Recently, a type of synthetic highly efficient OLED molecule based on a hybridized local excitation and charge transfer (HLCT) character has received much attention as a potential high-efficiency fluorescent OLED material. In this article, we report the relaxation dynamics of the excited states of cyano-substituted oligo α-phenylenevinylene-1,4-bis(R-cyano-4-diphenylaminostyryl)-2,5-diphenylbenzene (CNDPASDB) with HLCT character using steady-state and time-resolved spectroscopy as well as quantum chemical calculations. The dramatic dependence of the fluorescence quantum yield, radiative and non-radiative rate, as well as the excited state relaxation pathways on solvent polarity reveals that the solvation process controls the energy levels of two closely spaced electronic excited states. By employing femtosecond transient absorption spectra, the gradual transition from the LE state to the intramolecular CT state with an increase in solvent polarity is clearly resolved. In low-polarity solvents the fluorescence of CNDPASDB is mainly emission from the LE state, whereas in high-polarity solvents non-radiative decay from the CT state dominates. And in medium-polarity solvents, because of the relatively weaker solvation-induced stabilization of the CT state, its energy could be equal to or slightly lower than that of the LE state, leading to a smaller driving force for LE → CT interconversion; therefore complete LE → CT interconversion cannot take place. In this situation, LE ↔ CT intercrossed equilibration is established and the equilibrium constant is further estimated to be about 4 according to the obtained kinetics, and the equilibrium population of the CT state is about 80%. DFT/TDDFT analysis also confirmed an efficient intercrossing of LE and CT states with an increase in solvent polarity. It is found that upon increasing the solvent polarity, the hole and electron on a molecule are entirely separated, suggesting a complete CT character. These results provide guidance for understanding the relationship between solvent polarity and the HLCT process, as well as for designing and synthesizing advanced OLED materials.

Human activity recognition (HAR) is becoming increasingly important, especially with the growing number of elderly people living at home. However, most sensors, such as cameras, do not perform well in low-light environments. To address this issue, we designed a HAR system that combines a camera and a millimeter wave radar, taking advantage of each sensor and a fusion algorithm to distinguish between confusing human activities and to improve accuracy in low-light settings. To extract the spatial and temporal features contained in the multisensor fusion data, we designed an improved CNN-LSTM model. In addition, three data fusion algorithms were studied and investigated. Compared to camera data in low-light environments, the fusion data significantly improved the HAR accuracy by at least 26.68%, 19.87%, and 21.92% under the data level fusion algorithm, feature level fusion algorithm, and decision level fusion algorithm, respectively. Moreover, the data level fusion algorithm also resulted in a reduction of the best misclassification rate to 2%~6%. These findings suggest that the proposed system has the potential to enhance the accuracy of HAR in low-light environments and to decrease human activity misclassification rates.

Excited-state intramolecular charge transfer dynanmics and coherent oscillation of ligand-protected rod shaped Au ₂₀ clusters were modulated through the competition between solvation and surface trapping.

Arrhythmias are among the diseases with high mortality rates worldwide, causing millions of deaths each year. This underscores the importance of real-time electrocardiogram (ECG) monitoring for timely heart disease diagnosis and intervention. Deep learning models, trained on ECG signals across twelve or more leads, are the predominant approach for automated arrhythmia detection in the AI-assisted medical field. While these multi-lead ECG-based models perform well in automatic arrhythmia detection, their complexity often restricts their use on resource-constrained devices. In this paper, we propose an efficient, lightweight arrhythmia classification model using a knowledge distillation technique to train a student model from a teacher model, tailored for embedded intelligence in wearable devices. The results show that the student model achieves 96.32% accuracy, which is comparable to the teacher model, with a remarkable compression ratio that is 1242.58 times smaller, outperforming other lightweight models. Enabled by the proposed model, we developed a wearable ECG monitoring system based on the STM32F429 Discovery kit and ADS1292R chip, achieving real-time arrhythmia detection on small wearable devices.