Solid-state dewetting mechanisms of ultrathin Ni films revealed by combining<i>in situ</i>time resolved differential reflectometry monitoring and atomic force microscopy
Bibliographic record
Abstract
In this study we report on the solid-state dewetting of ultrathin Ni films on amorphous ${\text{SiO}}_{2}$. The dewetting process is monitored in situ using time resolved differential reflectometry (TRDR). The time resolved differential reflectivity signal during dewetting is found to exhibit a rich behavior, which is intimately connected the changes in morphology. Finite-difference time-domain simulation is used to explain the observed reflectivity data, where experimentally acquired atomic force microscope heightmaps are used as simulation inputs. From ex situ atomic force microscope heightmaps, the sequential processes of grain growth, grain boundary grooving, hole growth, and particle coarsening are observed. Grain growth of ultrathin films prior to dewetting is critically important in determining the particle density, which has been largely unexplored in previous dewetting studies. Kinetic analysis of the TRDR data revealed two rate-limiting processes, with activation energies of $0.31\ifmmode\pm\else\textpm\fi{}0.04$ and $0.59\ifmmode\pm\else\textpm\fi{}0.06\text{ }\text{eV}$. We hypothesize that these kinetic pathways correspond to Ni grain growth and surface mass self-diffusion on the Ni(111) planes, respectively.
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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 | 0.001 |
| 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".