Material versatility using replica molding for large-scale fabrication of high aspect-ratio, high density arrays of nano-pillars
Bibliographic record
Abstract
Arrays of high aspect-ratio (AR) nano-pillars have attracted a lot of interest for various applications, such as for use in solar cells, surface acoustic sensors, tissue engineering, bio-inspired adhesives and anti-reflective surfaces. Each application may require a different structural material, which can vary in the required chemical composition and mechanical properties. In this paper, a low cost fabrication procedure is proposed for large scale, high AR and high density arrays of nano-pillars. The proposed method enables the replication of a master with high fidelity, using the subsequent replica molds multiple times, and preparing arrays of nano-pillars in a variety of different materials. As an example applied to bio-inspired dry adhesion, polymeric arrays of nano-pillars are prepared in this work. Thermoset and thermoplastic nano-pillar arrays are examined using an atomic force microscope to assess their adhesion strength and its uniformity. Results indicate the proposed method is robust and can be used to reliably prepare nano-structures with a high AR.
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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 | 0.001 |
| 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".