Department of Chemistry, McGill University, Montreal, Quebec H3A 0B8, Canada.
Anal Chem. 2020 Mar 3;92(5):3958-3963. doi: 10.1021/acs.analchem.9b05451. Epub 2020 Feb 18.
To achieve super-resolution scanning electrochemical microscopy (SECM), we must overcome the theoretical limitation associated with noncontact electrochemical imaging of surface-generated species. This is the requirement for mass transfer to the electrode, which gives rise to the diffusional broadening of surface features. In this work, a procedure is developed for overcoming this limitation and thus generating "super-resolved" images using point spread function (PSF)-based deconvolution, where the point conductor plays the same role as the point emitter in optical imaging. In contrast to previous efforts in SECM towards this goal, our method uses a finite element model to generate a pair of corresponding blurred and sharp images for PSF estimation, avoiding the need to perform parameter optimization for effective deconvolution. It can therefore be used for retroactive data treatment and an enhanced understanding of the structure-property relationships that SECM provides.
为实现超分辨率扫描电化学显微镜(SECM),我们必须克服与表面生成物质的非接触电化学成像相关的理论限制。这是向电极传递物质的要求,这导致了表面特征的扩散展宽。在这项工作中,开发了一种克服此限制的方法,从而使用基于点扩散函数(PSF)的反卷积生成“超分辨率”图像,其中点导体在光学成像中发挥与点发射器相同的作用。与之前为实现这一目标而在 SECM 方面的努力相比,我们的方法使用有限元模型为 PSF 估计生成一对相应的模糊和清晰图像,从而避免了为有效反卷积进行参数优化的需要。因此,它可用于追溯性数据处理,并增强对 SECM 提供的结构-性质关系的理解。