Picoliter agar droplet breakup in microfluidics meets microbiology application: numerical and experimental approaches
Bibliographic record
Abstract
Droplet microfluidics has provided lab-on-a-chip platforms with the capability of bacteria encapsulation in biomaterials, controlled culture environments, and live monitoring of growth and proliferation. The droplets are mainly generated from biomaterials with temperature dependent gelation behavior which necessitates stable and size-controlled droplet formation within microfluidics. Here, the biomaterial is agar hydrogel with a non-Newtonian response at operating temperatures below 40 °C, the upper-temperature threshold for cells and pathogens. The size of the produced droplets and the formation regimes are examined when the agar is injected at a constant temperature of 37 °C with agar concentrations of 0.5%, 1%, and 2% and different flow rate ratios of the dispersed phase to the continuous phase (φ: 0.1 to 1). The numerical simulations show that φ and the capillary number (Ca) are the key parameters controlling the agar droplet size and formation regime, from dripping to jetting. Also, increasing the agar concentration produces smaller droplets. The simulation data were validated against experimental agar droplet generation and transport in microfluidics. This work helps to understand the physics of droplet generation in droplet microfluidic systems operating with non-Newtonian fluids. Pathogenic bacteria were successfully cultured and monitored in high resolution in agar droplets for further research in antibiotic susceptibility testing in bacteremia and urinary tract infection.
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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".