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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".