Insight into the Ehrlich–Schwoebel barrier via three-dimensional atomic force microscopy mapping of surface potentials on Au (111)
Bibliographic record
Abstract
Thin film growth is a critical process enabling modern applications ranging from electronic devices to advanced coatings. Among the parameters that govern thin film growth, the Ehrlich-Schwoebel barrier stands out with its tight control over interlayer transfer and, consequently, kinetics-dominated film morphology. Despite its importance, the precise measurement of the Ehrlich-Schwoebel barrier remains complicated, presenting a critical impediment to rational thin film design. Here, we provide an insight into the Ehrlich-Schwoebel barrier over monoatomic step edges on Au (111) surfaces via three-dimensional atomic force microscopy (3D-AFM) with sub-nanometer spatial precision, minimizing the need for empirical model assumptions or theoretical calculations. Our measurements provide a quantitative, real-space view of the complex potential energy and force landscape near step edges, verifying the presence of energy barriers and wells at the top and bottom of step edges, respectively. The effect of the herringbone reconstruction on the potential energy landscape is also analyzed, revealing an enhancement of interactions near the elbows and a slight attenuation of the ridges. Precise thin film growth is pivotal for applications in electronics and coatings, yet kinetics-dominated film morphology remains challenging to control. Here, the authors utilize three-dimensional atomic force microscopy to achieve sub-nanometer precision in measuring the Ehrlich-Schwoebel barrier on Au (111) surfaces, offering a detailed real-space view that enhances thin film design strategies.
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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 | 0.001 | 0.001 |
| 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".