Humidified Microcontact Printing of Proteins: Universal Patterning of Proteins on Both Low and High Energy Surfaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microcontact printing (μCP) of proteins is widely used for biosensors and cell biology but is constrained to printing proteins adsorbed to a low free energy, hydrophobic surface to a high free energy, hydrophilic surface. This strongly limits μCP as harsh chemical treatments are required to form a high energy surface. Here, we introduce humidified μCP (HμCP) of proteins which enables universal printing of protein on any smooth surface. We found that by flowing water in proximity to proteins adsorbed on a hydrophilized stamp, the water vapor diffusing through the stamp enables the printing of proteins on both low and high energy surfaces. Indeed, when proteins are printed using stamps with increasing spacing between water-filled microchannels, only proteins adjacent to the channels are transferred. The vapor transport through the stamp was modeled, and by comparing the humidity profiles with the protein patterns, 88% relative humidity in the stamp was identified as the threshold for HμCP. The molecular forces occurring between PDMS, peptides, and glass during printing were modeled ab initio to confirm the critical role water plays in the transfer. Using HμCP, we introduce straightforward protocols to pattern multiple proteins side-by-side down to nanometer resolution without the need for expensive mask aligners, but instead exploiting self-alignment effects derived from the stamp geometry. Finally, we introduce vascularized HμCP stamps with embedded microchannels that allow printing proteins as arbitrary, large areas patterns with nanometer resolution. This work introduces the general concept of water-assisted μCP and opens new possibilities for "solvent-assisted" printing of proteins and of other nanoparticles.
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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 it