Profiling contact ridge of soft substrates with metallic thin films using a novel interference technique
Bibliographic record
Abstract
As technology moves toward flexibility, embedded systems, and miniaturization, which often have metallic elements integrated with the functional substrates, understanding the contact deformation of such soft substrates coated with thin metallic films is crucial. However, probing nanoscale deformations optically, typically in a few hundred nanometers, poses challenges. Dual Wavelength - Reflectance Interference Contrast Microscopy (DW-RICM) holds promise in unveiling nanoscale deformations. However, when paired with conventional glass probes to measure deformation of underlying soft substrates, DW-RICM faces limitations due to light reflection at the glass-air interface, impeding accurate determination of the contact zone (e.g., contact area, contact ridge). Here we hypothesize that using alternative probes to eliminate unwanted reflections can enhance understanding of soft substrate deformation with thin metallic films (bilayer substrates). In this work, we investigate the deformation profile of viscoelastic soft substrates with sputter-coated gold thin films (referred to herein as bilayer substrates) using DW-RICM. We employ millimeter-sized black ink-coated glass spheres as probes, absorbing most light under DW-RICM. We focus our laser beams associated with DW-RICM at the contact zone between the probe and the underlying soft substrates. Our new technique of using black ink-coated glass probes in contact with soft bilayer substrates enables us to accurately extract the deformation profile, particularly the contact ridge. With the increase in the gold sputter coating time, the contact area and contact ridge of gold-polymer bilayer substrates decrease. Further, long-term contact analysis of gold-coated soft substrates reveals a steady increase in the contact ridge height over time, unlike the gradual decrease observed in their bare counterparts until reaching a steady state.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".