Self-Localizing Stabilized Mega-Pixel Picoliter Arrays with Size-Exclusion Sorting Capabilities
Why this work is in the frame
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Bibliographic record
Abstract
We report on a liquid self-localizing process capable of producing Mega-pixel arrays of picoliter volumes on a 1 cm(2) area, within seconds, for high throughput sampling. The chip is based on principles of spatially varying wetting and stabilization. The key is to develop differential surface contact regions, which lead to both localization of the solution and increasing the surface adsorption energy to further pin the liquid to the surface, as highlighted by other studies. By exploiting surface roughness for enhanced wettability, we demonstrate wetting of wells with the aspect ratio of 100. The high precision of reactive ion etching (RIE) of silicon substrates allows for an extremely reproducible method of preparing the array of identical well structures and increased contact area to increase surface adsorption in the wells. "Dynamic wetting" is then readily achieved through inducing contact line instability by simply moving a drop of liquid on the top surface of the array. Liquid samples self-localize into the array pattern with the associated liquid flow leading to self-localization of suspended particles or analyte. The resulting picoliter volumes are both spatially ordered and stable for long periods of time, even for such small volumes, to permit selective measurements of the contents. This development will be particularly important in the assembly of the massive amounts of crystalline particles needed for atomically resolved structural dynamics using the latest advances in high number density electron and X-ray sources.
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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 | 0.001 |
| 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