In Situ Quantification of a Wetted Surface Area during Scanning Electrochemical Cell Microscopy Using Retraction Curves
Why this work is in the frame
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Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide This work presents a new methodology to estimate the surface area of the working electrode during scanning electrochemical cell microscopy (SECCM) in situ by utilizing retraction curves. In this approach, the current is measured as a function of pipet displacement in the z -direction. When the current drops to zero, it is indicative of droplet detachment from the surface, allowing for the estimation of the droplet contact diameter based on the pipet displacement. This enables real-time estimations of surface areas of the wetted electrode at each point of measurement, rather than performing time-consuming measurements using ex situ correlative image analysis or estimating an average working electrode size from the pipet aperture. Notably, during SECCM measurements on copper in nitric acid, the working electrode diameter estimated using retraction curves was significantly smaller than the droplet footprint diameter observed post experiment using ex situ correlative image analysis. This discrepancy is attributed to droplet spreading after pipet retraction, as confirmed by goniometer and silanized pipet measurements. Upon cleaning the surface, the true wetted surface areas during SECCM measurements were found to be in good agreement with values estimated using retraction curves yet were larger than the pipet aperture. Additionally, the effects of approach separation, retraction rates, and probe diameter on the droplet contact size were analyzed using retraction curves. These findings were compared to ex situ methods to assess the reliability of the retraction curves for determining the working electrode surface area. This study demonstrates the potential of retraction curves to provide a higher accuracy in the quantitative analysis of local current density values extracted using SECCM.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it