Dual-Vacancy-Engineered ZnIn2S4 Nanosheets for Harnessing Low-Frequency Vibration Induced Piezoelectric Polarization Coupled with Static Dipole Field to Enhance Photocatalytic H2 Evolution.

This study investigates the impact of In- and S-vacancy concentrations on the photocatalytic activity of non-centrosymmetric zinc indium sulfide (ZIS) nanosheets for the hydrogen evolution reaction (HER). A positive correlation between the concentrations of dual In and S vacancies and the photocatalytic HER rate over ZIS nanosheets is observed. The piezoelectric polarization, stimulated by low-frequency vortex vibration to ensure the well-dispersion of ZIS nanosheets in solution, plays a crucial role in enhancing photocatalytic HER over the dual-vacancy engineered ZIS nanosheets. The piezoelectric characteristic of the defective ZIS nanosheets is confirmed through the piezopotential response measured using piezoelectric force microscopy. Piezophotocatalytic H2 evolution over the ZIS nanosheets is boosted under accelerated vortex vibrations. The research explores how vacancies alter ZIS's dipole moment and piezoelectric properties, thereby increasing electric potential gradient and improving charge-separation efficiency, through multi-scale simulations, including Density Functional Theory and Finite Element Analysis, and a machine-learning interatomic potential for defect identification. Increased In and S vacancies lead to higher electric potential gradients in ZIS along [100] and [010] directions, attributing to dipole moment and the piezoelectric effect. This research provides a comprehensive exploration of vacancy engineering in ZIS nanosheets, leveraging the piezopotential and dipole field to enhance photocatalytic performances.

AI-Assisted Fusion of Scanning Electrochemical Microscopy Images Using Novel Soft Probe.

Scanning electrochemical microscopy (SECM) is one of the scanning probe techniques that has attracted considerable attention because of its ability to interrogate surface morphology or electrochemical reactivity. However, the quality of SECM images generally depends on the sizes of the electrodes and many uncontrollable factors. Furthermore, manipulating fragile glass ultramicroelectrodes and blurred images sometimes frustrate researchers. To overcome the challenges of modern SECM, we developed novel soft gold probes and then established the AI-assisted methodology for image fusion. A novel gold microelectrode probe with high softness was developed to scan fragile samples. The distribution of EGFR (protein biomarker) in oral cancer was investigated. Then, we fused the optical microscopic and SECM images to enhance the image quality using Matlab software. However, thousands of fused images were generated by changing the parameters for image fusion, which is annoying for researchers. Thus, a deep learning model was built to select the best-fused images according to the contrast and clarity of the fused images. Therefore, the quality of the SECM images was improved using a novel soft probe and combining the image fusion technique. In the future, a new scanning probe with AI-assisted fused SECM image processing may be interpreted more preciously and contribute to the early detection of cancers.