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Band gap engineering of a semiconductor in a straightforward and efficient manner is highly desirable, but it remains technically challenging. Herein, we demonstrate a powerful nanoparticle occlusion strategy that enables the band gap of silver phosphate (Ag3PO4) crystals to be readily tailored. Specifically, a series of diblock copolymer nanoparticles of varying morphologies is prepared by polymerization-induced self-assembly (PISA) and subsequently used as a particulate additive during the crystallization of Ag3PO4. Remarkably, these copolymer nanoparticles can be efficiently occluded within Ag3PO4single crystals, forming organic/inorganic nanocomposite semiconductors whose optical band gap is tunable, depending on the extent of copolymer occlusion. Furthermore, these copolymer nanoparticles can act as “Trojan horses” to incorporate Pd nanoparticles into Ag3PO4, leading to an enhanced photocatalytic degradation of tetracycline under visible-light irradiation. This study provides a brand-new strategy to regulate the band gap of semiconductors, and in principle, it could enable the rational preparation of a wide range of nanocomposite semiconductor materials with controlled properties.

Long-term monitoring of ECG via wearable monitoring systems has already been widely adopted to detect and prevent heart diseases. However, one of the main issues faced by wearable ECG monitoring systems is that motion artifacts significantly affect the systems' stability and reliability. Therefore, motion artifact reduction is a very challenging task in filtering and processing physiological signals. Based on the existing algorithms and ECG prior knowledge, in this paper, we propose an algorithm, CEEMDAN-IMFx-PCA-CICA, for motion artifact reduction in ambulatory ECG signals using single-channel blind source separation technique. Our algorithm first utilizes CEEMDAN to decompose the mixed signals into IMFs (intrinsic mode function) containing different source signal features, thereby forming new multi-dimensional signals. Using the correlation between IMFx (IMF component with the most ECG features) and each IMF, and PCA are then applied to reduce the dimension of each IMF. Finally, the blind separation of the source ECG signals is achieved by using CICA with IMFx as the constraint reference component. The results of our experiments indicate that our algorithm outperformed CEEMDAN-CICA, CEEMDAN-PCA-CICA, and improved CEEMDAN-PCA-CICA. Besides, the number of iterations of the CICA is significantly reduced; the separated source signal is better; the obtained result is stable. Furthermore, the separated ECG signal has a higher correlation with the source ECG signal and a lower RRMSE, especially in the case of high noise-to-signal ratios.