Sintering Rate of Nickel Nanoparticles by Molecular Dynamics
Bibliographic record
Abstract
Nickel nanoparticles (Ni NPs) are widely used in batteries, catalysts, and filters. Properties of Ni NPs strongly depend on their crystal structure and morphology quantified by the state (i.e., solid, transient, or liquid phase) of primary particles (PPs), hard agglomerate (aggregate), and PP size. The growth rate of PPs during gas-phase synthesis is determined by their characteristic sintering time (τ s ) that is sensitive to temperature, the state, and size of PPs. Here, the crystallinity and sintering of Ni NP dimers (2 nm ≤ d p ≤ 5 nm) between 1000 and 1600 K are investigated by molecular dynamics (MD) simulations using the embedded-atom method (EAM) force field. It is shown that at low temperatures ( T ≤ 1400 K) and for large PPs ( d p ≥ 4 nm), diffusion of atoms in PPs controls solid-state sintering. However, PP crystallinity quantified by the disorder variable indicates that with increasing temperature or decreasing PP size, atoms become increasingly mobile and disordered starting from the surface of the PPs until the Ni NPs become fully melted and viscous flow sintering becomes dominant. A general formula for the τ s of Ni NPs is proposed that is valid for all particle states, and its performance is benchmarked by predicting the evolution of the morphology of Ni agglomerate quantified by its mobility and PP diameters during gas-phase sintering in a flow reactor.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".