The dynamics of metal nanoparticles on a supporting interacting substrate
Bibliographic record
Abstract
The interaction strength of the nanoparticles (NPs) with the supporting substrate can greatly influence both the rate and selectivity of catalytic reactions, but the origins of these changes in reactivity arising from the combined effects of NP structure and composition, and NP–substrate interaction is currently not well-understood. Since the dynamics of the NPs is implicated in many NP-based catalytic processes, we investigate how the supporting substrate alters the dynamics of representative Cu NPs on a model graphene substrate, and a formal extension of this model in which the interaction strength between the NPs and the substrate is varied. We particularly emphasize how the substrate interaction strength alters the local mobility and potential energy fluctuations in the NP interfacial region, given the potential relevance of such fluctuations to NP reactivity. We find the NP melting temperature Tm progressively shifts downward with an increasing NP–substrate interaction strength, and that this change in NP thermodynamic stability is mirrored by changes in local mobility and potential energy fluctuations in the interfacial region that can be described as “colored noise.” Atomic diffusivity, D, in the “free” and substrate NP interfacial regions is quantified, and observed variations are rationalized by the localization model linking D to the mean square atomic displacement on a “caging” timescale on the order of a picosecond. In summary, we find that the supporting substrate strongly modulates the stability and dynamics of supported NPs—effects that have evident practical relevance for understanding changes in NP catalytic behavior derived from the supporting substrate.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".