Ultrasmooth Gold Surfaces Prepared by Chemical Mechanical Polishing for Applications in Nanoscience
Bibliographic record
Abstract
For over 20 years, template stripping has been the best method for preparing ultrasmooth metal surfaces for studies of nanostructures. However, the organic adhesives used in the template stripping method are incompatible with many solvents, limiting the conditions that may subsequently be used to prepare samples; in addition, the film areas that can be reliably prepared are typically limited to ∼1 cm(2). In this article, we present chemical-mechanical polishing (CMP) as an adhesive-free, scalable method of preparing ultrasmooth gold surfaces. In this process, a gold film is first deposited by e-beam evaporation onto a 76-mm-diameter silicon wafer. The CMP process removes ∼4 nm of gold from the tops of the grains comprising the gold film to produce an ultrasmooth gold surface supported on the silicon wafer. We measured root-mean-square (RMS) roughness values using atomic force microscopy of 12 randomly sampled 1 μm × 1 μm areas on the surface of the wafer and repeated the process on 5 different CMP wafers. The average RMS roughness was 3.8 ± 0.5 Å, which is comparable to measured values for template-stripped gold (3.7 ± 0.5 Å). We also compared the use of CMP and template-stripped gold as bottom electrical contacts in molecular electronic junctions formed from n-alkanethiolate self-assembled monolayers as a sensitive test bed to detect differences in the topography of the gold surfaces. We demonstrate that these substrates produce statistically indistinguishable values for the tunneling decay coefficient β, which is highly sensitive to the gold surface topography.
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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 | 0.001 |
| 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".