Controlled deposition of gold nanodots using non-contact atomic force microscopy
Bibliographic record
Abstract
A technique for highly reproducible deposition of nanoscale sized gold dots in an atomic force microscopy (AFM) configuration is described. This is achieved by precisely controlling the tip–sample separation, using feedback control enabled by the application of an external electrostatic servo force. Application of a voltage pulse of either polarity to a gold coated oscillating cantilever tip leads to the deposition of the Au dot. Dimensions for the fabricated dots are 6–100 nm in width, and <1–10 nm in height. The well controlled deposition process allowed the study of dot formation and the obtaining of relevant statistics. We found that the deposition process is the field emission of Au ions. Nevertheless, threshold values obtained are higher than previously reported ones and were found to be dependent on the tip shape. Depositions are independent of substrate morphology and lithographically patterned lines formed by overlapping Au nanodots as long as 55 µm have been fabricated.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
AFM-based deposition of gold nanodots; nanoscience technique.
This studies nanoscale gold-dot deposition using atomic force microscopy, not research practice.
Nanofabrication AFM technique; technical 'reproducible deposition' is assay-sense polysemy.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".