Generation of flow and droplets with an ultra-long-range linear concentration gradient
Bibliographic record
Abstract
In the chemical and biological fields, the creation of concentration gradient microenvironments is an important approach for many applications, such as crystal growth and drug screening. Although many concentration gradient generators have been demonstrated, current generators can hardly produce ultra-long linear concentration gradients. In this paper, we propose a concentration-gradient flow/droplet generator which consists of a microfluidic flow switch, a cavity array for stage-by-stage concentration dilution, and an optional T-junction for droplet formation. The generator can realize an ultra-long continuously-varying concentration gradient along the flow direction. Generation of a 38 mm concentration gradient was demonstrated. The length can be further extended by enlarging the capacity of the cavities and increasing the number of the stages. The concentration gradient showed high linearity in the range of 10% to 90%. Moreover, cyclic generation of a concentration gradient flow and droplets with different concentrations was realized by the generator. In a demonstration of drug screening, the generator was employed to produce paclitaxel in different concentrations. A negative correlation between the 4T1 cell viability and the paclitaxel concentration was observed after the treatment. We envision that the concentration gradient generator will be a promising candidate for various drug screening applications.
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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".