Computational analysis of the effect of surface roughness on the deflection of a gold coated silicon micro-cantilever
Bibliographic record
Abstract
Numerical simulations were performed to study the effects of surface roughness on the deflection of gold coated silicon cantilevers due to molecular adsorption. The cantilever was modeled using a ball and spring system where the spring constants for the Si-Si, Si-Au, and Au-Au bonds were obtained from first principal calculations. The molecular adsorption process was simulated by elongating the natural bond length of the surface bonding sites by 0.5%. Increasing the bond length created a surface stress on the cantilever causing it to deflect. The system studied consisted of a 1 μm portion of a cantilever of various surface roughnesses with variable boundary condition and was processed in parallel on the Atlantic Computational Excellence Network cluster. The results have indicated that cantilevers with a rougher gold surface deflected more than those with a smoother surface. The increase in deflection is attributed to an increase in stress raisers in the gold film localized around the surface features. The onset of stress raisers increases the differential stress between the top and bottom surfaces and results in an increase in the deflection of the cantilever.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".