Whole Chip Temperature Measurements Using Thin-Film PDMS/Rhodamine B for Microfluidic Chip Design
Bibliographic record
Abstract
A novel method for in-situ temperature measurements of microfluidic devices using thin-film poly(dimethylsiloxane) (PDMS) saturated with Rhodamine B dye is reported. Rhodamine B is commonly injected into the working fluid for on-chip temperature field visualization of glass and silicon based microfluidic devices since its fluorescent intensity is temperature dependent. However, such a visualization method results in unreliable temperature measurements for polymeric devices such as PDMS due to high absorption and adsorption. Thus, an inexpensive temperature measurement technique was developed in which a thin PDMS layer (∼30 μm) is fabricated and submersed for several days into a Rhodamine B solution. To prevent backward diffusion of the dye into the working fluid during operation, a glass barrier (∼150 μm) is bonded between the thin film and the PDMS mold containing the microchannel design. Temperature measurements are made by utilizing standard method of measuring changes in the normalized fluorescent intensity. For verification purposes, a new calibration curve is developed and the thin film is tested with a tapered microchannel subjected to joule heating. The resulting temperature field along the axial direction of the channel for different input power compares well with numerical simulations. Errors in temperature measurement due to the current design are 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".