Graphene Sensor for Future Local Economic Development: A Review
Bibliographic record
Abstract
Abstract Graphene, a family of carbon has been known as a superior material of both conducting and transparent. Therefore, graphene is very promising material for many applications on microelectronics and nanotechnology. The structural, thermal, optical and electrical properties of the graphene were also potential to be applied on sensor. Graphene is the most recognized nanoparticle for fabrication of biomedical sensors due to its stimulating qualities such as excellence aqueous process ability, functional surface properties, surface-enhanced Raman scattering, cell growth ability, and good biocompatibility. Due to the high specific surface area of graphene, it was very excellent material for gas sensor application. The outstanding properties of graphene were also led to increasing the demand of graphene every year which is dominated by China (70%), India (14%), and Canada (2%). Moreover, current marketplace of graphene was also dominated for academic research, super capacitor, ITO, and sensor. Every year, the market of graphene sensor is continuously increasing. This trend reveals graphene-based sensors very promising commodity for future technology. The present study highlights the state of art review and potential future local economic development of graphene for use as sensors.
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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) | 0.003 | 0.001 |
| 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".