Photo-responsive hydrogel for controlling flow on a microfluidic chip
Bibliographic record
Abstract
One technological challenge in microfluidic system design has been controlling the directional flow of minute amounts of fluid through various narrow channels. Stimuli-responsive polymers can be used as micro control devices such as valves because these materials significantly change their volumetric properties in response to small environmental changes in pH, temperature, solvent composition, or electric field. In this paper, a bi-layered hydrogel structure is introduced as a light activated microactuator. The first layer of the device is a light sensitive polymer network containing poly(vinyl alcohol) (PVA) and the retinal protein bacteriorhodopsin (bR). The second layer is a blend of PVA hydrogel and a pH sensitive polymer polyethylenimine (PEI). When exposed to a light source with a peak response at 568 nm, the bR molecules in the first layer undergo a multistage photocycle that cause protons to be pumped into the surrounding medium. The diffusion of these similarly charged ions through the adjoining pH responsive hydrogel generates electrostatic repulsive and attractive forces which alter the osmotic pressure within the cross-linked polymer network. Depending upon the type of electrostatic forces generated, the pH sensitive hydrogel layer will swell or, alternatively, collapse. The multi-layered structure can be fabricated and inserted into the microchannel. The expanding volume of the actuating hydrogel is used to regulate flow or control leakage. Preliminary experiments on a 625mm3 optical actuating device are presented to identify key response characteristics and illustrate the mechanism for actuation
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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) | 0.000 | 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) | 0.002 | 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".