Reconfigurable microfluidic chip based on a light-sensitive hydrogel
Bibliographic record
Abstract
Glass is often used as a substrate material for developing microfluidic chips because it is hydrophilic (attracts and holds moisture), chemically inert, stable over time, optically clear, non-porous, and can be fabricated at low cost. However, the size and geometry of the various components, flow channels and fluid reservoirs are all fixed on the substrate material at the time of microfabrication. Recent advances on the development of a light driven microactuator for actively changing the size and geometry of micro features, based on a photo-responsive hydrogel, are described in this paper. Each discrete microactuator in the platform structure is a bi-layered hydrogel that exploits the ionic nature of the pH sensitive polymer blend of polyethylenimine (PEI) and poly(vinyl alcohol) (PVA), and the proton pumping ability of the retinal protein bacteriorhodopsin (bR). When irradiated by a light source with a peak response of 568 nm the bR molecules in the (bR-PVA) layer undergo a complex photocycle that causes protons to be pumped into the adjoining pH sensitive (PEI-PVA) layer. The diffusion of similarly charged ions through the second actuating layer 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 fabrication of the (bR-PVA)-(PEI-PVA) hydrogel microactuator is described and the experimental results from preliminary tests are presented. The application of the light sensitive hydrogels to developing a reconfigurable microchip platform is briefly 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 | 0.000 |
| 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 | 0.001 | 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".