Bibliographic record
Abstract
A scanning tunneling microscope tip is used to create nanoscale contacts on degenerately doped ${n}^{+}\text{\ensuremath{-}}\mathrm{Si}(100)$ surfaces. Current-voltage spectra are recorded as the tip transitions from tunneling to point contact on surfaces prepared in three ways: (1) the clean $2\ifmmode\times\else\texttimes\fi{}1$ surface, (2) with a covalently bonded benzene overlayer, and (3) through nanoscale clean silicon windows formed within the benzene film. Contacts to the clean surface are more Ohmic than rectifying and show a surface leakage current that arises from partially occupied ${\ensuremath{\pi}}^{*}$ states. Contacts to the benzene surface do not display a surface current and exhibit significant current rectification. The unpinning of the Si band structure by the organic adsorbate leads to inversion and a limiting minority-carrier tunnel current under reverse bias. Contacts to the windows simulate defects to an overlayer and reveal characteristics of a hybrid junction. Current-voltage spectra through the windows are free of surface leakage and are independent of the cleaned area. The return of majority carrier tunneling under reverse bias demonstrates that the pinning states of the clean substrate are restored to the windows. However, the charge distribution on the windows is significantly different from the clean surface because the restored surface states are isolated within the benzene monolayer.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".