Graphene Oxide Films Prepared Using Gelatin Nanofibers as Wearable Sensors for Monitoring Cardiovascular Health
Bibliographic record
Abstract
Abstract A gelatin nanofiber film that shows a failure tensile stress of 35 MPa, much stronger than conventional polyacrylamide hydrogels (<0.2 MPa), tough double network hydrogels (0.2–1.0 MPa), some engineering plastics such as polyethylene films (16 MPa) and polysulfones (1–10 MPa), is prepared by electrospinning. It is processed into a graphene oxide film, which exhibits high conductivity, via a high‐temperature treatment. A simple approach to prepare graphene oxide films using gelatin is provided. A highly sensitive and responsive wearable sensor is fabricated using the graphene oxide film, which is capable of sound recognition, apexcardiogram recording, and pulse spectrum measurement. The apexcardiogram is strongly associated with hemodynamic cardiac health, which reflects the cardiac process of ventricular contraction, blood ejection, diastole, semilunar valve open/close, atrioventricular valve valve open/close, etc. The developed cardiac sensor could be used to measure arterial stiffness index, a derivative of pulse spectrum, useful to evaluate artery walls stiffness and health status. Using the developed sensor, the detection result can be wirelessly relayed to mobile devices for personal cardiac health monitoring.
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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 | 0.000 | 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".