Transverse Diffusion of Laminar Flow Profiles To Produce Capillary Nanoreactors
Bibliographic record
Abstract
We introduce transverse diffusion of laminar flow profiles (TDLFP), the first generic method for mixing two or more reactants inside capillaries. Conceptually, solutions of reactants are injected inside the capillary by pressure as a series of consecutive plugs. Due to the laminar nature of flow inside the capillary, the nondiffused plugs have parabolic profiles with predominantly longitudinal interfaces between them. After injection, the plugs are mixed by transverse diffusion; longitudinal diffusion does not contribute to mixing. To prove the principle, we used TDLFP to mix two reactants-an enzyme and its substrate. After mixing the reactants by TDLFP, we incubated reaction mixtures for different periods of time and measured the reaction kinetics. We found that the reaction proceeded in time- and concentration-dependent fashion, thus confirming that the reactants were mixed by TDLFP. Remarkably, the experimental reaction kinetics were not only in qualitative agreement but also in good quantitative agreement with theoretically predicted ones. TDLFP has a number of enabling features. By facilitating the preparation of reaction mixtures inside the capillary, TDLFP lowers reagent consumption to nanoliters (microliters are required for conventionally mixing reagents in a vial). The reaction products can be then analyzed "on-line" by capillary separation coupled with optical, electrochemical, or mass spectrometric detection. The combination of TDLFP with capillary separation will be an indispensable tool in screening large combinatorial libraries for affinity probes and drug candidates: a few microliters of a target protein will be sufficient to screen thousands of compounds. The new method paves the road to a wide use of capillary nanoreactors in different areas of physical and life sciences.
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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".