Poly(3,4-ethylenedioxythiophene) (PEDOT) Coatings for High-Quality Electromyography Recording
Bibliographic record
Abstract
Conducting polymer coatings on metal electrodes are an efficient solution to improve neural signal recording and stimulation, due to their mixed electronic-ionic conduction and biocompatibility. To date, only a few studies have been reported on conducting polymer coatings on metallic wire electrodes for muscle signal recording. Chronic muscle signal recording of freely moving animals can be challenging to acquire with coated electrodes, due to muscle movement around the electrode that can increase instances of coating delamination and device failure. The poor adhesion of conducting polymers to some inorganic substrates and the possible degradation of their electrochemical properties after harsh treatments, such as sterilization, or during implantation limits their use for biomedical applications. Here, we demonstrate the mechanical and electrochemical stability of the conducting polymer, poly(3,4-ethylenedioxythiophene) (PEDOT) doped with LiClO 4, deposited on stainless steel multistranded wire electrodes for invasive muscle signal recording in mice. The mechanical and electrochemical stability was achieved by tuning the electropolymerization conditions. PEDOT-coated and bare stainless steel electrodes were implanted in the neck muscle of five mice for electromyographic (EMG) activity recording over a period of 6 weeks. The PEDOT coating improved the electrochemical properties of the stainless steel electrodes, lowering the impedance, resulting in an enhanced signal-to-noise ratio during in vivo EMG recording compared to bare electrodes.
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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".