Submicron probes for scanning electrochemical microscopy
Bibliographic record
Abstract
To improve the spatial resolutions of scanning electrochemical microscopy (SECM) imaging, the laser-pulled submicron electrode fabrication method was explored in this work. Manual polishing of a laser-pulled Pt nanoelectrode exposed a Pt tip diameter of 250 nm with a ratio of the tip glass to exposed Pt disc (RG) of 30. This fabricated submicron probe was then utilized to study the electrochemical functionality of an independently addressable microband electrodes (IAME) sample using SECM. In the constant imaging mode of SECM, where the probe is scanned linearly across the sample at a fixed z position, SECM demonstrated higher resolution than that of the conventional micrometer electrodes when the feedback currents from the Pt and glass microbands were characterized. In addition, the depth scan imaging mode of SECM was also used to extract experimental horizontal line scans and probe approach curves for analysis. Three-dimensional (3D) simulations of the IAME–SECM probe experiments were explored for the first time to quantify the tip-to-sample distances, tilt angle of the sample (or electrode), and height of the Pt microbands. The experimentally characterized height was found to be similar to manufacturer specification (125 nm vs 110 nm). Furthermore, the more computationally demanding 3D simulation of the true IAME sample geometry (110 nm height of the Pt microbands) revealed minimal difference in feedback behaviours in comparison with the idealized flat geometry. The removal of this simulation complexity was proved to be sufficient for SECM analysis of the IAME sample by a 250 nm Pt probe, which greatly saves computation resources.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".