A versatile snap chip for high-density sub-nanoliter chip-to-chip reagent transfer
Bibliographic record
Abstract
The coordinated delivery of minute amounts of different reagents is important for microfluidics and microarrays, but is dependent on advanced equipment such as microarrayers. Previously, we developed the snap chip for the direct transfer of reagents, thus realizing fluidic operations by only manipulating microscope slides. However, owing to the misalignment between arrays spotted on different slides, millimeter spacing was needed between spots and the array density was limited. In this work, we have developed a novel double transfer method and have transferred 625 spots cm(-2), corresponding to >10000 spots for a standard microscope slide. A user-friendly snapping system was manufactured to make liquid handling straightforward. Misalignment, which for direct transfer ranged from 150-250 μm, was reduced to <40 μm for double transfer. The snap chip was used to quantify 50 proteins in 16 samples simultaneously, yielding limits of detection in the pg/mL range for 35 proteins. The versatility of the snap chip is illustrated with a 4-plex homogenous enzyme inhibition assay analyzing 128 conditions with precise timing. The versatility and high density of the snap chip with double transfer allows for the development of high throughput reagent transfer protocols compatible with a variety of applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".