Retained fluorescence of aggregation-caused quenched Rhodamine grafted in the hierarchical mesopores of silica MCM-41 at solid-state

Traditional organic compounds in dilute solutions exhibit different photophysical properties in comparison to their concentrated solutions. For example, organic materials which obey the aggregation-caused quench (ACQ) effect phenomena are known to have weak luminescence at solid-state (or in high concentration solutions) as compared to their dilute counterparts. This effect limits the application of ACQ compounds at solid-state. Herein, we report a way to overcome this phenomenon in Rhodamine B (RhB) by anchoring it to mesoporous silica having a hierarchical structure (referred to as MCM-41) with the help of 3-Aminopropyltriethoxysilane (APTES). Neat solid-state RhB suffers dynamic intramolecular rotations which results in non-radiative annihilation of its excited states and thus luminescence quenching. The strategy explored herein of employing APTES-MCM-41 exposes the cylindrical one-dimensional mesopores of silica for possible selective anchoring of RhB dyes, which helps to overcome the stacking interactions of the fluorophores-thus fluorescent retention. Finally, we went on to show the capabilities of the modified reserved-fluorescent ACQ RhB-grafted mesoporous silica as a possible concentration indicator for liquids and vapors

Automatic Classification of Normal–Abnormal Heart Sounds Using Convolution Neural Network and Long-Short Term Memory

The phonocardiogram (PCG) is an important analysis method for the diagnosis of cardiovascular disease, which is usually performed by experienced medical experts. Due to the high ratio of patients to doctors, there is a pressing need for a real-time automated phonocardiogram classification system for the diagnosis of cardiovascular disease. This paper proposes a deep neural-network structure based on a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory network (LSTM), which can directly classify unsegmented PCG to identify abnormal signal. The PCG data were filtered and put into the model for analysis. A total of 3099 pieces of heart-sound recordings were used, while another 100 patients’ heart-sound data collected by our group and diagnosed by doctors were used to test and verify the model. Results show that the CNN-LSTM model provided a good overall balanced accuracy of 0.86 ± 0.01 with a sensitivity of 0.87 ± 0.02, and specificity of 0.89 ± 0.02. The F1-score was 0.91 ± 0.01, and the receiver-operating characteristic (ROC) plot produced an area under the curve (AUC) value of 0.92 ± 0.01. The sensitivity, specificity and accuracy of the 100 patients’ data were 0.83 ± 0.02, 0.80 ± 0.02 and 0.85 ± 0.03, respectively. The proposed model does not require feature engineering and heart-sound segmentation, which possesses reliable performance in classification of abnormal PCG; and is fast and suitable for real-time diagnosis application