Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
With the rapid development of smart devices for body area networks and smart packaging, there is a significant demand for low-waste and low-impact electronic systems in industries such as healthcare and transportation. We demonstrate that the dielectric material from capacitors in resistor-inductor-capacitor (RLC) wireless, chipless, resonant temperature sensors can be successfully recovered from flexible PCBs, with pristine sensors re-introduced to the tag’s sensor loading. First, we demonstrate that replacing the dielectric in a parallel plate capacitor with a pristine component, with recycled electrodes and sub-miniature-A (SMA) adaptor, results in only a 3% change in broadband capacitance. An identical substitution of the sensing element in an RLC circuit tuned to resonate at 21.0 MHz, with recycled parallel plates, a resistor, and an inductive PCB coil, results in a change of only 7.6% in the resonant frequency of the tag to 19.4 MHz. This work demonstrates the recyclability of chipless tags for temperature sensing for the first time, offering sustainability gains in smart packaging applications, with the potential to be expanded to other sensing tags for pH, humidity, and chemical analytes, towards chipless product passports....
Electronic devices are shrinking, and scanning transmission electron microscopy is essential for the characterization of inoperando nanoscale devices. This paper demonstrates the combined capabilities of 4D-STEM and STEM-EBIC for measuring localized electronic properties (electric field strength, field direction, built-in potential, and minority carrier diffusion length) in an in-operando nanoscale device. Quantitative analysis supported by simulations enables robust interpretation of local electric fields and potential gradients. STEM-EBIC measurements at different thicknesses show a regime where the effective diffusion length of minority carriers is entirely dominated by surface recombination. In situ biasing of a symmetrically doped 4 × 1017 cm− 3 p–n diode shows how 4D-STEM and STEM-EBIC complement each other for localized interpretation of electronic components....
Ensuring the availability and reliability of electronic components or materials during operation necessitates conducting a sampling inspection of their lifespan. This is particularly crucial when comparing equipment or materials deployed in various areas with diverse environments, conditions, and situations. Many studies rely on one-method parameter estimation methods, which fall short in identifying the two explicit parameters of the Weibull distribution. Moreover, inaccurate parameter estimation methods impede the attainment of reliable analysis results. Consequently, this paper introduces three analytical estimation methods to determine the parameters of the Weibull distribution. The accuracy of these methods is evaluated using the mean square error (MSE). Furthermore, we utlized the Kolmogorov–Smirnov and Anderson–Darling test on real datasets, confirming that the data follow a Weibull distribution. Simulation results indicate that the maximum likelihood estimate outperforms the other estimators by minimizing the MSE and yielding optimal parameters. These optimal parameters were then applied to real CCTV datasets, demonstrating a good fit and enabling assessment of CCTV lifespan through the mean time to failure, which is estimated to be 6.6 years. This holds true even when the equipment operates in environments with different conditions and situations....
In response to thermal failure risks in ultra-high voltage (UHV) bushing online monitoring devices and maintenance equipment—caused by high heat generation of electronic components and the intrinsically low thermal conductivity of conventional resin encapsulation materials—this study proposes a novel modification strategy based on flash Joule heating (FJH). Distinct from conventional interface modification methods, the proposed approach enables cross-scale, in situ microsoldering between multi-walled carbon nanotubes (MWCNTs) and carbon fibers (CFs), constructing a multiscale reinforcement network with integrated thermal transport and mechanical load transfer pathways. The transient ultra-high-temperature thermal shock generated by FJH not only effectively removes inert impurities on CF surfaces but also drives carbon structural reconstruction, enabling graphitic-level welding of MWCNTs onto the fiber surface. This micro-welded architecture fundamentally differs from traditional filler dispersion or interface coating strategies, which often suffer from the trade-off between interfacial thermal transport and mechanical bonding. By contrast, the FJH-induced carbon–carbon bonded nodes form a continuous conductive and load-bearing network at the micro–nano scale. Characterizations using scanning electron microscopy (SEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) confirm successful in situ welding of MWCNTs onto CF surfaces. Meanwhile, FJH treatment effectively removes oxygen-containing functional groups and surface impurities. Analysis of carbon bonding evolution indicates that the welding efficiency reaches its maximum at 90 V. Macroscopic performance tests demonstrate that, compared with epoxy resin, the thermal conductivity of the multiscale reinforced system increases by approximately 168%, while the mechanical strength improves by 62.72%. This study provides new theoretical insights and technical pathways for the development of nextgeneration polymer composite materials with both high thermal conductivity and high mechanical strength....
The increasing penetration of renewable energy resources has amplified variability and uncertainty in power systems, reducing the effectiveness of conventional single-period Optimal Power Flow (OPF) strategies. Multi-period AC-OPF offers a more comprehensive framework by incorporating inter-temporal constraints and resource flexibility, but its high computational complexity and strong temporal coupling make large-scale applications challenging, often causing scalability issues and convergence difficulties in conventional solvers. We address these issues with a spatio-temporal deep learning model that combines a Graph Attention Network (GAT) for topology-aware feature learning with a Temporal Convolutional Network (TCN) for multi-period temporal modeling. The proposed model is trained on large-scale 500-bus and 1354-bus systems under both 8-period and 24-period settings, and it achieves robust scalability with consistently high prediction accuracy. Using the model’s predictions, we construct an initial solution and provide it to a conventional OPF solver, which improves convergence performance and demonstrates the model’s effectiveness as an auxiliary tool for complex MP-ACOPF problems....
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