Enhancing image quality of scanning electrochemical microscopy by improved probe fabrication and displacement
Bibliographic record
Abstract
Scanning electrochemical microscopy (SECM) is a powerful tool for its wide applications in determining charge transfer kinetics, imaging chemical reactions and topography, as well as fabricating microstructures at various interfaces and (or) surfaces. Imaging applications, in particular, rely on the natures of SECM probes and the scanning systems to move them in the vicinity of interfaces. While progress has been made in new approaches to tip fabrication, there are few reports on the improvement of the tip positioning system to enhance SECM image quality. We have recently built an advanced SECM setup using a closed-loop scanning system and improved probe fabrication and characterization procedures. Here we will describe this development, as well as the application of these techniques to greatly improve the quality of SECM images. Video micrograph, cyclic voltammograms, and SECM approach curves (current vs. tipsubstrate distance) were chosen to characterize probe quality and to determine the ratio of electrode diameter to glass sheath diameter. The SECM setup has a resolution and repeatability of 20 nm in three dimensions (x, y, and z) and can locate and relocate areas of interest precisely after a coarse image. Interdigitated electrode arrays of platinum and gold were first imaged. Image resolution revealed by sharpness of Pt band edges was enhanced by using a 2 µm diameter electrode. Pt or Au band height was found to be around 80200 nm by fitting the approach curves to the theoretical ones. Imaging conditions such as delay time for a large step size between two succeeding data points were optimized. To test its thermal and temporal stability, the system was then used to image letters, which were printed on a transparency with font bold Courier New and font size 2. Minor drifts were found during the image process up to the experimental length of 8 h and 45 min. Letter thickness was found to be 1.01.2 µm. A silicon substrate with an array of square pits spaced apart on 10 µm centers was finally imaged. Good quality images were obtained at various tipsubstrate distances even though the squares were just as small, if not smaller, than the tip. The samples were also imaged by AFM for comparison.Key words: scanning electrochemical microscopy, atomic force microscopy, microelectrode fabrication, closed-loop imaging, probe approach curve.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".