Photo-injection based sample design and electroosmotic transport in microchannels
Bibliographic record
Abstract
Techniques for facilitating chemical reactions and transporting reactants in microfluidic applications are becoming increasingly important. Combined experimental and numerical studies of a photochemical injection process and the subsequent electroosmotic transport of products are presented here. In the experiments, intense focusing of ultraviolet light performs local photolysis of a caged fluorescent dye in a 25 μm i.d. capillary. The advection and diffusion of this sample in electroosmotic flow are imaged using a micro-flow visualization system. Independent numerical simulations of the sample transport are conducted with a custom-designed code. Strong agreement between the numerical predictions and the experimental results is established. Further comparisons demonstrate that near-ideal, diffusion-limited sample transport has been achieved. Focusing on diffusion, numerical simulations show that alternative sample concentration profiles may be obtained through the diffusive interactions of multiple, photo-injected Gaussian sample concentration profiles. In particular, a compact, flat-topped sample profile, exhibiting a constant concentration plateau is predicted. The ability to produce this sample profile using multiple photo injections was demonstrated experimentally in agreement with numerical simulation results.
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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 | 0.001 |
| 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) | 0.000 | 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 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".