A Portable Sweat Sensor Based on Carbon Quantum Dots for Multiplex Detection of Cardiovascular Health Biomarkers
Bibliographic record
Abstract
The future of personalized diagnostics and treatment of cardiovascular diseases lies in the use of portable sensors. Portable sensors can acquire biomarker information in biological fluids such as sweat, an approach that mitigates the shortcomings of conventional hospital-centered healthcare. Low sensitivity, selectivity, and specificity remain bottlenecks for the widespread use of portable sensors. Herein, we demonstrate a portable sensor that simultaneously detects Na +, ascorbic acid, and human neuropeptide Y in sweat, all useful biomarkers to index cardiovascular health. The portable sensor comprises a six-electrode system containing three working electrodes, two reference electrodes, and one counter electrode. The working electrodes were prepared by depositing sensing components on carbon quantum dot (CQD) electrodes. The sensing mechanisms were based on selective ion recognition, enzyme catalytic reaction, and immune response, which guarantees specificity to corresponding targets. The CQDs offer massive reactive sites and effectively reduce the interfacial impedance during the sensing reaction, thereby enhancing the three biomarkers’ detection sensitivity. As evidence of portable sensor capability, we demonstrate herein its effective simultaneous detection of the three biomarkers in a real sweat from healthy volunteers during routine activities including exercise, extra ascorbic acid ingestion, and extra Na + ingestion. As such, the sensor shows promise for real-time noninvasive personalized medical diagnostics and metabolic wellness management.
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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) | 0.000 | 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 | 0.001 | 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".