(Invited) Recent Advances in Ion Sensitive Graphene Field Effect Transistors
Bibliographic record
Abstract
I will summarize recent advances [1-3] in large-area graphene based ion sensitive field effect transistors (ISFETs), including the achievement of record potentiometric ion concentration resolution. Sensor resolution, the smallest measurable change in analyte concentration, is a critical parameter in the assessment of sensor performance. Notably, the absence of a bandgap in graphene is frequently perceived as prohibiting competitive graphene FET performance. A graphene FET cannot be driven into saturation, limiting intrinsic gain and high-frequency performance. The graphene FET cannot be turned off, severely limiting the on/off current ratio for switching applications. In contrast, in the case of graphene ISFETs, the graphene FET functions as an analog pre-amplifier directly integrated with a sensing layer, enabling record sensor resolution to be achieved. Large-area graphene ISFETs combine high mobility charge transport, low flicker noise, facile integration with a variety of ion sensitive layers. The advent of wafer-scale graphene processing methods facilitates reproducible production of graphene ISFETs. Field effect mobilities in graphene ISFETs can reach μ = 5000 cm2 V-1 s-1, contributing to high gain gm = ∂Id /∂Vg ∝ μVDS. In particular, high mobility allows for the reduction of bias voltage VDS , which improves potentiometric uniformity across the sensor channel. We further find that low-frequency (1/f) noise, which ultimately determines sensor resolution, decreases approximately inversely with graphene FET channel area, in accord with 1/f scaling in Si MOSFETs. In other words, large transistors are quiet transistors. Scaling graphene FETs up to a 5 mm x 5 mm channel area, we observe a normalized 1/f noise parameter K = f <Δv 2>/v0 2 = 5×10-13, the lowest reported to date for a graphene FET at room temperature. Facile integration of graphene FETs with ion sensitive layers, such as metal oxides and ionophore polymer membranes, enables sensitization to a wide range of ions including H+, K+, Na+, NH4 +, Cl-, NO3 -, HPO4 2- and SO4 2-. Combining the Nernstian limited sensitivity with low 1/f noise enables record ion concentration resolution as low as r ~ 0.003 log M. Beyond sensor resolution, specificity is another important sensor parameter. Ionophores suffer cross-sensitivity to ionic species other than their target ion. The combined stability and resolution of graphene ISEFTs allows simultaneous, real-time measurement of multiple ionic analytes through the use of an array of graphene ISFETs, with each graphene ISFET element sensitized to a particular ion. Measurement of the cross-sensitivity within the array allows the application of Nikolskii-Eisenmann analysis to quantitatively account for cross-sensitivity, and achieve selective response with the graphene ISFET array that would otherwise not be possible with graphene ISFETs operated in isolation. I will conclude with a discussion of challenges and prospects for the future development of graphene FET based sensors. REFERENCES [1] Tran et al., Nanotechnology 32, 045502 (2020). [2] Fakih et al., Nature Communications 11, 3226, (2020). [3] Fakih et al., Sensors and Actuators B 291 89 (2019).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".