Light-driven hydrogel microvalve based on BR proton pumps
Bibliographic record
Abstract
Regulating the directional flow of very small quantities of fluid along narrow microchannels has been an on-going challenge for Lab-on-a-Chip (LOC) technologies. The control and precise delivery of minute quantities of liquid is often accomplished by an externally controlled microvalve. A thin porous photoelectric film that controls the expansion and contraction of an ionic polymer actuator, or hydrogel microvalve, is described in this paper. The light driven transducer is comprised of an ultra-thin layer of oriented photosensitive bacteriorhodopsin (bR) purple membrane (PM) patches which are self-assembled on a bio-functionalized gold (Au) coated porous anodic alumina substrate. When exposed to visible light, the proton pumps formed by the bR PMs generate a unidirectional flow of ions through the very tiny pores of the Aucoated substrate altering the pH of two separated ionic solutions. The change in pH in the target solution is sufficient to induce isomotic pressure within the hydrogel microvalve and drive volume expansion. Preliminary experiments are performed using the light-driven proton pumps and an ionic hydrogel microvalve constructed from 2-hydroxyethyl methacrylate (HEMA) and acrylic acid (AA) monomers. The investigative study shows that an 8μm gap in a microchannel could be closed using a focused light beam. Further studies are, however, required to optimize important design parameters and fabrication steps to improve performance.
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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) | 0.001 | 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".