Graphene doped ZnO films for photoelectrowetting on microchannels
Bibliographic record
Abstract
Photoelectrowetting on dielectric surfaces can be used to drive droplets of liquid along reconfigurable paths on a microfluidic chip using controlled optical signals. These electrostatically activated surfaces along the desired path eliminate the need for precision molded channels and discrete functional components such as microvalves and micropumps. The photoelectrowetting effect exploits the surface tension of the droplet to maintain its volume during the transportation pathway and the photoelectric properties of the substrate surface are used to induce reversible fluidic flow. The active light-driven substrate is structured from graphene doped zinc-oxide (ZnO-G) films deposited on ITO coated glass. This substrate is coated from the ZnO-G side with Ruthenium-based dye (N719) to maximize its absorbability. The light triggers two forces that enable the droplet to be transported along the substrate. The first arises from the induced hydrophobicity gradient formed across the droplet contact area with the substrate surface. Exposing the ZnO-G film to a broad spectrum white light source alters the surface’s electric potential which induces a change in the droplet’s contact angle and the associated hydrophobicity. Once the hydrophobicity gradient is generated the droplet will start to move in the direction of the wetting zone. The second force is also created by the optical input when the absorbed light generates a photoelectric potential that produces a piezo-electrical effect on the ZnO-G film. The light triggered piezo-electrical behavior of the ZnO-G film can be used to generate the erasable microchannels that can guide droplet movement through a microfluidic chip. Preliminary experiments are performed to investigate the photoelectric potential of light activated ZnO-G films.
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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) | 0.001 | 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".