Effect of Device Design Parameters on the Sensitivity of Graphene Field-Effect Transistors
Bibliographic record
Abstract
Graphene field-effect transistors (GFETs) present several advantages for biomolecular sensing applications. Their high conductivity, carbon chemistry and sensitivity to the electrostatic environment have made GFETs a promising technology for the detection of biomarkers with high sensitivity. While research has mostly focused on biomolecular immobilization strategies to improve detection in GFET biosensors, much less attention has been given to the effect of different design parameters of the devices themselves on the sensitivity of the sensors. Here, we assess the sensitivity metrics of GFET sensors using their response to changes in buffer salinity. To do this, we incubated GFET devices in phosphate buffer solutions (PBS) with concentrations ranging from 0.001X to 10X, and collected electrical transfer curves before/after each incubation to extract the voltage of the charge neutrality point (VCNP). Two metrics were extracted from the calibration curve of VCNP against concentration: (1) the dynamic range, as the range of concentrations showing a variation of the VCNP, and (2) the sensitivity, as the slope of the calibration curve in the dynamic range, measured in mV/decade. Using small-area non-passivated GFET devices, we obtained a sensitivity of -50 mV/decade from 0.1X to 10X PBS using an Ag/AgCl reference electrode and -160 mV/decade from 0.1X to 10X PBS using a coplanar Au electrode. We will discuss the response to changes in salinity using different combinations of design parameters, including the size of the graphene area, the choice of reference electrode and the type of passivation. Overall, this study will shed light on device design optimization for increased sensitivity, giving us an indication as to which parameter combination is best for the development of highly sensitive biosensors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".