Unravelling soft interfaces: Visualization of gel ridges
Bibliographic record
Abstract
HYPOTHESIS: Soft materials, particularly elastomers, are extensively studied, but investigations into purely soft gel contact systems are limited due to their complex dual phases consisting of polymer and free liquids. While Dual Wavelength-Reflection Interference Confocal Microscopy (DW-RICM) is effective for noninvasively visualizing interfaces from a bottom view, it faces challenges in gel studies due to close refractive indices of polymeric networks and free liquids. We hypothesize that modulating the refractive index of soft gels using nanoparticles (NPs) enhances the visualization of contact zone beneath the free surface, providing insights into the configuration of phase-separated free oil within gel-on-gel contact systems. EXPERIMENTS: ) NPs were introduced into the organogel to modulate refractive indices. Given the lack of prior studies on the hidden contact zone between gels, various techniques, including DW-RICM, side-view imaging, and inverted optical microscopy, were employed to observe and validate our findings. Comparative analyses were conducted with elastomer-on-rigid, elastomer-on-gel, and gel-on-rigid contact systems. FINDINGS: addition did not alter the gels' mechanical and surface properties but significantly enhanced information on gel contact deformation. This enhanced visualization technique has the potential to advance our understanding of adhesive contacts in gels, providing valuable insights into interface phenomena involving biological soft tissues and cells.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".