Bibliographic record
Abstract
Polydimethylsiloxane (PDMS) is the most popular and versatile material for soft lithography due to its flexibility and easy fabrication by molding process. However, for nanoscale patterns, it is challenging to fill uncured PDMS into the holes or trenches on the master mold that is coated with a silane anti-adhesion layer needed for clean demolding. PDMS filling was previously found to be facilitated by diluting it with toluene or hexane, which was attributed to the great reduction of viscosity for diluted PDMS. Here, we suggest that the reason behind the improved filling for diluted PDMS is that the diluent solvent increases in situ the surface energy of the silane-treated mold and thus the wetting of PDMS to the mold surface. We treated the master mold surface (that was already coated with a silane anti-adhesion monolayer) with toluene or hexane, and found that the filling by undiluted PMDS into the nanoscale holes on the master mold was improved despite the high viscosity of the undiluted PDMS. A simple estimation based on capillary filing into a channel also gives a filling time on the millisecond scale, which implies that the viscosity of PMDS should not be the limiting factor. We achieved a hole filling down to sub-200-nm diameter that is smaller than those of the previous studies using regular Sylgard PDMS (not hard PDMS, Dow Corning Corporation, Midland, MI, USA). However, we are not able to explain using a simple argument based on wetting property why smaller, e.g., sub-100-nm holes, cannot be filled, for which we suggested a few possible factors for its explanation.
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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) | 0.001 | 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) | 0.002 | 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".