Aptamer-Enhanced Organic Electrolyte-Gated FET Biosensor for High-Specificity Detection of Cortisol
Bibliographic record
Abstract
Organic field-effect transistor (FET) devices offer a low-cost manufacturing and simple integration of biorecognition molecules for biosensory applications. In electrolyte-gated organic FET (EGOFET) biosensors, a biofluid is directly integrated as the OFET gate dielectric for label-free bioelectronic sensing. The critical constraints of these devices are biofouling and the organic polymer's susceptibility to environmental effects. In this letter, we solve these issues by implementing a multilayered gate dielectric system in our low-cost OFET biosensor, encompassing the bioelectrolyte and an aptamer-enhanced top gate surface for the rapid high-sensitivity detection of cortisol in synthetic buffer solutions. To emphasize the crucial architectural and operational differences to EGOFETs, we have termed these devices as organic electrolyte-gated FET (OEGFETs). We also reported a numerical model that can be fitted to predict the behavior of the cortisol-specific OEGFET biosensors and highlighted the distinct advantages of our system, compared with the more traditional EGOFET sensors. The specificity of our cortisol biosensor is rigorously tested using two negative control biomolecules, i.e., progesterone and cortisone. Molecules that structurally resemble cortisol and like cortisols are present in detectable concentrations in the saliva. The detectable cortisol concentration range of our OEGFET biosensor is currently identified as 27.3 pM-27.3 μM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".