Maleic anhydride (MA) is introduced to fabricate poly(vinylidene fluoride)/expanded graphite (PVDF/EG) composites via one-step melt mixing. SEM micrographs and WAXD results have demonstrated that the addition of MA helps to exfoliate and disperse the EG well in the PVDF matrix by promoting the mobility of PVDF molecular chains and enhancing the interfacial adhesion between the EG layers and the PVDF. Thus, much higher thermal conductivities are obtained for the PVDF/MA/EG composites compared to the PVDF/EG composites that are lacking MA. For instance, The PVDF/MA/EG composite prepared with a mass ratio of 93:14:7 exhibits a high thermal conductivity of up to 0.73 W/mK. It is 32.7% higher than the thermal conductivity of the PVDF/EG composite that is prepared with a mass ratio of 93:7. Moreover, the introduction of MA leads to an increased melting peak temperature and crystallinity due to an increased nucleation site provided by the uniformly dispersed EG in the PVDF matrix. This study provides an efficient preparation method for PVDF/EG composites with a high thermal conductivity.

Atrial fibrillation (AF) is the most prevalent arrhythmia characterized by intermittent and asymptomatic episodes. However, traditional detection methods often fail to capture the sporadic and intricate nature of AF, resulting in an increased risk of false-positive diagnoses. To address these challenges, this study proposes an intelligent AF detection and diagnosis method that integrates Complementary Ensemble Empirical Mode Decomposition, Power-Normalized Cepstral Coefficients, Bi-directional Long Short-term Memory (CEPNCC-BiLSTM), and photoelectric volumetric pulse wave technology to enhance accuracy in detecting AF. Compared to other approaches, the proposed method demonstrates faster preprocessing efficiency and higher sensitivity in detecting AF while effectively filtering out false alarms from photoplethysmography (PPG) recordings of non-AF patients. Considering the limitations of conventional AF detection evaluation systems that lack a comprehensive assessment of efficiency and accuracy, this study proposes the ET-score evaluation system based on F-measurement, which incorporates both computational speed and accuracy to provide a holistic assessment of overall performance. Evaluated with the ET-score, the CEPNCC-BiLSTM method outperforms EEMD-based improved Power-Normalized Cepstral Coefficients and Bi-directional Long Short-term Memory (EPNCC-BiLSTM), Support Vector Machine (SVM), EPNCC-SVM, and CEPNCC-SVM methods. Notably, this approach achieves an outstanding accuracy rate of up to 99.2% while processing PPG recordings within 5 s, highlighting its potential for long-term AF monitoring.