(Invited) Large Area Ion Sensitive Graphene Field Effect Transistors for Potassium, Sodium and Chlorine Sensing
Bibliographic record
Abstract
We review our work on large-area graphene based ion sensitive field effect transistors (ISFETs), employing metal oxide layers or ionophore based membranes. Unlike traditional silicon based ISFETs, our work is focused on macroscopic graphene devices fabricated using chemical vapour deposition methods. Large-area graphene ISFETs benefit from the combination of high mobility charge transport, reduced low-frequency noise with increasing transistor channel area, and facile integration with ion sensitive layers. Graphene ISFETs with an active area of approximately 1cm x 1cm are characterized by an rms current noise as low as 5 ~ nA in a 60 Hz bandwidth, field effect mobilities up to 5000 cm 2 V -1 s -1 and quantum capacitance limited coupling between graphene channel and the solid/electrolyte interface. We have demonstrated graphene ISFETs selectively sensitive to H + , K + , Na + and Cl - . In the case of H + sensing, metal oxides such as Ta 2 O 5 and Al 2 O 3 deposited by atomic layer deposition can be used to achieve Nernstian limited sensitivity with a 0.1 mpH resolution. Ion sensitive membranes based on ionophores can be used for selective sensing of other ionic species. For example, in the specific case of the K + , real-time sensing was achieved using potassium ionophore III with a detection limit of 10 -9 M [K + ], equivalent to 39~ng/L, and a resolution of 1.5×10 -3 log[K + ]. Spiking experiments reveal good reversibility and stability. The cross-sensitivity has been measured to be: 2.5 mV/decade for Na + , 4.2 mV/decade for Ca 2+ , 1.5 mV/decade for Mg 2+ and 9.0 mV/decade for NH 4 + . Graphene ISFETs are sufficiently robust to measure the K + content of common beverages and blood samples. We will conclude our talk with a discussion of open questions concerning graphene ISFET performance and their potential applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".