Flexible Organic Electrolyte Gated FET Biosensor with Integrated Soft Fluidics for Cortisol Monitoring in Oral Samples
Bibliographic record
Abstract
Electrolyte Gated Field Effect Transistor (EGFET) devices are ideal biosensor platforms as they require small sample volumes, can directly transduce biomolecule quantities to electrical signals, and have a fast sensor response time, making them a desirable candidate for future integration into a Point of Care (PoC) device. However, the lack of emphasis on EGFET device integration with fluidic sample handling has led to very few architectures that are PoC viable in their present states. In response, we have created the first flexible, organic, electrolyte gated FET (OEGFET) cortisol sensor, with integrated soft fluidic microchannels. Our OEGFET demonstrates excellent cortisol sensing properties, with improved longevity, electrical characteristics, and detection range over previously published OEGFET devices. The device demonstrates robustness to repeated testing and preserved bioactivity over a period of 15 days. The nominal operating voltage is up to 5V, 4x lower than the previously reported device. The broad cortisol sensitivity range of 27.6µM - 2.76pM comprehensively covers the physiological concentration range of cortisol in saliva, with a detection limit 10x lower than our previous device. The integrated device is low temperature and low-cost fabricated onto flexible polymer substrates, tailored as the first such PoC compatible EGFET oral biosensor reported.
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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 | 0.000 |
| Open science | 0.000 | 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".