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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".