Tuning the thermal conductivity of silicon nanowires by surface passivation
Bibliographic record
Abstract
Abstract Using large scale molecular dynamics simulations, we study the thermal conductivity of bare and surface passivated silicon nanowires (SiNWs). For the cross–sectional widths w ⩽ 2 nm, SiNWs become unstable because of the surface amorphization and also due to the evaporation of a certain fraction of Si atoms. The observed surface (in–)stability is related to a large excess energy Δ of the surface Si atoms with respect to the bulk Si, resulting from the surface atoms being less coordinated and having dangling bonds. We first propose a practically relevant method that uses Δ as a guiding tool to passivate these dangling bonds with hydrogen or oxygen, stabilizing the SiNWs. These passivated SiNWs are used to calculate the thermal conductivity coefficient κ. While the expected trend of κ ∝ w is observed for all SiNWs, surface passivation provides an added flexibility of tuning κ with the surface coverage concentration c of passivated atoms. Indeed, with respect to the bulk κ, passivation of SiNW reduces κ by 75%–80% for c → 50 % and increases it by 50% for the fully passivated samples. Analyzing the phonon band structures via spectral energy density, we discuss separate contributions from the surface and the core to κ. Our results also reveal that surface passivation increases SiNW stiffness, contributing to the tunability in κ.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".