Nanoscale terahertz STM imaging of a metal surface
Bibliographic record
Abstract
Terahertz scanning tunneling microscopy (THz-STM) has enabled studies of ultrafast dynamics in materials down to the atomic scale. However, despite recent advances, more work is needed to better understand and quantify the subpicosecond THz pulse-induced tunnel currents and corresponding THz-STM images of nanoscale features on surfaces. Here, we perform THz-STM on a metal surface and fully characterize the observed THz pulse-induced tunnel current and nanoscale imaging at atomic steps and defects using a Bardeen tunneling model in a three-dimensional (3D) tip geometry. We show that the measured steady-state STM current-voltage curves can be used in our model to accurately map the observed ultrafast THz-induced tunnel currents and calibrate the magnitude of the near-field peak transient THz voltage bias in the tunnel junction. Peak THz voltage bias transients greater than 10 V across the STM junction are achieved leading to field emission of subpicosecond tunnel currents with current densities exceeding ${10}^{9}\phantom{\rule{0.28em}{0ex}}\mathrm{A}/\mathrm{c}{\mathrm{m}}^{2}$ in THz-STM imaging of a Cu(111) surface. Our results establish an important benchmark for future studies in THz-STM by quantifying the ultrafast THz-induced currents and bias voltages in the tunnel junction and providing a 3D tunneling model for understanding and accurately simulating THz-STM images of nanoscale features on metal surfaces.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".