Temperature Measurement in Microfluidic Systems Using Photobleaching of a Fluorescent Slab
Bibliographic record
Abstract
Temperature control is key to microfluidic-based Lab-on-a-Chip devices for a variety of applications such as polymerase chain reaction for DNA amplification and isoelectric focusing for protein separation where pH gradients are thermally generated. The most widely used temperature measurement method involves the mixing of the buffer solution with a fluorescent dye, which has a temperature-dependent fluorescent intensity. The temperature distribution in the liquid can be obtained by monitoring the fluorescent intensity distribution in the channel. However, this method can not be easily applied to polymer-made microfluidic chips because of dye absorption and penetration into polymer chips, electrophoresis of dye which causes artificial temperature gradients, and inevitable photobleaching of fluorescent dye. Therefore, a novel method is developed and presented here for temperature measurement by utilizing photobleaching of fluorescent dye. This method includes two novel contributions: i) a specially developed model for converting temperature-dependent photobleaching speed distribution to temperature distribution, and ii) an introduction of a thin polydimethylsiloxane (PDMS) layer with saturated Rhodamine B for solving the above-mentioned dye diffusion and electrophoresis problems. In this new method, a thin PDMS layer saturated with Rhodamine B is bonded with another PDMS layer with microchannels instead of mixing the dye with the buffer solution. Therefore, the problems associated with dye diffusion into PDMS chips and electrophoresis when an electrical field is applied to channels are avoided. The developed theory is validated by comparing the experimentally measured temperature distribution with numerical predicted results. The theory and its validation will be presented and discussed.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".