Characterisation of graphene electrodes for microsystems and microfluidic devices
Bibliographic record
Abstract
Fabrication of microsystems is traditionally achieved with photolithography. However, this fabrication technique can be expensive and non-ideal for integration with microfluidic systems. As such, graphene fabrication is explored as an alternative. This graphene fabrication can be achieved with graphite oxide undergoing optical exposure, using optical disc drives, to impose specified patterns and convert to graphene. This work characterises such a graphene fabrication, and provides fabrication, electrical, microfluidic, and scanning electron microscopy (SEM) characterisations. In the fabrication characterisation, a comparison is performed between traditional photolithography fabrication and the new graphene fabrication. (Graphene fabrication details are also provided.) Here, the minimum achievable feature size is identified and graphene fabrication is found to compare favourably with traditional photolithography fabrication. In the electrical characterisation, the resistivity of graphene is measured as a function of fabrication dose in the optical disc drive and saturation effects are noted. In the microfluidic characterisation, the wetting properties of graphene are shown through an investigation of the contact angle of a microdroplet positioned on a surface that is treated with varying fabrication dose. In the SEM characterisation, the observed effects in the previous characterisations are attributed to chemical or physical effects through measurement of SEM energy dispersive X-ray spectra and SEM images, respectively. Overall, graphene fabrication is revealed to be a viable option for development of microsystems and microfluidics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".