Enhancing the Ion Detection of Graphene Field Effect Transistors at the Quantum Capacitance Limit
Bibliographic record
Abstract
Graphene field effect transistors (FETs) are attractive candidates for sensing applications because of their high charge carrier mobility and the ideal coupling between graphene charge carriers and surface potential. However, depositing a selective layer on the graphene for these sensing applications can degrade graphene’s electrical properties, increase hysteresis, and present a challenge for maintaining its sensitivity and stability. Here, we protect the graphene by encapsulating it with an ultrathin layer (4-8 nm) of parylene, a hydrophobic polymer, and then deposit 3-5 nm sensing layers, either aluminum oxide or tantalum pentoxide for sensing pH. We demonstrate gate capacitances approaching the quantum limit with 0.6 uF/cm2 and near Nernstian pH sensitivities of 55.2 mV/pH. We also observe significant improvements in field effect mobilities of 7000 cm2V-1s-1 and in transconductance with limited hysteresis compared to previous work. The improvements due to encapsulation have resulted in a detection limit of 0.1 mpH at a 60 Hz electrical bandwidth. We demonstrated our pH response by monitoring the change in acidity of carbonated water in real time. Figure 1
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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 | 0.000 | 0.001 |
| 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".