a-machine-vision-approach-to-transmission-electron-microscopy

A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management

Transmission electron microscopy (TEM) enables users to study materials at their fundamental, atomic scale. Complex experiments routinely generate ...

Protochips, Inc.

Insight into dynamic electrochemical processes can be obtained with in situ electrochemical-scanning/transmission electron microscopy (ec-S/TEM), a technique that utilizes microfluidic electrochemical cells to characterize electrochemical processes with S/TEM imaging, diffraction, or spectroscopy. The microfluidic electrochemical cell is composed of microfabricated devices with glassy carbon and platinum microband electrodes in a three-electrode cell configuration. To establish the validity of this method for quantitative in situ electrochemistry research, cyclic voltammetry (CV), choronoamperometry (CA), and electrochemical impedance spectroscopy (EIS) were performed using a standard one electron transfer redox couple [Fe(CN)6]3-/4--based electrolyte. Established relationships of the electrode geometry and microfluidic conditions were fitted with CV and chronoamperometic measurements of analyte diffusion coefficients and were found to agree with well-accepted values that are on the order of 10-5 cm2/s. Influence of the electron beam on electrochemical measurements was found to be negligible during CV scans where the current profile varied only within a few nA with the electron beam on and off, which is well within the hysteresis between multiple CV scans. The combination of experimental results provides a validation that quantitative electrochemistry experiments can be performed with these small-scale microfluidic electrochemical cells provided that accurate geometrical electrode configurations, diffusion boundary layers, and microfluidic conditions are accounted for. 

David Nackashi

Protochips, Inc.

A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management