Diazonium-Based Anchoring of PEDOT on Pt/Ir Electrodes via Diazonium Chemistry
Bibliographic record
Abstract
Conducting polymers, specifically poly (3,4-ethylenedioxythiophene) (PEDOT), have recently been coated onto Pt/Ir electrodes intended for neural applications, such as deep brain stimulation (DBS). This modification reduces impedance, increases biocompatibility, and increases electrochemically active surface area. However, direct electropolymerization of PEDOT onto a metallic surface results in physically adsorbed films that suffer from poor adhesion, precluding their use in applications requiring in vivo functionality (i.e. DBS treatment). In this work, we propose a new attachment strategy, whereby PEDOT is covalently attached to an electrode surface through an intermediate phenylthiophene layer, deposited by electrochemical reduction of a diazonium salt. Our electrodes retain their electrochemical performance after more than 1000 redox cycles, whereas physically adsorbed films begin to delaminate after only 40 cycles. Additionally, covalently attached PEDOT maintained strong adhesion even after 10 minutes of ultrasonication (vs. 10 s for physically adsorbed films), confirming its suitability for long-term implantation in the brain. The simple two-step covalent attachment strategy proposed here is particularly useful for neural applications and could also be adapted to introduce other functionalities on the conducting surface.
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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) | 0.001 | 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".