Molecular Dynamics Simulation of Thermal Accommodation Coefficients for Laser-Induced Incandescence Sizing of Nickel Nanoparticles
Bibliographic record
Abstract
While time-resolved laser-induced incandescence is most often used to characterize the size and concentration of aerosolized carbonaceous particles, it has recently been applied to aerosols containing metal nanoparticles. This calculation requires the thermal accommodation coefficient, however, which is often difficult to determine experimentally. This paper presents a molecular dynamics investigation of the thermal accommodation coefficient between laser-energized nickel nanoparticles immersed in argon, and the underlying the gas-surface scattering physics. The predicted interaction between gas molecules and the laser-energized surface depends strongly on the potential between the gas molecule and a surface atom: a Lennard-Jones 6–12 potential derived using the Lorentz-Berthelot combination rules overestimates the potential well due to a bond-order effect in the nickel, resulting in strong trapping-desorption and near-perfect thermal accommodation. A Morse potential with parameters obtained directly from ab initio free energies predicts a relatively brief interaction between the gas molecule and nickel surface, on the other hand, and a lower thermal accommodation coefficient similar to experimentally-derived values for laser-energized iron nanoparticles in argon reported in the literature.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".