Graphene material characterisation for optofluidic applications
Bibliographic record
Abstract
Traditional photolithography methods of fabrication of micro-opto-electro-mechanical systems (MOEMS) can be substituted with graphene to minimize cost and enhance optofluidic system integration. The use of optical disc drives allows graphite oxide to undergo (near infrared) light exposure, with a specified pattern, and transformation to graphene, also with this specified pattern. This work describes the fabrication methods, electrical conduction and hydrophobicity characteristics for graphene microsystems. The fabrication characterisation involves a comparison of graphene fabrication of microsystems with photolithography fabrication. Graphene fabrication was observed to be comparable to the photolithography fabrication, with a comparable minimum feature size. The electrical characterisation involves resistivity measurements of graphene which decrease from n = 1 (where n represents the dose of light exposure in the disc drive), and saturates at n = 12, representing the final transformation to graphene from graphite oxide. The microfluidic characterisation of the graphene surface involves contact angle measurements and favourable wetting properties are shown. By increasing the fabrication dose, the contact angle rises from 50 degrees until saturation at 116 degrees, allowing for contact angle tunability over this range. Overall, fabrication of MOEMS is found to be successfully achievable using graphene fabrication.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".