Single nanotube array based nano encoders
Bibliographic record
Abstract
Linear encoders for nanoscale position sensing based on single carbon nanotube (CNT) arrays are presented. Vertically aligned single multi-walled carbon nanotubes (MWNTs) are realized using a combination of e-beam lithography and plasma-enhanced chemical vapour deposition (PECVD) growth. Electron beam lithography is used to define 50-150 nm nickel catalyst dots at precise locations on a silicon chip. Precise control of the position, density and alignment of the tubes has been achieved. Aligned nanotube arrays with spacing varying from 250 nm to 25 /spl mu/m are realized. Field emission properties of the array are investigated inside a scanning electron microscope (SEM) equipped with a 3-DOF nanorobotic manipulator with nanometer resolution functioning as a scanning anode. With this scanning anode and the single MWNT array, a nano encoder is investigated experimentally. Vertical position is detected by the change in emission current, whereas the horizontal position of the scanning anode is sensed from the emission distribution. A resolution of 98.3 nm in the vertical direction and 38.0 nm (best: 12.9 nm) in the lateral direction has been achieved.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".