Electrostatic enhancement factor for the coagulation of silicon nanoparticles in low-temperature plasmas
Bibliographic record
Abstract
Abstract The coagulation enhancement factor due to electrostatic (Coulomb and polarization-induced) interaction between silicon nanoparticles was numerically computed for different nanoparticle sizes and charges in typical low-temperature argon-silane plasma conditions. We used a rigorous formulation, based on a multipole moment coefficients, to describe the complete electrostatic interaction between dielectric particles. The resulting interaction potential is non-singular at the contact point, which allows to adapt the orbital-motion limited theory to calculate the enhancement factor. It is shown that, due to induced polarization, coagulation is enhanced in neutral-charged particles encounters up to several orders of magnitude. Moreover, the short-range force between like-charged nanoparticles can become attractive as a direct consequence of the dielectric nature of the nanoparticles. The multipolar coefficient potential is compared to an approximate analytic form which can be readily used to simplify the calculations. The results presented here provide a better understanding of the electrostatic interaction in coagulation and can be used in dust growth simulations in low-temperature plasmas where coagulation is a significant process.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".