Noninvasive Label-Free Detection of Cortisol and Lactate Using Graphene Embedded Screen-Printed Electrode
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it