How morphology and surface crystal texture affect thermal stability of a metallic nanoparticle: the case of silver nanobelts and pentagonal silver nanowires
Bibliographic record
Abstract
Thermal instability of metallic nanoparticles is typically attributed to chemical attack by contaminants. However, thermodynamic stability is independent of other affecting parameters. The importance of this will be clarified when the structural change toward a more stable thermodynamic condition may be followed by a chemical reaction with the surroundings, which may cause a wrong diagnosis. In this research, molecular dynamics simulations and experimental observations were performed to investigate the effect of crystallography and surface texture on stability at high temperature using two closely related model nanoparticles: silver nanobelts and pentagonal nanowires. Previously, the instability of silver nanowires was associated with sulfidation of the wire at high temperature. However, we found that the silver nanowires are inherently unstable at high temperature, degrading due to the high-energy nature of the nanowire's predominately (100) crystallographic surface and pentagonal geometry. In contrast, the silver nanobelts resist thermal degradation up to 500 °C because of their predominately low-energy (111) crystallographic surfaces. In this case study, we successfully demonstrate that inherent thermodynamic stability driven by morphology is significant in metallic nanoparticles, and should be investigated when selecting a nanoparticle for high temperature applications. Moreover, we identify a new one-dimensional nanoparticle, the silver nanobelt, with inherent high-temperature stability.
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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 | 0.000 |
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".