Ivinskij Vadimas, Zinovicius Antanas, Dzedzickis Andrius, Subaciute-Zemaitiene Jurga, Rozene Juste, Bucinskas Vytautas, Macerauskas Eugenijus, Tolvaisiene Sonata, Morkvenaite-Vilkonciene Inga
Department of Electronics Engineering, Vilnius Gediminas Technical University, Plytinės g. 25, 10105 Vilnius, Lithuania.
Department of Mechatronics, Robotics, and Digital Manufacturing, Vilnius Gediminas Technical University, Plytinės g. 25, 10105 Vilnius, Lithuania.
Ultramicroscopy. 2024 May;259:113937. doi: 10.1016/j.ultramic.2024.113937. Epub 2024 Feb 15.
Scanning electrochemical microscopy (SECM) is a scanning probe microscope with an ultramicroelectrode (UME) as a probe. The technique is advantageous in the characterization of the electrochemical properties of surfaces. However, the limitations, such as slow imaging and many functions depending on the user, only allow us to use some of the possibilities. Therefore, we applied visual recognition and machine learning to detect micro-objects from the image and determine their electrochemical activity. The reconstruction of the image from several approach curves allows it to scan faster and detect active areas of the sample. Therefore, the scanning time and presence of the user is diminished. An automated scanning electrochemical microscope with visual recognition has been developed using commercially available modules, relatively low-cost components, design, software solutions proven in other fields, and an original control and data fusion algorithm.
扫描电化学显微镜（SECM）是一种以超微电极（UME）为探针的扫描探针显微镜。该技术在表征表面电化学性质方面具有优势。然而，其局限性，如成像速度慢以及许多功能依赖用户操作，仅使我们能够利用部分可能性。因此，我们应用视觉识别和机器学习从图像中检测微观物体并确定其电化学活性。通过对多条进近曲线进行图像重建，可实现更快的扫描并检测样品的活性区域。因此，扫描时间缩短且减少了用户的参与。我们利用市售模块、成本相对较低的组件、设计、在其他领域已得到验证的软件解决方案以及一种原创的控制和数据融合算法，开发出了一种具有视觉识别功能的自动扫描电化学显微镜。
