Method for Microfluidic Whole-Chip Temperature Measurement Using Thin-Film Poly(dimethylsiloxane)/Rhodamine B
Bibliographic record
Abstract
A novel method is presented for on-chip temperature measurements using a poly(dimethylsiloxane) (PDMS) thin film dissolved with Rhodamine B dye. This thin film is sandwiched between two glass substrates (one of which is 150 microm thick) and bonded to a microchannel molded in a PDMS substrate. Whole-chip (liquid and substrate) temperature measurements can be obtained via fluorescent intensity visualization. For verification purposes, the thin film was tested with a tapered microchannel subjected to Joule heating, with resulting axial temperature gradients comparing well with numerical simulations. Errors induced by the definite film thickness are discussed and accounted for during experimental and analytical analysis. Alternative validation using the traditional in-channel Rhodamine B injection method was also attempted. The thin film has several advantages over traditional methods. First, false intensity readings due to adsorption and absorption of Rhodamine B into PDMS channels are eliminated. Second, whole-chip temperature measurements are possible. Third, separation of working liquid from Rhodamine B dye prevents possible electrophoresis effects.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".