Ab initio calculation of the interaction between neutral and charged silicon nanoclusters
Bibliographic record
Abstract
Abstract In dusty plasmas, the formation of nanoclusters marks the beginning of the coagulation stage, leading to the rapid generation of larger particles. In this work, we present an overview of the interaction between silicon nanoclusters (SNCs) of about 1 nm diameter within the framework of density functional theory (DFT), taking into account chemical, van der Waals, and multipolar electrostatic interactions. Two types of SNCs are considered: particles composed entirely of silicon (Si30, Si40, Si50, Si60) and a particle whose dangling bonds are occupied by hydrogen atoms (Si29H24). The interaction energies obtained between two neutral or weakly charged SNCs all have a repulsive part at a short separation distance, followed by a minimum corresponding to a stable state of coagulation due to chemical bonds between the particles. In particular, our calculations show that: (1) the Hamaker constant (which characterizes the London-type van der Waals interaction) depends on the pair of identical SNCs, (2) the multipolar electrostatic contribution at large separation distances allows the extraction of the charged SNC polarization coefficient, and (3) the coagulation rates between SNCs are significantly higher than previously estimated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".