Origin and Nature of Spontaneous Shape Fluctuations in “Small” Nanoparticles
Bibliographic record
Abstract
Normally chemically inert materials such as Au have been found to be catalytically active in the form of particles whose size is about 1 nm. Direct and indirect observations of various types of metal nanoparticles (NPs) in this size range, under catalytically relevant conditions for fuel-cell operation and catalysis, have indicated that such "small" particles can exhibit large spontaneous shape fluctuations and significant changes in shape and chemical activity in response to alterations in environmental conditions. NPs also normally exhibit facile coalescence when in proximity, impacting their stability and reactivity in applications. We perform molecular dynamics simulations on Ni nanoparticles, a commonly used NP in catalytic applications and carbon nanotube growth, in the ≈1 nm size regime where large-scale shape fluctuations have been observed experimentally. An analysis of the large-scale shape fluctuations observed in our simulations of these "small" NPs indicates that they are accompanied by collective motion of Ni atoms through the NP center, and we quantify these dynamic structures and their impact on NP shape. In contrast, stringlike collective atomic motion is confined to the NP interfacial region of NPs having a diameter greater than a few nanometers, and correspondingly, the overall NP shape remains roughly spherical, a case studied in our prior Ni NP simulations. Evidently, the large spontaneous NP shape fluctuations reflect a change in character of the collective atomic dynamics when the NPs become critically small in size.
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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 | 0.001 |
| 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 | 0.001 |
| 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".