Electrochemical etching technique: Conical-long-sharp tungsten tips for nanoapplications
Bibliographic record
Abstract
This paper proposes an electrochemical etching technique to fabricate tungsten tips. Tips combining well-defined conical shape, a length as large as 2 mm, and sharpness with a radius of curvature of around 20 nm are fabricated using the proposed technique. These tips are needed in a variety of applications including multipoint contact measurements and nanomanipulation. The technique consists of three steps: the first is static etching, which creates a neck-in phenomenon on the wire; the second is dynamic etching, where the wire is oscillated up and down in the solution to form a long conical shape; finally, static etching is applied again to break the wire, and thus, sharp tips are produced. The best operating conditions of the process were experimentally obtained. These factors include the position of the cathode, the length of the immersed wire, and the applied voltage. The effects of these factors on the etching current and tip fidelity were also examined based on the measured etching current. In particular, the position of the cathode determines the strength of the electrical field near the air/solution interface; the immersed wire depth determines mainly the equivalent resistance of the process and thus controls the etching current; and the applied voltage defines the etching rate of the wire.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".