Simplified Besocke scanning tunneling microscope with linear approach geometry
Bibliographic record
Abstract
Besocke-style scanning tunneling microscopes are used in low and variable temperature applications because they are compact and the tip-sample spacing is insensitive to thermal drift. It is demonstrated that the economical Besocke design can be simplified even further if a linear approach geometry is used. In this geometry, the sample has only to be moved along two orthogonal axes and just 11 wires are required to control both tip-sample approach and image acquisition. This simplifies the control electronics, increases the reliability of the microscope and, if the microscope is operated in a cryostat, it weakens the thermal link between low and room temperature. Nevertheless, all of the advantages of the Besocke design are retained including thermal compensation of the tip-sample spacing. A self-aligning mechanism is also described that automatically locates the sample relative to the scanner before tip-sample approach. This feature is particularly useful because the microscope is designed for remote operation in a cryostat where there is restricted visual access. Graphite was used as a test surface and images are presented of β-site corrugation and moiré supermeshes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".