A Novel 3-D-Printed Passive Microfluidic Temperature Sensor for Medical Applications
Bibliographic record
Abstract
The electrical response of a novel passive 3-D-printed temperature sensor could significantly broaden its scope of applications and enhance the integration of microelectro-mechanical system (MEMS) microfluidic-based laboratory-on-a-chip (LOC) technologies. This article introduces an innovative temperature sensor based on the microfluidic technology which is well-suited for medical applications. The sensor’s design and optimization were conducted using multiphysics modeling and finite element method (FEM) simulations, implemented through FreeFEM++ software. Samples were produced using stereolithographic 3-D printing. A metal carrier was constructed to secure the chips during tank heating and the flow visualization with a microscope. X-ray microtomography tests were performed on chips to compare real parts with CAD models. Filling tests were conducted to position the liquid within the microfluidic channel. Furthermore, several types of liquids were tested, and contact angle (CA) measurements were employed to characterize the microfluidic chip’s structural material (DS3000) and various liquids, aiding in discerning the dielectric liquid were applied also. Among the liquids tested, water emerged as the most promising for this type of temperature sensor. Volume expansion calculations for different temperature values were performed, revealing a measured linear thermal expansion exceeding <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$40~\mu $ </tex-math></inline-formula>m/°C within the range of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20~^{\circ }$ </tex-math></inline-formula>C–<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$55~^{\circ }$ </tex-math></inline-formula>C. This study paves the way for microfluidic devices capable of measuring low flow rates using a temperature effect, thereby providing access to 16 an electrical response.
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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".