Protein patterning by microcontact printing using pyramidal PDMS stamps
Bibliographic record
Abstract
Micro-contact printing, μCP, is a well-established soft-lithography technique for printing biomolecules. μCP uses stamps made of Poly(dimethylsiloxane), PDMS, made by replicating a microstructured silicon master fabricated by semiconductor manufacturing processes. One of the problems of the μCP is the difficult control of the printing process, which, because of the high compressibility of PDMS, is very sensitive to minute changes in the applied pressure. This over-sensitive response leads to frequent and/or uncontrollable collapse of the stamps with high aspect ratios, thus decreasing the printing accuracy and reproducibility. Here we present a straightforward methodology of designing and fabricating PDMS structures with an architecture which uses the collapse of the stamp to reduce, rather than enlarge the variability of the printing. The PDMS stamp, organized as an array of pyramidal micro-posts, whose ceiling collapses when pressed on a flat surface, replicates the structure of the silicon master fabricated by anisotropic wet etching. Upon application of pressure, depending on the size of, and the pitch between, the PDMS pyramids, an air gap is formed surrounding either the entire array, or individual posts. The printing technology, which also exhibits a remarkably low background noise for fluorescence detection, may find applications when the clear demarcation of the shapes of protein patterns and the distance between them are critical, such as microarrays and studies of cell patterning.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".