Electropolymerized Poly(3,4-ethylenedioxythiophene) (PEDOT) Coatings for Implantable Deep-Brain-Stimulating Microelectrodes
Bibliographic record
Abstract
Conducting polymers have been widely explored as coating materials for metal electrodes to improve neural signal recording and stimulation because of their mixed electronic-ionic conduction and biocompatibility. In particular, the conducting polymer poly(3,4-ethylenedioxythiophene) (PEDOT) is one of the best candidates for biomedical applications due to its high conductivity and good electrochemical stability. Coating metal electrodes with PEDOT has shown to enhance the electrode's performance by decreasing the impedance and increasing the charge storage capacity. However, PEDOT-coated metal electrodes often have issues with delamination and stability, resulting in decreased device performance and lifetime. In this work, we were able to electropolymerize PEDOT coatings on sharp platinum-iridium recording and stimulating neural electrodes and demonstrated its mechanical and electrochemical stability. Electropolymerization of PEDOT:tetrafluoroborate was carried out in three different solvents: propylene carbonate, acetonitrile, and water. The stability of the coatings was assessed via ultrasonication, phosphate buffer solution soaking test, autoclave sterilization, and electrical pulsing. Coatings prepared with propylene carbonate or acetonitrile possessed excellent electrochemical stability and survived autoclave sterilization, prolonged soaking, and electrical stimulation without major changes in electrochemical properties. Stimulating microelectrodes were implanted in rats and stimulated daily, for 7 and 15 days. The electrochemical properties monitored in vivo demonstrated that the stimulation procedure for both coated and uncoated electrodes decreased the impedance.
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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) | 0.001 | 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) | 0.000 | 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".