Development of an electrochemical surface-enhanced Raman spectroscopy (EC-SERS) fabric-based plasmonic sensor for point-of-care diagnostics
Bibliographic record
Abstract
Early disease diagnosis is crucial for timely and effective healthcare monitoring and treatment. Demand for modern point-of-care (POC) technologies has increased during the past decade. Continuous monitoring of patient health status can be achieved through wearable sensors which can be incorporated into clothing and other wearables. While electronic textiles that monitor physical parameters (heart rate, blood pressure, etc.) are increasingly commonplace, smart textiles capable of monitoring chemical biomarkers are much less common. In this work, a conductive plasmonic electrochemical sensor was developed from a cotton blend fabric modified with silver nanoparticles and conductive inks. para-Aminothiophenol (pATP) was used as an initial probe molecule to evaluate the performance of the fabric-based electrode for electrochemical surface-enhanced Raman spectroscopic (EC-SERS) measurements. Further investigation was then carried out to detect levofloxacin, a commonly prescribed antibiotic, in both 0.1 M NaF and synthetic urine as supporting electrolyte. It was found that the fabric-based electrode provided excellent EC-SERS signals, comparable to commercial screen-printed electrodes, allowing for rapid detection of levofloxacin at clinically relevant concentrations. To the best of our knowledge, this is the first time a fabric-based electrode has been reported for EC-SERS investigations, highlighting a promising platform for wearable point-of-care sensors.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".