Plasma-Enhanced Atomic Layer Deposition of Nanostructured Gold Near Room Temperature
Bibliographic record
Abstract
plasma as the reactant. The process has a deposition window from 50 to 120 °C with a growth rate of 0.030 ± 0.002 nm per cycle on gold seed layers, and it shows saturating behavior for both the precursor and reactant exposure. X-ray photoelectron spectroscopy measurements show that the gold films deposited at 120 °C are of higher purity than the previously reported ones (<1 at. % carbon and oxygen impurities and <0.1 at. % phosphorous). A low resistivity value was obtained (5.9 ± 0.3 μΩ cm), and X-ray diffraction measurements confirm that films deposited at 50 and 120 °C are polycrystalline. The process forms gold nanoparticles on oxide surfaces, which coalesce into wormlike nanostructures during deposition. Nanostructures grown at 120 °C are evaluated as substrates for free-space surface-enhanced Raman spectroscopy (SERS) and exhibit an excellent enhancement factor that is without optimization, only one order of magnitude weaker than state-of-the-art gold nanodome substrates. The reported gold PE-ALD process therefore offers a deposition method to create SERS substrates that are template-free and does not require lithography. Using this process, it is possible to deposit nanostructured gold layers at low temperatures on complex three-dimensional (3D) substrates, opening up opportunities for the application of gold ALD in flexible electronics, heterogeneous catalysis, or the preparation of 3D SERS substrates.
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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 | 0.001 |
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".