High-resolution investigation of metal nanoparticle growth on an insulating surface
Bibliographic record
Abstract
The three-dimensional nanoparticle morphology and the nanoparticle-substrate relationship during the submonolayer growth of three metals (gold, tantalum, and palladium) on the alkali halide KBr (001) surface is investigated by combining in situ high-resolution noncontact atomic force microscopy and ex situ transmission electron microscopy approaches. Highly varied growth behavior between the metals is revealed. Gold produces nearly spherical multiply twinned nanoparticles at room temperature and an increasing number of epitaxial particles at elevated temperatures. In contrast, the tantalum grows as relatively flat fractal particles, despite the square symmetry of the substrate lattice, a condition which normally precludes fractal growth. The tantalum also exhibits a strong affinity for KBr surface steps, leading to one-dimensional chains of nanoparticles. The deposition of palladium results in the creation of protruding substrate distortions and monolayer-high rectangular KBr islands in addition to the growth of palladium nanoparticles. It is hypothesized that the unusual growth observed in the palladium-KBr system is caused by the interdiffusion of palladium under the KBr surface. The range of growth behavior in the three systems is described in terms of the surface and interface energies, yielding bounds on the metal/KBr interface energies.
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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) | 0.000 | 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 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".