RESUMO
Rhenium disulfide (ReS2) is a promising piezoelectric catalyst due to its excellent electron transfer ability and abundant unsaturated sites. The 1T' phase structure leads to the evolution of ReS2 into a centrosymmetric spatial structure, which restricts its application in piezoelectric catalysis. Herein, we propose a controllable defect engineering strategy to trigger the piezoelectric response of ReS2. The introduction of vacancy defects disrupts the initial centrosymmetric structure, which breaks the piezoelectric polarization bond and generates piezoelectric properties. By using transmission electron microscopy, we characterized it at the atomic scale and determined that vacancy defects contribute to an excellent piezoelectric property through first-principles calculations. Notably, the piezoelectric coefficient of the catalyst with 40 s-etching (ReS2@C-40) is 23.07 pm/V, an order of magnitude greater than other transition metal dichalcogenides. It demonstrated the feasibility of optimizing piezoelectric properties by increasing the conformational asymmetry. Based on its remarkable piezoelectric activity, ReS2@C-40 exhibits highly efficient piezo-photocatalytic synergistic sterilization performance with 99.99% eradication of Escherichia coli and 96.67% of Staphylococcus aureus within 30 min. This pioneering research on the coupling effect of ReS2 in piezoelectric catalysis and photocatalysis provides ideas for the development of piezo-photocatalysts and efficient water purification technologies.
RESUMO
In the past few years, deep learning-based electrocardiogram (ECG) compression methods have achieved high-ratio compression by reducing hidden nodes. However, this reduction can result in severe information loss, which will lead to poor quality of the reconstructed signal. To overcome this problem, a novel quality-guaranteed ECG compression method based on a binary convolutional auto-encoder (BCAE) equipped with residual error compensation (REC) was proposed. In traditional compression methods, ECG signals are compressed into floating-point numbers. BCAE directly compresses the ECG signal into binary codes rather than floating-point numbers, whereas binary codes take up fewer bits than floating-point numbers. Compared with the traditional floating-point number compression method, the hidden nodes of the BCAE network can be artificially increased without reducing the compression ratio, and as many hidden nodes as possible can ensure the quality of the reconstructed signal. Furthermore, a novel optimization method named REC was developed. It was used to compensate for the residual between the ECG signal output by BCAE and the original signal. Complemented with the residual error, the restoration of the compression signal was improved, so the reconstructed signal was closer to the original signal. Control experiments were conducted to verify the effectiveness of this novel method. Validated by the MIT-BIH database, the compression ratio was 117.33 and the root mean square difference (PRD) was 7.76%. Furthermore, a portable compression device was designed based on the proposed algorithm using Raspberry Pi. It indicated that this method has attractive prospects in telemedicine and portable ECG monitoring systems.