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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".