Electrodeposited PEDOT:BF<sub>4</sub> Coatings Improve Impedance of Chronic Neural Stimulating Probes In Vivo
Bibliographic record
Abstract
Abstract Resulting from its many unique properties, such as mechanical compliancy, electrochemical stability, and high conductivity, the conducting polymer poly(3,4‐ethylenedioxythiophene) (PEDOT) is a promising material for improving the stimulation efficiency of neural microelectrodes. The long‐term electrochemical stability of penetrating PEDOT‐coated electrodes undergoing high‐frequency stimulation is not extensively studied in vivo and the inflammatory response of the brain to PEDOT‐coated stimulating neural probes is not well understood. In this work, electropolymerized PEDOT doped with tetrafluoroborate (PEDOT:BF 4 ) is selectively deposited on the electrode sites of platinum iridium (PtIr) neural probes and implanted for 2 weeks and 2 months to evaluate the effect of implantation on the electrical performance, and the foreign body response to the probes. Histological evaluation after 8 weeks of implantation reveals no difference in the degree of inflammation around PtIr and PEDOT probes. Additionally, PEDOT and PtIr probes are implanted for 60 days, subjected to daily high frequency stimulation and are monitored for changes in electrochemical properties. Impedance measurements reveal an overall lower impedance for PEDOT probes. These results indicate that PEDOT:BF4 coatings offer a promising approach for improving the stability of neural interfaces for stimulation.
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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".