Graphene field effect transistor scaling for ultra-low-noise sensors
Bibliographic record
Abstract
Abstract The discovery of the field effect in graphene initiated the development of graphene field effect transistor (FET) sensors, wherein high mobility surface conduction is readily modulated by surface adsorption. For all graphene transistor sensors, low-frequency 1/ f noise determines sensor resolution, and the absolute measure of 1/ f noise is thus a crucial performance metric for sensor applications. Here we report a simple method for reducing 1/ f noise by scaling the active area of graphene FET sensors. We measured 1/ f noise in graphene FETs with size 5 μ m × 5 μ m to 5.12 mm × 5.12 mm, observing more than five orders of magnitude reduction in 1/ f noise. We report the lowest normalized graphene 1/ f noise parameter observed to date, 5 × 10 −13 , and we demonstrate a sulfate ion sensor with a record resolution of 1.2 × 10 −3 log molar concentration units. Our work highlights the importance of area scaling in graphene FET sensor design, wherein increased channel area improves sensor resolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".