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 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".