Prediction on the Surface Phase Diagram and Growth Morphology of Nanocrystal Ruthenium Dioxide
Bibliographic record
Abstract
Surface energy has an important role in controlling the exposed facets and growth morphology of nanocrystals. In this study, we employed first‐principle thermodynamic modeling and calculations to evaluate the substantial effects of environmental factors (temperature and oxygen partial pressure), on the surface structure, stability, and nanocrystal morphology of rutile‐type ruthenium dioxide ( RuO 2 ). Both stoichiometric and nonstoichiometric surfaces with ideal bulk terminations were assessed. The relative ordering of stoichiometric surface stabilities was predicted as (110) > (101) > (100) > (001). The sensitive environment dependence of nonstoichiometric surface stabilities was evaluated by calculating the surface phase diagram, and partially validated by comparing with available experimental observations. The predicted surface stabilities were further coupled with the Gibbs–Wulff construction of equilibrium crystal shape, to predict the morphological evolutions of RuO 2 nanocrystals under practical growth conditions. A morphology‐controlled growth technique was finally suggested for designing and developing hierarchical nanostructures by intelligently adjusting the thermodynamic growth conditions.
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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".