Modelling and Optimization of the Differential Surface Capacitance in Electrolyte-Gated Graphene Field-Effect Transistors
Bibliographic record
Abstract
Graphene field-effect transistors (GFETs) are a promising avenue for the detection of biomarkers, allowing to envision improvements in diagnostic approaches for different types of cancer and other diseases. To enable such applications, the detection metrics of the devices must be optimized, including their sensitivity, selectivity, and stability. In particular, the capacitance of the device is one of the key determinants of the sensitivity of the electrical current of graphene to nearby changes in electrical potentials. In this presentation, we present our approach to optimize the surface capacitance of electrolyte-gated GFETs, by investigating theoretically and/or experimentally the effect of specific design parameters, including the graphene area, the removal of the backgate, and the passivation of electrodes. On the theoretical side, a mathematical representation of the device capacitance was established as an equivalent circuit of capacitors modelling for the quantum capacitance of graphene, the backgate capacitance, and the capacitance of electrical double layers in the electrolyte. On the experimental side, microchips of GFET arrays were assembled using microfabrication techniques on two types of substrates: a standard Si++/SiO2 wafer in which the doped silicon layer is used as a backgate, and a pure non-conducting borosilicate glass wafer in comparison. The capacitance of the devices was then measured using a custom parallel electrical prober by stepping the applied voltage in the solution and analyzing the resulting current response through a standard RC direct current model. We will present our analyses by comparing the relative impact of electrode passivation, backgate usage, and graphene area on the capacitance of devices. These results will enable a rational design of GFET devices with improved sensitivity for their use in applications such as biological, chemical, and gas sensing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".