Electrochemical layer-by-layer approach to fabricate mechanically stable platinum black microelectrodes using a mussel-inspired polydopamine adhesive
Bibliographic record
Abstract
OBJECTIVE: Platinum black (PtBK) has long been used for microelectrode fabrication owing to its high recording performance of neural signals. The porous structure of PtBK enlarges the surface area and lowers the impedance, which results in background noise reduction. However, the brittleness of PtBK has been a problem in practice. In this work, we report mechanically stable PtBK microelectrodes using a bioinspired adhesive film, polydopamine (pDA), while maintaining the low impedance of PtBK. APPROACH: The pDA layer was incorporated into the PtBK structure through electrochemical layer-by-layer deposition. Varying the number of layers and the order of materials, multi-layered pDA-PtBK hybrids were fabricated and the electrical properties, both impedance and charge injection limit, were evaluated. MAIN RESULTS: Multilayered pDA-PtBK hybrids had electrical impedances as low as PtBK controls and charge injection limit twice larger than controls. For the 30 min-ultrasonication agitation test, impedance levels rarely changed for some of the pDA-PtBK hybrids indicating that the pDA improved the mechanical property of the PtBK structures. The pDA-PtBK hybrid microelectrodes readily recorded neural signals of cultured hippocampal neurons, where background noise levels and signal-to-noise were 2.43 ∼ 3.23 μVrms and 28.4 ∼ 69.1, respectively. SIGNIFICANCE: The developed pDA-PtBK hybrid microelectrodes are expected to be applicable to neural sensors for neural prosthetic studies.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".