Large area periodic, systematically changing, multishape nanostructures by laser interference lithography and cell response to these topographies
Bibliographic record
Abstract
The fabrication details to form large area systematically changing multishape nanoscale structures on a chip by laser interference lithography (LIL) are described. The feasibility of fabricating different geometries including dots, ellipses, holes, and elliptical holes in both x- and y- directions on a single substrate is shown by implementing a Lloyd's interferometer. The fabricated structures at different substrate positions with respect to exposure time, exposure angle and associated light intensity profile are analyzed. Experimental details related to the fabrication of symmetric and biaxial periodic nanostructures on photoresist, silicon surfaces, and ion milled glass substrates are presented. Primary rat calvarial osteoblasts were grown on ion-milled glass substrates with nanotopography with a periodicity of 1200 nm. Fluorescent microscopy revealed that cells formed adhesions sites coincident with the nanotopography after 24 h of growth on the substrates. The results suggest that laser LIL is an easy and inexpensive method to fabricate systematically changing nanostructures for cell adhesion studies. The effect of the different periodicities and transition structures can be studied on a single substrate to reduce the number of samples significantly.
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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".