Influence of multipolar electrostatic and van der Waals forces on the coagulation of silicon nanoparticles in low-temperature argon-silane plasmas
Bibliographic record
Abstract
Abstract Numerical calculations have been performed to investigate the effects of electrostatic and van der Waals interactions on coalescing silicon nanoparticles in isotropic low-temperature argon–silane plasmas. The electrostatic interaction between nanoparticles is modeled using three approaches, including the elementary Coulomb interaction, a rigorous electrostatic multipolar expansion, and an approximate form of the latter. The van der Waals interaction is described using the Hamaker expression regularized at short separation distance to manage the singularity at the contact surface. The evolution of the size and charge distributions are calculated by solving the general dynamic equation numerically for the coagulation of charged particles and using simplified approaches for nucleation, surface growth, and self-consistent plasma dynamics. A two-population size distribution, as observed in experiments, results naturally from this model. The electrostatic multipolar force is found to enhance the coagulation as compared to the elementary Coulomb force. The details of the growth process depend, however, significantly on the adopted regularization of the Hamaker expression at a short distance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".