Self-Localizing Stabilized Mega-Pixel Picoliter Arrays with Size-Exclusion Sorting Capabilities
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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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 | 0.001 | 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".