically design an ISTA adapter for bidirectional feature interaction between the two branches. The ISTA adapter is derived from the sparse representation theory by unfolding the iterative shrinkage-thresholding algorithm. Additionally, we incorporate a temporal downsampling attention module within the adapter to align multi-step SNN features with single-step ANN features in the latent space. Experimental results on RGB-Event tracking benchmarks, such as FE240hz, VisEvent, COESOT, and FELT, have demonstrated that ISTASTrack achieves state-of-the-art performance while maintaining high energy efficiency. This work highlights the effectiveness and practicality of hybrid ANN-SNN designs for robust visual tracking. The code is publicly available at https://github.com/lsying009/ISTASTrack.git.

Using a composite layered carbon electrode structure, this study achieved room-temperature flash sintering of disc-shaped SiC ceramics without sintering aids. Under a DC voltage of 100 V, the developed method enabled the preparation of sintered SiC discs (20 mm diameter) in 180 s with a relative density of 94.13%. Despite exhibiting a larger grain size relative to conventionally sintered counterparts, the flash-sintered samples developed well-defined grain boundaries. These samples demonstrated enhanced electrical performance, including a voltage gradient of 94.4 V/mm and a nonlinear coefficient of 2.0, along with improved mechanical properties such as a Vickers hardness of 0.674 GPa and a fracture toughness of 1.403 MPa·m0.5 compared to conventionally sintered counterparts. Not only did flash sintering reduce the sintering time from several hours to minutes, significantly lowering energy consumption and equipment requirements, but it also achieved a dense microstructure and robust macroscopic properties. Moreover, the sintering device and method employed in this study overcame the limitations of existing flash sintering techniques regarding the shape of SiC ceramic green bodies, providing a viable strategy for preparing high-performance non-oxide ceramics with adaptable geometries under energy-efficient conditions.

Porous ZnO ceramics with tunable pore structures were prepared in air at room-temperature via flash sintering using the pore-former method in conjunction with a flash sintering device based on a carbon electrode structure. By employing various amounts of basic zinc carbonate as the pore-former, ceramics with adjustable porosity were obtained. The results showed that as the content of basic zinc carbonate increased, both the sample porosity and average pore size increased significantly; at high contents, millimeter-scale macropores and well-developed mesoporous structures were achieved. Flash sintering enabled rapid densification within a very short time, demonstrating high efficiency and energy-saving advantages. Mechanical property tests indicated that increased porosity led to decreases in Vickers hardness and fracture toughness. This study applies the room-temperature flash sintering technology to porous ceramic materials, expanding the application scope of the room-temperature flash sintering technology and providing a more efficient and energy-saving new method for the preparation of porous ceramic materials.

Field-assisted sintering technology has revolutionized material processing by integrating temperature, mechanical, electrical, and magnetic fields to achieve unprecedented densification efficiency and microstructural control. Recent advances in techniques such as hot oscillatory pressing, cold sintering, high/ultra-high pressure sintering, spark plasma sintering, ultrafast high-temperature sintering, and flash sintering have enabled the fabrication of previously unattainable materials, including ultrafine-grained ceramics, nanostructured composites, and functionally graded materials. These materials possess exceptional performances under extreme conditions, expanding applications in aerospace, electronics, energy, and biomedicine. However, the rapid development of these methods has exposed limitations in conventional sintering theory, particularly in describing mass transport and interface evolution under multi-physics coupling. This review systematically examines representative field-assisted sintering technologies and discusses their principles, equipment configurations, and application cases. By analyzing current challenges and opportunities, we aim to bridge fundamental understanding with industrial implementation, providing insights for the design and fabrication of next-generation high-performance materials.