Dynamics of temperature-actuated droplets within microfluidics
Bibliographic record
Abstract
Characterizing the thermal behavior of dispersed droplets within microfluidic channels is crucial for different applications in lab-on-a-chip. In this paper, the physics of droplets volume during their transport over a heater is studied experimentally and numerically. The response of droplets to external heating is examined at temperature ranges of 25-90 °C and at different flow rates of the dispersed phase respect to the continuous flow. The results present a reliable prediction of the droplet volume and stability when heating is applied to the droplets at the downstream channel in a quite far distance from the droplets' ejection orifice. Increasing the ratio of flow rate resulted in larger droplets; for instance, the flow ratio of 0.25 produced drops with 40% larger diameter than the flow rate of 0.1. For every 10 °C increase in temperature of the droplets, the droplet diameter increased by about 5.7% and 4.2% for pure oil and oil with a surfactant, respectively. Also, the droplets showed a degree of instability during their transport over the heater at higher temperatures. Adding SPAN 20 surfactant improved the stability of the droplets at temperatures higher than 60 °C. The experimentally validated numerical model helped for systemic analysis of the influence of key temperature-dependence parameters (e.g. surface tension, density and viscosity of both phases) on controlling the volume and stability of droplets. Our findings supported to develop highly functional systems with a predetermined droplets performance under high temperatures up to 90 °C. This report provides a preliminary basis for enhancing the performance of droplet microfluidic systems for digital droplet polymerase chain reaction (ddPCR), continuous flow digital loop-mediated isothermal PCR (LAMP), and droplet-based antibiotic susceptibility testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".