Fully-Flexible Multifunctional Polydimethylsiloxane (PDMS) Neural Probe With a U-Turn Polyester Microchannel
Bibliographic record
Abstract
OBJECTIVE: This study aims to develop a flexible, implantable neural probe with tunable stiffness and multifunctionality for electrophysiology, drug delivery, and optogenetics, while minimizing immune response. METHODS: The probe was fabricated from polydimethylsiloxane (PDMS) with a U-turn polyester-filled microchannel (30 μm × 30 μm, 14.7 mm length). Polyester provides rigidity at room temperature and softens near body temperature for tissue compatibility. Mechanical simulations optimized probe dimensions (60 μm × 300 μm × 7 mm), ensuring insertion forces above 1.5 mN. A microfluidic mixer (90 μm × 30 μm, 7 mm) was integrated for controlled drug delivery. Heat transfer and fluid simulations assessed thermal stability and laminar flow. The device also included four gold electrodes, a bio-amplifier, and a blue μ-LED. RESULTS: Experiments confirmed stable channel performance with no leaks or bubbles, effective heat management at 37 °C, and a 60° tip angle as optimal for insertion. The probe consumed ∼46.6 mW and enabled reliable electrophysiological recordings and fluorescence activation in vitro. Biocompatibility testing validated its suitability for long-term implantation. CONCLUSION: The PDMS-polyester probe achieves thermally controlled stiffness, precise drug delivery, and integrated optogenetic stimulation while maintaining stable electrophysiological recording performance. SIGNIFICANCE: This work introduces a multifunctional neural probe that addresses limitations of rigid implants by combining flexibility, drug delivery, and optogenetics. The platform has strong potential for advancing long-term neuroengineering applications, reducing tissue response, and enabling multimodal brain interfacing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".