Fabrication and testing of hydrogel-based microvalves for flow control in flexible lab-on-a-chip systems
Bibliographic record
Abstract
Due to the ease of fabrication and localized response to stimulus (pH, ionic strength, or heat), many researchers have employed stimuli-responsive hydrogels such as poly(N-isopropylacrylamide) (PNIPAAm) as excellent biocompatible materials for microfluidic actuators. We have previously presented the design and fabrication of a mechanically flexible diaphragm-based actuator by employing a reservoir of thermally responsive hydrogel PNIPAAm and a conductive nanocomposite polymer (C-NCP) heater element. We now present the construction, characterization, and simulation of a hydrogel-based microvalve and its application for flow control with a new inexpensive and efficient flexible heater. In this work, we have fabricated the microvalve using traditional microfabrication and soft lithography processes. We accurately pattern and insert the hydrogel plug structure as a fluidic control component within a microfluidic channel. We demonstrate that swelling and shrinking of the hydrogel plug in the microchannel results in closing and opening of the valve. New simulations of the hydrogel plug design were employed using COMSOL® Multiphysics to show the pressure distribution and hydrogel plug movement as well as fluidic velocity in the simulated channel. We then compare the theoretical computed value with the prediction of the COMSOL simulation result which verifies the functionality of our hydrogel plug microvalve design.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".