Electrostatic enhancement factor for the coagulation of silicon nanoparticles in low-temperature plasmas
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
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.
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 | 0.001 |
| 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