Sintering Rate of Nickel Nanoparticles by Molecular Dynamics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it