SIZE-CONTROLLED DROPLET GENERATION IN A MICROFLUIDIC DEVICE FOR RARE DNA AMPLIFICATION BY OPTIMIZING ITS EFFECTIVE PARAMETERS
Bibliographic record
Abstract
Versatility and portability of microfluidic devices play a dominant role in their widespread use by researchers. Droplet-based microfluidic devices have been extensively used due to their precise control over sample volume, and ease of manipulating and addressing each droplet on demand. Droplet-based polymerase chain reaction (PCR) devices are particularly desirable in single DNA amplification. If the droplets are small enough to contain only one DNA molecule, single molecule amplification becomes possible, which can be advantageous in several cases such as early cancer detection. In this work, flow-focusing microfluidic droplet generation’s parameters are numerically investigated and optimized for generating the smallest droplet possible, while considering fabrication limits. Taguchi design of experiment method is used to study the effects of key parameters in droplet generation. By exploiting this approach, a droplet with a radius of 111[Formula: see text]nm is generated using a 3[Formula: see text][Formula: see text]m orifice. Since the governing physics of the droplet generation process is not totally understood yet, by means of analysis of variance (ANOVA) analysis, a generalized linear model (GLM) is proposed to predict the droplet radius, given the values of eight major parameters affecting the droplet size. The proposed model shows a correlation of 95.3% and 64.95% for droplets of radius greater than and lower than 5[Formula: see text][Formula: see text]m, respectively. Finally, the source of this variation of behavior in different size scales is identified.
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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 | 0.000 |
| 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 | 0.001 | 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".