Atomic layer restructuring of gold surfaces by N-heterocyclic carbenes over large surface areas
Bibliographic record
Abstract
Even highly planar, polished metal surfaces display varying levels of roughness that can affect their optical and electronic properties, impacting performance in state-of-the-art microelectronics. Current methods for smoothing rough metallic surfaces require either the removal or addition of substantial amounts of material using complex processes that are incompatible with 3-dimensional nano-scale features needed for state-of-the-art applications. We present a vapor-phase process that results in up to a 60% smoothing of nanometer scale rough gold surfaces through a single exposure to a new class of ligand called N-heterocyclic carbenes (NHCs). This process does not require removal or addition of metal from the surface and provides smoothing at the Ångström scale. Smoothing occurs in a single deposition, giving quantifiable differences in the adsorption behavior of the resulting surfaces. The process takes place through an adatom–extraction-driven destabilization and restructuring of the surface in a self-limiting manner. This novel process is achieved without the use of harsh chemical etchants or mechanical intervention, takes only minutes, and can easily be integrated with vapor phase processing in situ. It offers a promising avenue for subnanometer smoothing in microfabrication workflows. Our observations pave the way for atomic layer restructuring (ALR), a technique that compliments atomic layer deposition (ALD) and atomic layer etching (ALE) in the fabrication and processing high precision materials.
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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) | 0.001 | 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".