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The aging phenomenon is commonly observed in quantum-dot light emitting diodes (QLEDs), involving complex chemical or physical processes. Resolving the underlying mechanism of these aging issues is crucial to deliver reliable electroluminescent devices in future display applications. Here, we report a reversible positive aging phenomenon that the device brightness and efficiency significantly improve after device operation, but recover to initial states after long-time storage or mild heat treatment, which can be termed as warming-up effects. Steady and transient equivalent circuit analysis suggest that the radiative recombination current dramatically increases but electron leakage from the quantum dots (QDs) to hole transport layer becomes more accessible during the warming-up process. Further analysis discloses that the notable enhancement of device efficiency can be ascribed to the filling of shell traps in gradient alloyed QDs. This work reveals a distinct positive aging phenomenon featured with reversibility, and further guidelines would be provided to achieve stable QLED devices in real display applications. [ABSTRACT FROM AUTHOR]

Rapid advancements in electric vehicle (EV) technology have highlighted the importance of lithium-ion (Li) batteries. These batteries are essential for safety and reliability. Battery data show non-stationarity and complex dynamics, presenting challenges for current monitoring and prediction methods. These methods often fail to manage the variability seen in real-world environments. To address these challenges, we propose a Transformer model with a wavelet transform dynamic attention mechanism (WADT). The dynamic attention mechanism uses wavelet transform. It focuses adaptively on the most informative parts of the battery data to enhance the anomaly detection accuracy. We also developed a deep learning model with an improved Transformer architecture. This architecture is tailored for the complex dynamics of battery data time series. The model accounts for temporal dependencies and adapts to non-stationary behavior. Experiments on public battery datasets show our approach's effectiveness. Our model significantly outperforms existing technologies with an accuracy of 0.89 and an AUC score of 0.88. These results validate our method's innovation and effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes an electrostatic-piezoelectric-electromagnetic hybrid vibrational power generator with different frequency broadening schemes. Both the nonlinear frequency broadening mechanisms and the synergized effect of the electrostatic-piezoelectric-electromagnetic hybrid structures are investigated. The structure and performance of the composite generator are optimized to improve the response bandwidth and performance. We propose that the electrostatic power generation module and the electromagnetic power generation module be introduced into the cantilever beam to make the multifunctional cantilever beam, realizing small integrated output loss, high output voltage, and high current characteristics. When the external load of the electrostatic power generation module is 10 kΩ, its peak power can reach 3.6 mW; when the external load of the piezoelectric power generation module is 2 kΩ, its peak power is 2.2 mW; and when the external load of the electromagnetic power generation module is 170 Ω, its peak power is 0.735 mW. This means that under the same space utilization, the performance is improved by 90%. Moreover, an energy management circuit (ECM) at the rear end of the device is added, through the energy conditioning circuit, the device can directly export a 3.3 V DC voltage to supply power to most of the sensing equipment. In this paper, the hybrid generator's structure and performance are optimized, and the response bandwidth and performance are improved. In general, the primary advantages of the device in this paper are its larger bandwidth and enhanced performance. [ABSTRACT FROM AUTHOR]