A Low-Temperature-Processed, Soft-Fluidic OEGFET Saliva Aptasensor for Cortisol
Why this work is in the frame
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Bibliographic record
Abstract
Label-free, organic field-effect transistor (OFET)-based biosensors have often overlooked a key challenge to commercialization, i.e., process integration. Many promising literature point-of-care (PoC) prototypes are poor integration candidates due to short shelf life, rigid form factor, and reliance on specialized data collection. Our flexible organic electrolyte-gated FET (OEGFET) sensor device architecture is designed to mitigate some of these integration challenges using a novel low temperature, low-cost fabrication process. As a result of the new process, we observed significant improvements in sensor operating parameters over our previous OEGFETs printed using conventional materials, including a 75% reduction in operating voltage (<5 V), <inline-formula> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> superior cortisol detection limit, preserved electrical characteristics (<20% reduction), and laboratory shelf life of over 15 days. We observed excellent repeatability and a predictable distinction between concentration versus output current responses for synthetic samples and complex media, such as spiked saliva supernatant. The device demonstrated a broad detection range of 0.276 pM–27.6 <inline-formula> <tex-math notation="LaTeX">$\mu \text{M}$ </tex-math></inline-formula> for cortisol samples, encompassing the salivary cortisol physiological range. Device specificity to cortisol was observed with progesterone samples, with highly repeatable results and predictable distinctions between binding and nonbinding assays. The fully flexible OEGFET is the first example of an electrolyte-gated OFET biosensor device with integrated soft microfluidic channels, validated using both synthetic and spiked saliva samples.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it