Noninvasive Label-Free Detection of Cortisol and Lactate Using Graphene Embedded Screen-Printed Electrode
Bibliographic record
Abstract
A sensitive and specific immunosensor for the detection of the hormones cortisol and lactate in human or animal biological fluids, such as sweat and saliva, was devised using the label-free electrochemical chronoamperometric technique. By using these fluids instead of blood, the biosensor becomes noninvasive and is less stressful to the end user, who may be a small child or a farm animal. Electroreduced graphene oxide (e-RGO) was used as a synergistic platform for signal amplification and template for bioconjugation for the sensing mechanism on a screen-printed electrode. The cortisol and lactate antibodies were bioconjugated to the e-RGO using covalent carbodiimide chemistry. Label-free electrochemical chronoamperometric detection was used to analyze the response to the desired biomolecules over the wide detection range. A detection limit of 0.1 ng mL−1 for cortisol and 0.1 mM for lactate was established and a correlation between concentration and current was observed. A portable, handheld potentiostat assembled with Bluetooth communication and battery operation enables the developed system for point-of-care applications. A sandwich-like structure containing the sensing mechanisms as a prototype was designed to secure the biosensor to skin and use capillary action to draw sweat or other fluids toward the sensing mechanism. Overall, the immunosensor shows remarkable specificity, sensitivity as well as the noninvasive and point-of-care capabilities and allows the biosensor to be used as a versatile sensing platform in both developed and developing countries.
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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) | 0.001 | 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 | 0.001 | 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".