Orientation Selection of Supported Au Nanoparticles on (111)- and (001)-Terminated SrTiO3 Substrates
Bibliographic record
Abstract
Orientation-dependent performance has been demonstrated in different materials consisting of nanoparticles on substrates. The fabrication of desirably oriented nanoparticles requires knowledge of orientation selection rules. Based on the Wulff–Kaishew theory, our analysis shows that the energy-favorable orientation(s), is influenced by the surface energy of particles, in addition to the dominant factor, i.e., the energy difference between particle/substrate interfacial energy and surface energy of the substrate. To verify this, a model system of dewetted Au nanoparticles on SrTiO3 is studied. The {111}-terminated SrTiO3 supports only {111}-orientated Au particles, with the lowest interfacial energy. On the other hand, {100}-terminated SrTiO3 supports multiple Au particles, with {111}-, {100}-, {110}- orientations, as a possible result of close surface energy contributions. The above orientations can be additionally manipulated by changing the heat treatment temperature. Our results provide fundamental insights into fabricating supported nanoparticles for practical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".