Large‐Scale Dried Reagent Reconstitution and Diffusion Control Using Microfluidic Self‐Coalescence Modules
Bibliographic record
Abstract
The positioning and manipulation of large numbers of reagents in small aliquots are paramount to many fields in chemistry and the life sciences, such as combinatorial screening, enzyme activity assays, and point-of-care testing. Here, a capillary microfluidic architecture based on self-coalescence modules capable of storing thousands of dried reagent spots per square centimeter is reported, which can all be reconstituted independently without dispersion using a single pipetting step and ≤5 μL of a solution. A simple diffusion-based mathematical model is also provided to guide the spotting of reagents in this microfluidic architecture at the experimental design stage to enable either compartmentalization, mixing, or the generation of complex multi-reagent chemical patterns. Results demonstrate the formation of chemical patterns with high accuracy and versatility, and simple methods for integrating reagents and imaging the resulting chemical patterns.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".