Precision-Trimming 2D Inverse-Opal Lattice on Elastomer to Ordered Nanostructures with Variable Size and Morphology
Bibliographic record
Abstract
A low-cost and scalable method is developed for producing large-area elastomer surfaces having ordered nanostructures with a variety of lattice features controllable to nanometer precision. The method adopts the known technique of molding a PDMS precursor film with a close-packed monolayer of monodisperse submicron polystyrene beads on water to form an inverse-opal dimple lattice with the dimple size controlled by the bead selection and the dimple depth by the molding condition. The subsequent novel precision engineering of the inverse-opal lattice comprises trimming the PDMS precursor by a combination of polymer curing temperature/time and polymer dissolution parameters. The resultant ordered surface nanostructures, fabricated with an increasing degree of trimming, include (a) submicron hemispherical dimples with nanothin interdimple rims and walls; (b) nanocones with variable degrees of tip-sharpness by trimming off the top part of the nanothin interdimple walls; and (c) soup-plate-like submicron shallow dimples with interdimple rims and walls by anisotropically trimming off the nanocones and forming close-packed shallow dimples. As exemplars of industrial relevance of these lattice features, tunable Young's modulus and wettability are demonstrated.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".