Nonthrombogenic, stretchable, active multielectrode array for electroanatomical mapping
Bibliographic record
Abstract
High-precision monitoring of electrophysiological signals with high spatial and temporal resolutions is one of the most important subjects for elucidating physiology functions. Recently, ultraflexible multielectrode arrays (MEAs) have been fabricated to establish conformal contacts with the surface of organs and to measure propagation of electrophysiological signals with high spatial-temporal resolution; however, plastic substrates have high Young's modulus, causing difficulties in creating appropriate stretchability and blood compatibility for applying them on the dynamically moving and surgical bleeding surface of the heart. Here, we have successfully fabricated an active MEA that simultaneously achieves nonthrombogenicity, stretchability, and stability, which allows long-term electrocardiographic (ECG) monitoring of the dynamically moving hearts of rats even with capillary bleeding. Because of the active data readout, the measured ECG signals exhibit a high signal-to-noise ratio of 52 dB. The novel stretchable MEA is carefully designed using state-of-the-art engineering techniques by combining extraordinarily high gain organic electrochemical transistors processed on microgrid substrates and a coating of poly(3-methoxypropyl acrylate), which exhibits significant antithrombotic properties while maintaining excellent ionic conductivity.
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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".