Soft flexible conductive CNT nanocomposites for ECG monitoring
Bibliographic record
Abstract
Abstract With the continuing development and interest in wearable electronics and smart textiles, the need for a flexible conductive electrode for use in portable and wearable electrocardiogram (ECG) for long term monitoring rises. Here, we assess the efficacy and performance of various conductive composite polymers in collecting electrical signals from the heart. Thermoplastic polyurethane (TPU), ethylene-vinyl acetate (EVA), and styrene-butadiene-styrene (SBS) were blended with 1%, 2%, 5%, and 10% carbon nanotube (CNT) content using ultrasonication and compression molding techniques. The elastic modulus of the composites increased dramatically at 5% CNT and higher due to the high tensile modulus of the CNTs. The conductivity of each material also increased due to the formation of the conductive network past the percolation threshold. At 10% CNT, SBS, TPU, and EVA had conductivities of 257.9, 51.5, and 2.41 S m −1 respectively. TPU and SBS composites had better strain response due to their linearity between resistance and strain. On the acquisition of electrical signals from the heart, only 5% and 10% SBS-CNT composites were able to detect the ECG waves from the heart. The performance of the material met and even exceeded that of the commercial electrodes with slightly less high frequency noise.
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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".